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The implementation of artificial intelligence (AI) and machine learning (ML) has the potential to turn the healthcare industry upside down. And this global transformation is already beginning. Innovative technologies are being used in diagnostics and treatment. They are influencing pharmaceuticals, research work, and other aspects of medical practice.

Analysts at Global Market Insights have calculated that the AI market in healthcare is valued at $18.7 billion. However, by 2032, the situation will change. According to forecasts, its size will exceed $317 billion.

Considering this trend, we find it appropriate to explore the prospects that AI and the technologies based on it offer to medical professionals worldwide. We will also focus on the near future of AI-based medical solutions and discuss examples of digital products that are already available to specialists.

What Are Machine Learning Healthcare Projects?

Machine learning projects in the medical field involve the use of ML algorithms and models to perform various tasks for healthcare workers. These include:

• data collection and analysis;
• predicting the condition of patients and treatment outcomes;
• making decisions about the appropriateness of certain prescriptions, and more.

The effectiveness of using AI and ML in healthcare is confirmed by numerous specialized studies. Below are some examples:

• The Houston Method Research Institute used an AI-based tool for diagnostic purposes. Specifically, it was used to detect malignant breast tumors. The results shocked scientists. The system demonstrated a disease detection accuracy approaching 99%. Moreover, AI was able to analyze mammograms 30 times faster than humans.

• The Nationwide Korean Cohort Study focused on researching the effectiveness of predicting infectious disease outcomes using machine learning. The results showed that the accuracy of the applied ML model was over 90%.

• Scientists from Indiana University and Purdue University in Indianapolis developed an ML-powered system to predict the outcome of acute myeloid leukemia. The results of the model's tests demonstrated that it could predict remission with 100% accuracy. The likelihood of recurrence was correctly identified in 90% of cases.

• Researchers at BMJ Health & Care Informatics developed and tested a machine learning model for detecting NASH (non-alcoholic steatohepatitis). This disease is difficult to diagnose. However, the use of ML contributes to more accurate screening of NASH and allows for the timely application of effective treatments.

All of these statistics confirm the vast potential of machine learning in healthcare projects. So, what is this technology most often used for?

Popular Use Cases of AI Projects in Healthcare

According to Morgan Stanley Research, 94% of respondents (medical companies) reported using AI or ML in their practice.

Let’s take a look at which aspects of healthcare these technologies can optimize.

1. Pathology Detection

Machine learning models can be used to analyze various types of images. The goal is to recognize pathological conditions.

For example, they can be used to diagnose cancer, tuberculosis, and other infections.

How does this work in practice? Here are several examples of using ML in the field of pathology detection:

• A machine learning-based program analyzes tissue samples and helps identify malignant growths.
• ML algorithms recognize tuberculosis by examining sputum samples from an infected person.
• ML-based systems help differentiate between benign and malignant tumors.

An example of such software is AIM-PD-L1: an AI and ML-based tool that detects PD-L1. This protein is formed as a result of cancer cell development. Research on this solution showed that its results closely align with human diagnostics. Thus, the tool can be useful in identifying pathology.

Another solution from the same developer, PathAI, is the PathExplore panel for histopathological features. It can analyze tissue images at the pixel level. This allows it to examine millions of cells and accurately identify malignant ones.

2. Medical Imaging and Diagnostics

Healthcare machine-learning projects often use innovative technologies to analyze various images. These include CT scans, MRIs, mammograms, X-rays, and more.

Here’s how machine learning models can be used in diagnostics based on visual data:

• An ML-based system can analyze CT scan results and, with a high degree of accuracy, identify malignant tumors.
• Similarly, such platforms can diagnose ischemic strokes, such as hemorrhage, based on brain MRIs.
• Specialized ML-based tools allow for retina scans. This helps in the early detection of blindness due to diabetes, specifically diabetic retinopathy.

There are many similar solutions available on the market. Among them are Viz.ai and InnerEye.

The first, Viz.ai, is a care coordination system based on AI. It allows for automate detection of many diseases, including vascular pathologies.

The second, InnerEye,  is a solution from Microsoft that significantly speeds up work with patient images. For example, with this platform, doctors can accurately differentiate between healthy and cancerous cells. It also accelerates the start of treatment by 13 times.

3. Personalized Medicine

Each patient is unique due to the following characteristics:

• genetic predisposition;
• medical history;
• lifestyle;
• comorbid health issues, and more.

Machine learning-based platforms are able to take all these factors into account when selecting treatment methods.

Here’s how this can work in practice:

• ML models analyze genomic data and recommend individualized treatment methods for oncological diseases.
• Thanks to predictive analytics, AI-based tools can adjust insulin dosage. This is possible through constant monitoring of its level in the patient.
• Machine learning algorithms help choose the optimal antidepressant based on the patient's psychological state and genetic predispositions.

As an example, let's look at IBM Watson for Oncology. This is a machine learning-based system that considers many factors when recommending cancer treatments. These include the compatibility of prescribed medications, possible side effects, and the effectiveness of treatment in each specific case. As a result, the system provides healthcare professionals with several therapy options from which they can choose the most optimal.

4. Drug Discovery Process

The use of ML can accelerate the invention of new pharmaceuticals. Specialized systems show excellent results in discovering the medicinal properties of various substances. They can also analyze the interactions of drug components and optimize clinical trials.

AI in healthcare projects can be used for this purpose as follows:

• ML models accelerate the search for antibiotics and antiviral drugs.
• AI-based modeling helps reduce the number of failed clinical trials, thus saving time and resources.
• Machine learning algorithms can analyze the structure of substances, assisting in the development of new drugs.

A notable example of using AI and ML in creating new drugs is AlphaFold by DeepMind. This tool is designed to determine the structure of proteins, which are the foundation of biological processes in every living organism. Currently, the platform is successfully used for:

• fighting malaria;
• searching for a treatment for Parkinson's disease;
• counteracting bacteria resistant to existing drugs.

5. Epidemic Prediction

One of the capabilities of ML models is analyzing patterns and making predictions about outbreaks of various diseases.

Here are some examples of using ML-powered solutions:

• Such systems can predict global pandemics. To do this, they analyze data on population movement and disease statistics.
• ML algorithms track seasonal disease spikes, such as the flu.
• “Smart” platforms can predict outbreaks of specific diseases in high-risk areas. For example, they can forecast cases of malaria or dengue fever.

EPIWATCH is one of the leading examples of such platforms. This tool uses artificial intelligence to predict epidemics that could reach a global scale. The system collects and processes extensive infectious disease data in real time. It has proven effective in providing early warnings about potential pandemics.

6. Remote Patient Monitoring

In recent years, IoT (Internet of Things) devices have become widely used in medicine. These are objects equipped with sensors that can collect and transmit data wirelessly without human intervention.

According to Yahoo!Finance, the IoT market in healthcare, was valued at $58.8 billion in 2024, and by 2032, it is expected to exceed $305.5 billion.

This situation is explained by the fact that AI and ML enhance the efficiency of wearable devices and health monitoring systems in general.

The use of ML in remote monitoring includes the following cases:

• Smartwatches can track irregular heart rhythms and detect instances of sleep apnea.
• ML models can analyze ECG data in real time. Based on the obtained data, they can predict a heart attack.
• AI-based inhalers can track the patient’s readings and warn about an asthma attack.

There are many such solutions on the market. These include popular devices like the Apple Watch and Fitbit. These smart bracelets can monitor heart rhythms and detect uncontrolled breathing stops.

There are also more complex systems available to healthcare professionals. For example, SafeBeing from Somatix. Thanks to wearable devices, this platform responds to patient gestures and draws conclusions about their health condition.

7. Virtual Assistants

Not everyone can visit a medical facility in a timely manner. Moreover, many people need assistance and support after being discharged from the hospital. AI handles these challenges effectively. Developers are actively creating digital assistants with integrated ML algorithms. These assistants are capable of providing first-aid and consulting patients after treatment.

What can such tools do?

• These platforms help assess health status independently and suggest possible diseases.
• Smart chatbots help schedule doctor visits, remind patients to take medications, and more.
• Virtual assistants help monitor the progress of chronic diseases.

There are many such solutions available to patients. Let’s look at the features of Ada Health as an example. This platform helps assess symptoms and provide preliminary diagnoses using machine learning algorithms. It can:

• track changes in the body over time;
• provide recommendations regarding treatment and a healthy lifestyle.

8. Psychiatric Assistance

ML algorithms help diagnose and treat various psychiatric disorders, including those used in the fight against depression and anxiety.

Practical examples of ML usage in psychiatry:

• Intelligent chatbots can mimic real-world conversations. They can answer patients' questions and provide recommendations.
• ML models can analyze user behavior on social media and detect suicidal tendencies.
• These digital solutions can analyze human speech and identify early signs of Alzheimer’s disease.

Woebot is a smart chatbot that helps people with mental health disorders. It can assess a user’s mood and emotions. Among its functions are conducting conversations with patients and providing personalized strategies for coping with difficulties.

9. Robotic Surgery

Just imagine. Surgeons no longer need to be in the operating room. All procedures are performed by a robot. Sounds like a scene from a sci-fi movie? Nevertheless, this is our reality. Developers have already created robotic systems based on AI that enhance the accuracy of surgical interventions and reduce rehabilitation time.

What can such software do?

• Smart surgical systems can perform minimally invasive surgeries.
• AI-based software shows excellent results in neurosurgery and orthopedics.
• ML models predict potential surgical risks and possible complications.

Here, we cannot fail to mention the famous Da Vinci surgical robot created by Intuitive Surgical. This robotic system offers medical professionals incredible possibilities in the field of minimally invasive surgery. Its features include fluorescence visualization, integrated table movement, a vision cart, and more. This allows for precise and effective surgical interventions.

10. Automation of Administrative Tasks

Routine tasks such as organizing document flow or billing require a lot of time, diverting medical staff from their primary duties — treating patients. The implementation of machine learning-based software allows automation of these processes, reducing the time spent by medical personnel.

AI-powered systems are handling numerous administrative tasks:

• Platforms based on NLP can extract data from medical records and update patient electronic charts.
• ML algorithms analyze insurance cases, minimizing fraudulent claims.
• Smart scheduling systems efficiently manage hospital resources.

An example of such software is the Optum system. It offers numerous solutions for the healthcare business, including a platform for Revenue Cycle Management. Its capabilities include billing, increasing revenue efficiency, medical coding, and documentation.

Benefits of Machine Learning for Healthcare Analytics Projects

The use of ML models in healthcare allows for the analysis of large amounts of medical data. Furthermore, such models are continuously trained and improved. The implementation of tools based on these models brings a number of benefits to both medical professionals and patients:

Early Disease Diagnosis

Identifying serious diseases at early stages reduces mortality, improves the quality of life for patients, and increases the likelihood of full recovery or sustained remission. For medical institutions, this helps reduce their expenses and saves time.

ML models excel at early diagnosis. They analyze medical images, test results, and patient medical histories. They then track patterns and detect even the slightest deviations from the norm.

Improvement in Patient Care Quality

This is facilitated by AI-based digital solutions for personalized care, virtual assistants, and chatbots. They help maintain a person's health outside of the hospital, for instance, after discharge or before visiting a specialist.

At the same time, such platforms help reduce the workload on medical institutions. They can provide the patient with an initial consultation and allow medical professionals to remotely monitor the patient's condition.

Development of the Pharmaceutical Industry

Innovative technologies allow for faster development of new medications. This is a step toward effectively combating various diseases. This is particularly relevant during global epidemics. 

Cost Reduction

ML automates many processes in healthcare, from documentation management in clinics to diagnostics and treatment prescriptions. Additionally, the use of smart technologies helps reduce human errors and allows for some patients to switch to home care. As a result, medical institutions can increase the number of patients served and their profits.

Improvement in Treatment Effectiveness

In medicine, predictive models are used, which are trained on large sets of historical patient data. Such models can predict:

• the likelihood of recurrence;
• the need for hospitalization;
• the tendency for the disease to progress.

Medical professionals can use all this data to adjust the prescribed treatment to improve its effectiveness.

Key Challenges in Healthcare Machine Learning Projects

The implementation of machine learning in healthcare projects opens up many opportunities for the industry. We have already seen this today. However, alongside numerous advantages, some challenges may arise for innovators.

Security of Confidential Data

The digitalization of healthcare has led to a particularly sharp issue. We mean the preservation of medical confidentiality and the privacy of patient data. The use of virtual tools implies entering personal information into the system, which increases the risk of data leakage.

To mitigate these risks, maximum anonymity should be maintained. For example, financial and insurance data, as well as the patient's address, should not be included. It is also important to implement reliable data encryption methods and threat notification mechanisms on the platforms.

Quality of Data for Model Training

The effectiveness of ML models depends on the data input into the system. Inaccurate input data can lead to incorrect diagnoses, wrong treatments, and even death.

To prevent this problem, machine learning software should be used under the supervision of highly qualified medical staff.

Skepticism of Healthcare Professionals and Patients

Since machine learning projects in the medical field are still in the early stages of gaining ground, many doctors and patients are not fully confident in its capabilities. The main concerns include inaccurate diagnostics and the complete replacement of human involvement in medical practice.

To build trust, continuous training for healthcare professionals and patients is necessary. It is also important to notify people about the chosen treatment methods.

As for the complete replacement of human labor, this is highly questionable. Scientists believe that innovative technology will, in fact, expand the role of healthcare professionals. Their duties will include overseeing the operation of ML models and interpreting their results.

Difficulty of Integration with Existing Software

To ensure the smooth operation of medical institutions, new software products need to be seamlessly integrated into the existing infrastructure. The task becomes more complex because clinics may be using outdated systems that are not compatible with new technologies.

To solve this problem, investment in qualified IT personnel and gradual replacement of old software with new ones is necessary.

Technologies Used in Machine Learning Projects in Healthcare

Such high efficiency in AI projects in healthcare is achieved thanks to the wide range of technologies used in specialized software. Let's look at the main ones.

Computer Vision

This is the ability of a machine to understand and interpret visual information from the external world. In healthcare, it is used for analyzing medical images for subsequent diagnosis and treatment.

Examples of use:

• detection of tumors in X-rays, CT scans, or MRIs;
• analysis of biopsy results to identify malignant formations.

Robotic Process Automation (RPA)

This involves using bots (software robots) to perform repetitive tasks. In medicine, this technology is applied to automate administrative processes.

Examples of use:

• processing insurance claims;
• billing;
• inputting data into electronic patient records.

Logistic Regression

This is a statistical machine learning algorithm that is effective for predicting the probability of disease and classifying ailments.

Examples of use:

• assessing the risk of developing diabetes;
• determining the likelihood of cardiovascular diseases.

Support Vector Machines (SVM)

This is a supervised machine learning algorithm that is best suited for classification, i.e., separating data into different categories. It is used in medicine for diagnostic purposes.

Examples of use:

• differentiating between benign and malignant tumor cells based on biopsy data;
• classifying genes to identify genetic disorders.

Natural Language Processing (NLP)

This is a field of artificial intelligence that enables computers to understand, interpret, and generate human language. It is used to process medical text, such as doctor’s notes or specialized literature.

Examples of use:

• analyzing electronic medical records to extract patient data;
• virtual assistants and chatbots to assist patients outside medical facilities.

Large Language Models (LLM)

These are a specific type of machine learning models trained on large datasets. Their capabilities include understanding and generating human-like language.

Examples of use:

• answering complex medical questions;
• automating clinical documentation.

Deep Learning (DL)

A subset of machine learning that uses neural networks with multiple layers to perform tasks. It is applicable for image recognition and speech processing. It is widely used in medical imaging, as well as in fields such as genomics and pharmaceuticals.

Examples of use:

• developing new medications;
• diagnosing diseases based on visual data.

Reinforcement Learning (RL)

A type of machine learning where an agent learns by interacting with its environment, in other words, through trial and error. This technology helps optimize the effectiveness of treatment strategies.

Examples of use:

• robotic surgical systems;
• personalized treatment for various diseases.

The powerful potential of AI-based technologies suggests that the future of healthcare lies in them.

Future Trends in AI and Machine Learning in Healthcare Projects

It is predicted that in the coming years, AI and machine learning will continue to strengthen their position in healthcare. This is facilitated by:

• better awareness of these technologies among doctors and patients;
• an increase in the number of developers capable of creating and integrating innovative systems;
• a growing demand for skilled healthcare services and medications.

What can we expect in the near future?

Transformation of Primary Healthcare

AI and machine learning systems will take on more responsibility during a patient's initial visit to the doctor. They will classify patients based on their risk level, using examination results. Patients with a low probability of illness will be notified immediately. Those with a high risk will be referred to specialists.

This will reduce the waiting time for referrals and ease the burden on specialists.

Improvement of Healthcare Accessibility

According to the International Labour Organization, 56% of people living in rural areas have no access to healthcare. In cities, the situation is better but still concerning, with 22% of the population unable to access medical services.

AI and machine learning have the potential to fundamentally change this situation. This will be possible through telemedicine and mobile health services.

Focus on Data Security

AI system developers understand the importance of ensuring data security in healthcare. The HIPAA Journal states that in 2023, over 133 million medical records were exposed due to data breaches. This means the problem is becoming global in scale.

As a result, IT specialists will work on enhancing the reliability of the security measures being implemented.

Progress in Early Disease Detection

Modern intelligent software is capable of recognizing symptoms at the very early stages of disease development. However, in the near future, AI capabilities will go even further. The first steps in this direction are already being taken. An example is the Sybil tool developed by scientists from the Massachusetts Institute of Technology. It can predict the development of lung cancer within a year with 94% accuracy.

This is a huge breakthrough in healthcare. It has the potential to save millions of lives.

Emphasis on Personalized Treatment Plans

Doctors will increasingly move away from general prescriptions that do not account for the specific characteristics of individual patients. AI systems will be used to analyze medical histories, genetics, and other factors. Based on this analysis, personalized treatment methods will be selected.

This approach is particularly relevant for treating complex diseases such as cancer.

Darly Solutions Case Studies

Finally, we would like to share the experience of the Darly Solutions team. Our custom healthcare software development company has implemented many successful AI-powered projects for healthcare.

A company approached us with concerns about patient safety during the diagnosis of infectious diseases. They decided to rely on AI and machine learning for the accuracy and safety of their research.

The Darly Solutions development team created a platform for the client that can achieve 99.9% accuracy in clinical diagnostics. To achieve such impressive results, we used the following technology stack:

• Python for machine learning and data analytics;
• JavaScript and React for creating an intuitive user interface;
• WordPress for backend development.

We successfully implemented predictive tools, high-quality data visualization, and a user-centered UI/UX design in the diagnostic solution. As a result of our collaboration, the client’s goals were met. The company received a flexible, scalable, and highly efficient system.

Conclusion

It can be confidently stated that machine learning projects in the medical field are the future of global healthcare. Intelligent solutions have the potential to revolutionize disease diagnosis and treatment, patient care, and drug development.

Do you want to follow the latest trends and provide the highest quality services? Now is the perfect time to find an expert team to develop an AI and machine learning-based medical application.

The Darly Solutions team is ready to assist you with this! Contact our manager to discuss the project details and receive a digital solution that will exceed all your expectations.

Medical device software now drives the core of digital health. AI powers diagnostics, IoT connects patients to providers, and real-time data shifts care from reactive to predictive. According to recent statistics, the global SaMD market is projected to grow from $2.77 billion in 2024 to $52.87 billion by 2033 at a compound annual growth rate (CAGR) of 38.4%. The U.S. advances rapidly with FDA pathways designed for speed, while Europe maintains a GDPR-compliant digital health infrastructure, holding a 30% global market share.

Nevertheless, the future is bright for SaMD; it has benefits and challenges. Let's dive into the meaning of medical device software development, including the steps and types.

What Is Medical Device Software Development?

Medical device software development refers to designing, creating, testing, and maintaining software that is integral to the functionality of medical devices or operates as a standalone medical device. This software must meet strict regulations for safety and accuracy, including those from the FDA, MDR, and ISO 13485. 

Who Benefits from Medical Device Software Development

Demand for medical device software spans many sectors. Let's explore the industries leading the way.

Healthcare Providers

Real-time patient data helps providers make sharper decisions and diagnoses. Automation reduces manual errors and allows staff to focus on care. AI and IoT add efficiency and enable remote, personalized treatment. This delivers faster, safer care while cutting costs.

Medical Device Manufacturers

Medical device manufacturers use software to automate quality control and ensure compliance with regulations. Real-time monitoring reduces errors and helps make proactive adjustments. Scalable updates future-proof devices, while ERP systems improve supply chains, reduce costs, and accelerate time-to-market.

Pharmaceutical Companies

Pharma firms cut trial costs via wearables, automate drug production with digital batch records, and monetize AI-powered SaMD apps that improve adherence. Real-time monitoring speeds up approvals, while AI-driven data tailors therapies and unlocks revenue from digital treatments and SaaS models, sharpening efficiency and market edge.

Telemedicine Providers

Telemedicine thrives in video conferencing and remote monitoring, particularly in underserved areas. Yet, it needs specialized software to connect with medical devices for remote diagnosis, monitoring, and treatment. Modern medical device software improves access to care, diagnostic accuracy, patient engagement, and chronic disease management while also helping reduce hospital readmissions.

Biotech Companies

Biotech companies use medical device software to process complex biological data, speed up drug discovery, and perform diagnostics. Custom tools automate tasks while ensuring compliance with FDA and HIPAA regulations, reducing risk. The result? This tech drives innovation, cuts costs, and accelerates the development of market-ready biotech products.

Clinical Research Organizations (CROs)

CROs gain efficiency and accuracy through automated data handling in device trials. Better data management and real-time monitoring improve trial quality and speed. This leads to faster market entry, reduced costs, and ensured regulatory compliance.

Research and Development Laboratories

Medical device software transforms R&D labs. Thus, teams automate tasks, use AI for sharper data analysis, and lock in compliance from day one. As a result, labs hit faster diagnostics, therapies, and disease control breakthroughs without slowdowns.

Advantages of Software Development for Medical Devices

Software is the new heartbeat of healthcare, making devices smarter, faster, and more efficient. Better outcomes and lower costs are just the beginning. Discover the full positive impact below.

Increased diagnostic accuracy

Medical device software improves diagnostic precision through advanced data analysis techniques. Algorithms detect subtle patterns humans might miss, especially in imaging and laboratory results. Machine learning models continually refine their accuracy by analyzing millions of patient cases, significantly reducing error rates.

In practice, whole-slide imaging in digital pathology achieves a diagnostic concordance of 96.5%, matching that of traditional light microscopy. AI models like GPT-4 show pooled diagnostic accuracy of 52.1%, on par with non-expert physicians but still below experts by 15.8%

Process automation

Automation eliminates repetitive tasks, reduces errors, and allows medical staff to focus on patients. The software handles everything from scheduling to medication management, reducing costs while boosting efficiency. Hospitals report significant time savings and happier staff after implementing automated systems.

Real-time patient monitoring

Connected devices track vital signs without constant staff presence. The software alerts doctors when readings breach safe thresholds for immediate intervention. This capability saves lives in ICUs and helps patients with unpredictable chronic conditions.

Integration with medical systems

Medical software integrates with hospital databases, electronic health records, and laboratory systems. This eliminates data silos and ensures all providers see complete patient information. System interoperability reduces duplicate data entry and lowers the risk of transcription errors.

For instance, integrated medical software reduces duplicate data entry by 30% and transcription errors by over 50%. Hospitals with connected EHR and lab systems report a 25% boost in care coordination and faster clinical decisions.

Regulatory compliance

Built-in audit trails, access controls, and encryption protect patient data while meeting legal medical device software standards. These safeguards prevent costly violations and data breaches that damage trust and finances.

Flexibility and scalability

Medical software adapts to changing needs through modular design and cloud deployment. Solutions scale from single clinics to hospital networks without performance loss. Providers start with core functions and expand as requirements evolve.

Remote access and telemedicine

Telemedicine breaks geographical barriers to healthcare delivery. Remote monitoring enables home recovery while maintaining professional oversight. These systems maintain care continuity during emergencies when facilities reach capacity.

Cost reduction

Software-driven automation delivers significant savings through improved efficiency. Predictive maintenance prevents equipment failures and reduces downtime. Digital transformation reduces administrative overhead, with hospitals reporting a 30% decrease in operational costs. 

Improved user experience

Intuitive interfaces cut training needs and boost adoption among medical staff. Human-centered medical device software design makes complex technology accessible to everyone. Patient apps improve treatment adherence with simple instructions and timely reminders.

Support for analytics and AI

Analytics transform medical data into actionable insights for treatment. AI identifies high-risk patients before acute episodes occur. Machine learning models continually improve with new clinical data, keeping recommendations updated with medical advances.

Key Aspects to Consider in Medical Device Software Development

Medical device software revolutionizes healthcare, but building it is no walk in the park. From regulations and compatibility to testing and ongoing maintenance, the stakes are high, and the margin for error is zero in medical device software development. Here's what matters most and saves lives.

Meeting Regulatory Standards

Regulatory compliance is non-negotiable. Developers must navigate a maze of standards from bodies like the FDA or EMA, which classify software based on risk and dictate the approval process. Whether it's a 510(k) or a PMA, getting them wrong can result in costly delays or outright rejection. Stay updated, plan for compliance from day one, and document everything—regulators love paperwork.

Ensuring Data Security and Privacy

Medical software handles sensitive patient data, which makes security a top priority. Encryption, secure authentication, and regular audits are must-haves to protect against breaches. HIPAA and GDPR aren't just guidelines—they're the law. One misstep can lead to hefty fines and a PR nightmare, so lock it down.

Designing for Usability and User-Centered Experience

If your software design for medical devices isn't user-friendly, it's a liability. Engage with doctors, nurses, or patients early and often to design interfaces that reduce errors and fit seamlessly into their workflows. Accessibility isn't optional—it's essential. A confusing UI can lead to misdiagnoses or delays, so make it intuitive and easy to use.

Ensuring Compatibility with Healthcare Systems

The software never lives in a vacuum. It must comply with EHRs, LIS, or PACS, using standards like HL7 or DICOM. Since interoperability isn't just a buzzword—it's how data flows smoothly in healthcare—build with flexibility in mind. Systems change, and your software should keep up.

Implementing Effective Risk Mitigation

Risk management is baked into medical software development. Use frameworks like ISO 14971 to identify hazards, assess risks, and implement controls to manage them. Plus, tools like FMEA help catch issues before they become disasters. In this field, an ounce of prevention is worth a pound of cure.

Thorough Testing and Quality Validation

From unit tests to clinical validation, every layer counts. Rigorous validation, covering unit, integration, system, and user acceptance testing, helps catch defects early. Include performance, security, and interoperability checks. Pay attention to documenting everything: regulators will demand proof.

Ongoing Maintenance and Software Enhancement

Launch day is just the beginning. Bugs, security patches, and new features require constant attention. Set up a robust post-market surveillance plan to catch issues early. Your software should evolve with healthcare, not fall behind.

Types of Medical Device Software

Based on hardware and regulatory integration, medical device software falls into two primary categories:

Embedded Software for Medical Systems (EMSSW)

This type of software is an integral part of medical devices, such as pacemakers, infusion pumps, and imaging machines. Embedded systems control device functions, ensuring they operate safely and effectively. It is not standalone software like desktop or mobile apps, and it cannot function without the hardware of the medical device.

Examples of Embedded Software for Medical Systems (EMSSW) are:

• Pulse oximeters.
• Smart bio-sensors.
• Glucometers.
• Electronic blood pressure sensors.
• Medical imaging devices, such as X-rays, MRIs, and CT scans.

Standalone Software as a Medical Device (SaMD)

Standalone Software as a Medical Device (SaMD) performs a medical function independently of dedicated hardware. Think algorithms spotting heart issues in ECGs or apps managing diabetes. Regulated by the FDA and EU MDR, it operates on phones, laptops, or clouds, slashing costs and supercharging care delivery.

Examples of Standalone Software as a Medical Device (SaMD):

• Patient imaging or scan analysis.
• Remote ECG-monitoring.
• MRI Viewing applications.

Steps in Medical Device Software Development

When lives depend on technology, there's no room for error. Follow the steps of medical device software development grounded in timeless principles: safety, usability, and trust.

Comprehend Regulatory Standards

Compliance encompasses global frameworks such as IEC 62304 (software lifecycle management) and ISO 13485 (quality management), as well as regional mandates, including FDA design controls in the U.S. and MDR/IVDR in the EU. Data privacy laws, such as HIPAA in the U.S. and GDPR in the EU, govern the handling of patient information. Developers must integrate these standards early, ensuring risk management, usability testing, and documentation meet audit requirements. A deep understanding of these rules prevents costly delays and ensures software reliability in clinical settings.

Conduct Market Research and Identify User Needs

Research market trends and competitors to define your software's unique value. To gather user needs, engage stakeholders, such as clinicians, patients, and administrators, through interviews or surveys. Translate these into clear use cases and functional requirements aligned with clinical workflows. 

Select an Experienced Development Partner

Choose a development partner with proven expertise in medical device software and IEC 62304 compliance. Ensure they understand relevant regulations, such as FDA or EU MDR, and can support submissions and audits. Verify their technical skills in areas such as embedded systems or AI, and confirm that they follow rigorous testing protocols. Prioritize clear communication and strong project management to meet your timeline and goals.

Development and System Integration

Adopt an iterative development approach, such as Agile, tailored to IEC 62304 to balance flexibility and traceability. Design a modular software architecture for scalability and integration with hardware or external systems, such as EHRs, using standards like HL7 or FHIR. Follow secure coding practices and document code for compliance. Integrate software with hardware or APIs, then conduct thorough verification and validation through unit, integration, and system testing to ensure safety and performance.

Deployment and Ongoing Monitoring

Prepare regulatory submissions, such as a 510(k) for the FDA or a Technical File for EU MDR, to gain market approval. Deploy the software in phases, starting with pilot testing in clinical settings to validate real-world performance, train users to ensure proper operation, and minimize errors. Implement post-market surveillance per ISO 13485 to monitor performance, collect feedback, and address adverse events. Use analytics to track usage and identify issues, such as cybersecurity threats.

Ongoing Support and Maintenance

Provide prompt bug fixes and updates to enhance functionality or security, ensuring compliance with regulations for significant changes. Offer user support through helpdesks or chatbots to resolve issues. Plan periodic upgrades to keep up with new clinical needs and technologies. Prepare for software end-of-life by managing data migration and complying with data retention rules.

How to Select the Best Medical Device Software Development Company for Your Project?

First, clearly define your project goals and requirements. List specific features like AI diagnostics or cloud integration, compliance needs (e.g., FDA, EU MDR), and budget constraints. Identify the target users, such as clinicians or patients, to guide usability expectations. This ensures you communicate precise needs to potential vendors.

Second, research companies with a proven track record in medical device software. Check portfolios for projects like remote monitoring or EHR-integrated tools. Review client feedback on platforms like Clutch or KLAS Research. Confirm their experience with regulatory approvals, such as ISO 13485 or CE marking.

Third, evaluate their regulatory expertise. Ask how they handle standards like HIPAA, IEC 62304, or FDA guidelines. Request examples of compliance documentation or successful submissions. Strong regulatory knowledge prevents delays and costly revisions.

Fourth, assess their technical capabilities. Verify expertise in your required tech stack, such as IoT, HL7/FHIR for interoperability, or secure cloud platforms like AWS. Ensure they can integrate with healthcare systems and support cross-platform development. Technical alignment reduces risks and boosts performance.

Fifth, prioritize cybersecurity expertise. Confirm they follow secure medical device software engineering practices like OWASP's S-SDLC and use encryption and authentication. Ask about their process for regular updates and vulnerability patches. Robust security protects patient data and builds trust.

Sixth, check their focus on user-centric design. Review examples of intuitive, compliant interfaces for medical devices. Ensure they conduct UX research to minimize user errors. A user-friendly design improves adoption and care outcomes.

Seventh, confirm their collaboration and transparency. Look for clear communication, detailed project plans, and methodologies such as Agile or Scrum. Request clarity on pricing, timelines, and milestones. Transparent collaboration aligns the project with your vision.

Eighth, compare costs while prioritizing value. Expect custom medical software to cost between $ 200,000 and $400,000, depending on its complexity. Ensure they offer scalable solutions and ongoing support. Choose a vendor that strikes a balance between quality and budget without compromising on standards.

Finally, shortlist 3-5 companies and request detailed proposals from them. Conduct interviews to assess their approach, certifications, and alignment with your goals. Select the partner with the best mix of expertise, reliability, and transparency. Sign a contract only after reviewing terms and ensuring mutual clarity.

The Future of Medical Device Software Development

Medical device software already stands at the cutting edge of healthcare innovation, driving massive change in how we diagnose and treat patients. Yet, with billions of dollars flowing into health tech and regulatory barriers easing, the future of medical device software development is bright for the coming years. 

AI and Machine Learning Integration

AI algorithms are already making waves in the medical device software sector. Predictive analytics and personalized treatments help spot patterns humans miss, delivering faster diagnoses and better outcomes. The tech now works across nearly every medical specialty, and the trends show it won't stop soon, only evolve.

Cybersecurity as Priority

Security doesn't leave the center stage as connected medical devices face sophisticated threats. Manufacturers now build in encryption, strong authentication, and continuous security monitoring from day one. Patient data protection matters as much as clinical functionality.

Edge Computing Implementation

Processing moves to the device itself, shortening wait times for essential procedures. The tech works even when networks fail, and it is a game-changer for implantables and wearables. Edge computing delivers real-time insights exactly when patients need them.

Interoperability Standards

The industry finally tackles its fragmentation problem with standards that work. FHIR and open APIs create true connectivity between previously siloed systems. The seamless data flow means better decisions based on complete patient records.

Sustainability and Scalability

Developers focus on energy-efficient software to extend the battery life of devices. Modular designs allow easy updates without replacing hardware. Cloud integration supports scaling for large patient populations. This reduces costs and environmental impact.

Regulatory Evolution

Regulators are finally catching up to tech innovation with more flexible frameworks. The FDA and international bodies now offer pre-certification pathways and real-world performance monitoring. Companies can innovate faster while still proving their products are safe.

Why Darly Solutions is Your Best Medical Device Software Partner?

No cookie-cutter code here. We're healthcare tech experts who solve real problems. For hospitals, clinics, and MedTech startups, we've delivered over 60 projects that handle messy data, strict compliance, and seamless integration.

Every solution is tailored, whether you're upgrading old systems or launching new ones. Our tech stack is healthcare-tested: secure, compliant, and built to last. Here's how we do it:

Our services cover:

• Custom software development for unique workflows.
• App development that works across devices.
• Cross-platform solutions to reach more patients.
• Automation services to slash admin time.
• UI/UX design that the medical staff actually uses.
• Integration services to connect legacy systems.

We build dashboards that act, not just display. Our scheduling tools cut admin work in half while keeping patients happy. Need e-prescribing or billing that's both secure and fast? We've done it reliably on a large scale.

Need experts who speak healthcare? Whether it's a whole managed IT team or project-specific help, we're here. Outsource your software development to us, and let's build tools that save time, money, and lives.

Conclusion

Medical device software development is more than code—it's about building tools that make healthcare smarter, safer, and faster. With the right partner, you get solutions that boost outcomes, cut costs, and keep you ahead of the curve. Darly Solutions brings deep healthcare expertise, proven results, and a relentless focus on real-world impact. If you're ready to transform your healthcare business, Darly Solutions is the team to trust.

The future of healthcare is digital. In 2022 alone, the global patient portal market reached $2.9B, as the Grand View Research reports. By  2030, it’s projected to grow even higher—at a CAGR of 19.44%. This trend reflects how public and private healthcare organizations strive to digitize paper documentation and automate everyday business operations.

Indeed, the stakes in healthcare are high. Even minor errors can lead to incorrect diagnoses and worsen patients’ conditions. This is where patient portal software greatly minimizes errors, particularly those caused by human factors 

Below, read on how to create a patient portal. We’ll analyze the target audience’s needs, dive into the most successful examples of its implementation, and examine the nature of the target audience's requirements.

What is a patient portal system?

An online patient portal is a web or mobile solution (usually for Android and iOS platforms) that gives patients access to medical records, helps schedule appointments in a few clicks, and easily communicate with healthcare providers at any time. 

By automating the interaction between the parties via the patient portal, organizations can improve the quality of care while making their services more accessible and convenient for patients.

In particular, modern patient portal requirements are:

• Fast and secure access to their medical data and records with guaranteed privacy protection.
• High-quality medical services without the need to wait in long queues.
• A clear understanding of the treatment plan and progress.

Healthcare organizations instead aim to:

• Reduce their administrative costs – in particular, for maintaining paper documentation.
• Maximize personnel efficiency without the risk of burnout.
• Increase patient satisfaction through transparent interaction and remote patient monitoring.
• Comply with the healthcare regulations.
• Reach a new level of income by reducing expenses and attracting new patients.

That is why the patient portal system is the one-stop solution that, if implemented correctly, covers all the above-mentioned needs. This is typically achieved through the following features:

• Access to medical records (including medical history and test results).
• Appointment booking (with the option to select the date, time, and doctor).
• Online consultations (for example, via video calls and chats with doctors).
• Repeat prescription orders (automatic issuance of electronic prescriptions and reminders for refills).
• Online bill payment.
• Personal data management (including patient contact information).
• Push notifications (reminders for upcoming visits, tests, vaccinations, etc.).
• Educational materials.
• Feedback tools (surveys for patient satisfaction and forms for submitting complaints, etc.).

Types of patient portals: Which fits your healthcare needs?

Generally, there are two global types of patient portals: integrated and standalone. The former is presented as a component of electronic health records (EMR/EHR) and is fully tied to the infrastructure of a healthcare facility. Integrated portals ensure uninterrupted data exchange between doctors and patients but, in the case of ready-made solutions, are limited by the functionality offered by the specific healthcare provider.

Standalone portals, however, come in two forms: SaaS and on-premise. The former are hosted in the cloud and are available by subscription, which is especially convenient for healthcare providers within small organizations. Their advantage is quick deployment and configuration. At the same time, their customization options are often limited. 

As for the second category, these are software solutions installed on local servers of a specific healthcare organization. They provide comprehensive control over data but require significant infrastructure and support costs. They can also be difficult to scale and integrate with third-party solutions.

It is worth noting that every medical organization has its own regular processes and specific business tasks distributed among different departments. In particular:

• The reception desk keeps track of patient records.
• Laboratories collect, process, and store test results.
• The finance department is responsible for payments and insurance processes.
• Doctors and medical personnel work with medical records and appointments.

All the above processes can rarely be integrated into a single system, which makes it difficult to automate them using only one off-the-shelf patient portal. Moreover, medical institutions vary greatly in specialization (including clinics, dentistry, laboratories, hospitals, etc.), and ready-made products do not always meet their needs. 

If we add to this the need to comply with regional security standards for patient data, it becomes clear that out-of-the-box solutions may not be viable. This is the main reason why healthcare organizations are increasingly resorting to custom healthcare portal development.

Patient portal requirements: Key features and functionalities

Now, let's check the main functionalities and features that should be integrated into a patient portal.

User authentication and access management system

This portal component uses OAuth 2.0 and OpenID Connect for secure login. It protects users' credentials and enables multi-factor authentication. Usually, developers provide multi-level access with restrictions based on user roles (including patients, doctors, administrators, etc.). 

At the same time, when implementing role-based access, it is important to adhere to widely recognized patient data security policies, such as HIPAA and GDPR. Specifically, doctors and administrators should have full access to all patient data. In contrast, individual patients should only be able to view, edit, and delete their info.

Medical data storage

The storage is located on local or cloud servers using AES-256 and TLS 1.3 for data encryption. By the way, many cloud vendors have ready-made solutions for healthcare organizations that meet the standards of patient data privacy (including AWS HealthLake, Azure Healthcare, and others). It is also crucial to ensure that such storage is protected against data misuse and unauthorized access.

Integration with EHR systems

This integration is necessary for consolidating patient data. Thus, developers must adhere to FHIR and HL7 standards to ensure compatibility and data exchange. As for the integration itself, if we are not talking about legacy solutions, it is carried out through an API. 

Finally, to ensure that the patient portal system complies with the requirements of the National Coordinator for Health Information Technology (particularly, the ONC's Cures Act), it is essential that the platform could get full access to the patient, with all the ability to edit and delete.

Appointment schedules, doctors’ cards, and telemedicine tools

The first two components involve creating a catalog with therapists’ information and a scheduling system where patients can book visits. This can be achieved through RESTful API to synchronize with hospital calendars. Also, integrating with trusted video communication platforms like Zoom Healthcare can facilitate direct communication between patients and doctors.

Automation of bill calculations and online payments

Online payments are processed through integrations with payment gateways like Stripe or PayPal. In general, they must support PCI DSS standards. As for the automated billing system, this is essentially a regular calculator that “pulls up” data on the cost of services selected by the patient and sums them up (minus insurance and benefits, if any).

Integration with wearable devices

This is necessary so that doctors can monitor the condition of patients in real time. To do this, developers use APIs to collect data from devices (for example, from Apple HealthKit or Google Fit, as well as more specialized IoT equipment). At the same time, they must also implement a mechanism for obtaining prior patient consent to collect personal data.

Messaging 

It also makes sense to integrate a chat (with the ability to attach media files) for interaction with doctors, supporting end-to-end encryption (E2E) and using WebSockets protocols that provide real-time messaging.

User dashboards

The patient portal needs an interface for each user role. Basically, it should adapt the data and tools for doctors, admins, and patients. Among other things, it can also be enriched with advanced analytical tools. For example, Power BI or Tableau can be used to generate reports and visualize data (assessing attendance dynamics, treatment results, patient satisfaction, etc.).

How to develop a patient portal software: From concept to launch

When it comes to custom solutions, our telehealth website development services are provided in several subsequent stages. Let’s consider them in detail.

Ideation and requirements analysis

At this stage, our team consolidates the portal concept of custom healthcare portal development, according to your business goals, unique aspects of your internal business processes and IT infrastructure, the needs of the target audience, and an analysis of competitors’ strengths and weaknesses. Based on this blueprint, we create documentation indicating functional and non-functional requirements.

Architecture design

From now on, our patient portal development team has everything ready to define the project's technological stack (for the frontend, backend, and database) and choose a specific architectural approach. We also approve the necessary third-party Integrations (including EHR, payment gateways, telemedicine solutions, etc.). 

Finally, we select tools and protocols to ensure our portal has a proper security level, including data encryption, user authentication/authorization methods, diagrams of interaction between components, etc.

UI/UX design

This stage involves building interface prototypes and gradually transforming them into a full-fledged design after usability testing. At the same time, our patient portal app development experts always take into account the requirements for the accessibility of the user interface. Basically, they are most accurately outlined in the WCAG 2.1 and ADA guidelines.

Development

Now, we can start working on the frontend and backend of the patient portal. Once individual functional modules are ready, we conduct unit testing to prevent hard-to-fix bugs at later stages of the project. This stage also involves the implementation of security mechanisms and integration with third-party solutions.

Testing and quality control

When the code base is ready, our patient portal development experts conduct rigorous testing, including functional, load, security, usability, etc. This stage ensures that the solution meets the best specs and software quality standards in healthcare.

Deployment and launch

Before launching the portal, we prepare all the necessary infrastructure, build a CI/CD pipeline, and set up a monitoring system. If needed, we can train your staff on how to use the portal. After all the previous stages are checked—we usually start deployment.

Post-release support and updates

To better understand ​​​​its further optimization, our patient portal development experts regularly check the portal’s logs and metrics, collecting feedback. In addition, as post-release bugs are discovered, we fix them and, if necessary, implement new features.

Understanding the needs of your patients

A patient portal and medical website design should fully adapt to the target audience's problems. That is why it is essential to define them in advance. Based on our experience, we would like to highlight the following ones:

• Long wait time when making an appointment.
  Patients often encounter long wait times in phone and in-person queues. A patient portal can help. It lets users book online appointments 24/7 and choose a specialist. In addition, it sends SMS or push notifications to remind patients about upcoming visits.
• Lack of full access to medical data.
  Many patients are forced to visit clinics because healthcare organizations still document their test results manually. The patient portal gives access to electronic medical records, test results, and doctor recommendations, which can be easily downloaded or saved (for offline access).
• Lack of understanding of the treatment plans.
  Patients may find it difficult to track their treatments' progress, which may result in forgotten, delayed, or canceled visits and procedures. To prevent this, portals display the treatment plan and the current status of therapy. In addition, patients get notifications about upcoming procedures, calendars, and recommendations, which is very smart.
• Difficulties in paying for medical services.
  The need for patients to be physically present makes offline payments resource-intensive. Patient portals eliminate this headache with integrated payment systems, allowing users to view their payment history and current bills in just a few clicks.
• Insufficient communication with the attending physician.
  In-person appointments to explain test results and treatment plans can be time-consuming for both patients and healthcare providers. Patient portals streamline this process with online chat and video calling capabilities.‍
• Weak control over the treatment of chronic conditions.
  Patients with chronic conditions often lack convenient tools for regular monitoring. Portals can address this with real-time health data visualization, medication reminders, and appointment scheduling, as well as integrating with wearable devices for continuous data collection.‍
• Low involvement in the treatment process.
  Since some patients find it difficult to understand how their actions affect treatment results, the patient portal development team can also add interactive tools to the portal to track patients' treatment progress or provide educational materials.
  Gamification elements can enhance the effect.

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Patient portal development challenges and solutions

In this section, we will analyze the most typical challenges of patient portal software development and the most effective ways to overcome them.

• Ensuring compliance with regulations.
  Telemedicine hugely relies on strong data protection, first—to comply with laws like HIPAA and GDPR, and second—obviously, it is for patient trust. End-to-end encryption, role-based access control (RBAC), and multi-factor authentication are key practices, along with regular system audits and penetration testing to ensure security.
• Integration with existing solutions in the organization's IT infrastructure.
  Many healthcare providers still use older systems that don't easily connect with modern software. To solve this, developers can build 'middleware'—software that acts as a bridge—to automate data transfer between patient portals and other systems, ensuring everything works smoothly.
• Ensuring high performance and scalability.
  Many portals frequently freeze when user traffic exceeds the original design capacity. To solve this, developers can host the portal in a cloud environment that scales as users grow and use a microservice architecture to balance the workload across the portal's services. Additionally, they can optimize a database through indexing and caching.
• Ensuring intuitiveness. 
  ‍Since the target audience of patient portals is diverse (including the elderly, people with disabilities, and people with no technical literacy), there should be universal access to the full use of all portal functionality. To solve this, during the development of the patient portal software, the team should conduct user research and surveys to guide and optimize the software effectively. They should also adhere to the compliance of this software with the WCAG 2.1 guidelines. 

• Accessibility on mobile OSes.
  As patients increasingly manage their healthcare on digital devices, patient portals must offer a seamless mobile experience. Poor mobile optimization can lead to slow loading times and display issues. Developers can address this by building progressive web apps (PWAs) or cross-platform solutions, implementing offline access for key features, and optimizing content loading with techniques like lazy loading and data compression.

Patient portal software cost: What to expect

The cost of telemedicine app development depends on project complexity,  the team involved in the patient portal software development process, geographic location, development timeline, and the scope of features. Let's examine each of the points that influenced the cost in detail:

1. Project complexity

• Basic consultation apps with user registration, appointment scheduling, video calls, and messaging: $30K-$100K.
• Advanced telehealth platforms with multi-user portals, integrated EHR, and health analytics: $100K-$300K+.
• Telemedicine platforms with IoT/AI integration: $250K-$500K.

2. Team composition

A typical team for creating a basic patient portal usually includes:

• 2-3 developers: Where 1-2 are front-end developers, and 1 back-end developer.
• The project manager: Tracks the project goals and wins. 
• QA specialist: Tests the app to make sure it works correctly.

3. Development timeline

The time it takes for patient portal software development also affects costs. Most telemedicine apps take between 2 to 6 months to develop:

• Simple apps usually take about 2-3 months.
• More complex apps may take up to 6-12 months.

4. Geographic location

American tech experts can be expensive. For example, a front-end developer might earn over $100K annually, about $8K monthly. He might charge between $70K-$80K for a three-month project.

Hiring remote developers can save you money. For instance, Eastern European developers or other regions often charge between $2,500 and $3,500 monthly. You can easily hire a similar team for about $30K to $40K for the same project.

If you want to find out the patient portal software cost in your case, please fill out this contact form. 

Improving patient outcomes with data management: A case by Darly Solutions

From theory to practice—let's examine our relevant Darly Solutions experience through a top case from our portfolio.

In particular, we were approached by a HealthTech company specializing in consolidating unstructured medical data for organizations in the healthcare sector. They needed a custom digital solution to monitor clinical data. 

Faced with a tight deadline and the need for high precision, we meticulously planned and assigned all the UX/UI design, web development, QA, and project management tasks. We also reviewed the team's work schedule, as some members had to work overtime. Finally, we carefully chose the technology stack. We settled on Angular, HTML, CSS, Docker, ngRx, TailwindCSS, NGX Pagination, and JSON. 

Finally, our efforts paid off. We launched the product on time and met all design, functionality, and quality standards. As a result, the client has a competitive product with all the chances to thrive in the innovative telemedicine sector.

Find out more about this case here.

The future of patient portal development

In general, the future of patient portal software development is closely connected with the fast development of modern technologies, such as:

• Artificial intelligence and machine learning.
  These technologies will personalize the portal and related healthcare services. AI-powered chatbots, including ones with recognition of human voice commands, will assist with scheduling appointments and answering FAQs. In advance, machine learning can also analyze patient data and symptoms to provide preliminary diagnoses.‍

• Blockchain.
  The introduction of blockchain technologies in patient portals can be aimed at more secure storage of their medical records and ensuring access to them only to authorized users. In addition, with its help, each interaction with data (adding, editing, and deleting) will be recorded, increasing the system's transparency.

• Virtual and augmented reality.
  These technologies can form the basis of next-generation telemedicine solutions. In particular, in the future, patients will be able to interact with doctors at a distance without reducing the quality of service. AR interfaces will also help inform patients about upcoming treatment plans.
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• Internet of Things and integration with wearable devices.
  IoT solutions can provide end-to-end RPM in real-time (for example, portals will be able to collect and analyze data on the patient's pulse, blood pressure, oxygen level, and activity). Based on this data, doctors can receive instant notifications of critical changes regardless of patients' locations.
  ‍
• Big data and business intelligence.
  Examples of patient portals use big data to identify trends, such as tracking the rise in the incidence of a particular virus or assessing the effectiveness of treatments. Also, end-to-end medical data analysis ensures timely prediction of exacerbation of chronic diseases. Finally, doctors and administrators of healthcare institutions will be able to monitor departments' workloads and optimize appointment scheduling.
  ‍
• Biometric identification.
  Biometric mechanisms can give users access to the portal through fingerprint, face, or voice scanning, making the authorization process more secure and easier.

Conclusion

Now you know everything about patient portal development and can safely choose an off-the-shelf solution or search for a team to implement a custom one. If the second option is closer to you but you would not like to turn the search process into a real headache, you can contact us, and we will be happy to discuss the details of our future cooperation to implement your idea.