Product Design vs. UX Design

In 2024 alone, the medical imaging software market size reached $8.11B. By 2029, it is projected to grow to $11.83B and up to 7.84% at a CAGR. This is a fairly predictable trend due to the development of AI. Especially since big data, cloud technologies, and other advancements are already significantly speeding up the accuracy of diagnostics. 

If you are considering custom development of medical image analysis software, now is the most favorable time. Below, we will reveal the specifics of creating such solutions and describe the requirements and the challenges you may face.

What is the definition of medical imaging software?

Medical imaging software—it's the digital tool doctors use to examine medical images. Think X-rays, MRI and CT scans, ultrasounds, PET, and other radiology scans. Basically, it helps to see the details of every complex illness and make informed decisions about patient care.

To maximize efficiency, medical imaging software integrates a range of advanced technologies. These include AI for anomaly detection, ML for image segmentation, and methods for filtering, contrast enhancement, and noise reduction to improve image quality. 

Also, 3D reconstruction technologies create volumetric models of organs and tissues. Developers also rely on the DICOM standard for medical images as it allows seamless transfer. They also use cloud tech to access data, integrated medical records, and VR and AR to visualize data and create interactive interfaces.

As a result, with medical image analysis software, healthcare organizations reduce the workload of their doctors and researchers and minimize the likelihood of misdiagnosis.

Examples of medical imaging software

To better grasp how these solutions work, we suggest you look at several medical imaging software examples that have gained worldwide recognition.

RadiAnt DICOM viewer

It is a high-performance medical imaging software that processes DICOM images. Due to its rich functionality, both doctors and researchers use it in their work. It has smart multimodality tools for 3D and 2D visualization and MPR (multiplanar reconstruction). Moreover, developers made the interface very user-friendly, so this software is also a great choice for users with low technical skills.

OsiriX MD

Specifically designed for macOS, OsiriX MD is a powerful DICOM platform that meets the needs of radiologists. Its advanced capabilities support 3D and 4D image analysis, hybrid imaging with PET-CT and PET-MRI, and integration with PACS servers. Crucially, it is FDA- and CE-certified for clinical us.

Horos

Horos is a free OsiriX-based DICOM viewer available on macOS. It has rich customization options for analyzing volumetric data, such as 3D reconstruction, and is especially useful for students and researchers. 

GE Healthcare Centricity PACS

GE Healthcare Centricity PACS is a proprietary enterprise medical image analysis software. It has EHR and EMR integration, real-time collaboration, advanced AI analysis, DICOM standards, and format support. It can be a full-fledged assistant for doctors and researchers. 

Philips IntelliSpace Portal

Tailored for large clinical institutions, Philips IntelliSpace Portal excels in medical image analysis and visualization. It integrates AI-driven automation and tools for multiparametric imaging in cardiology, neurology, and oncology; this medical imaging software supports multi-user collaboration.

Key features of medical image processing software

This section explores the key functionalities typically found in standard medical imaging software.

Tools for viewing and processing medical images

Ensure your medical imaging software works with various input data (CT scans, MRI scans, X-rays, ultrasounds, and hybrid studies like PET-CT and PET-MRI). Usually, this is done by supporting the DICOM format. In addition, you will need tools to scale, rotate, and adjust image contrast. So, optionally, develop a panel for 3D and 4D visualization, including multiplanar reconstruction.

AI-driven image analysis 

AI is key in automating the detection of anomalies in medical scans. It can identify cancerous tumors, blood clots, and fractures early, with a high degree of independence. Also, AI in your medical imaging software can classify pathologies using trained models. It can segment organs and tissues on scans and analyze multiparametric data.

Diagnostic and treatment planning tools

This includes tools for creating 3D models, surgical planning, and evaluating the effectiveness of treatment. You should also consider integrating your medical imaging software with robotic surgical systems.

Medical data management tools

To implement effective medical data management, you will probably need to integrate your medical imaging software with PACS (for storing and transmitting data), EHRs (for centralized access to personal patient information), and cloud solutions (for unimpeded access to images from anywhere in the world where there is an Internet connection).

Collaboration tools

It's mainly for remote access so doctors and specialists can chat and comment on each other's actions. It also involves integrating telemedicine platforms to discuss complex cases and hold educational seminars.

What development?

A wide range of organizations can benefit from medical image analysis software development. Now, let's find out which areas of healthcare benefit from medical imaging software the most.

• Cardiology.

In this field, medical imaging software is mostly used to analyze CT and MRI of the heart and angiography. In addition, it monitors treatment effectiveness, plans operations, and predicts cardiovascular disease risks.

• Dentistry.

Inevitable for 3D scanning when planning dental implants, diagnosing jaw diseases, visualizing root canals, etc.

• Oncology.
  Here, medical imaging software detects and classifies tumors, tracks their growth, and assesses treatment effectiveness.

• Neurology.

In this sector, medical image analysis software analyzes brain MRIs and CTs and provides 3D visualizations to assess the spine and nerves.

• Orthopedics.

Orthopedics studies thrive on precise X-ray analysis, which includes 3D joint modeling and spinal disease diagnostics.

• Mammology.

Medical imaging software can detect microcalcifications and early breast cancer through comparative analysis of changes in mammary gland tissue.

• Urology.

In this industry, medical imaging software helps diagnose kidney and bladder diseases. It does this by analyzing CT and ultrasound images. Additionally, the software can help plan operations and monitor patients with chronic diseases.

• Pulmonology.

Industry specialists can use such software to diagnose lung diseases, analyze chest CT data, and assess COVID-19 damage.

• Gynecology.

In most cases, medical image analysis software is used to perform pregnancy ultrasounds. It helps monitor the fetus, find pelvic tumors, and analyze the endometrium and other tissues.

• Traumatology and emergency medicine.

In traumatology, 3D medical imaging software can quickly diagnose fractures and internal injuries. It can also visualize organs for urgent decisions.

Still, deciding on the right healthcare sector for your medical imaging project? Contact us and discuss the possibilities of its practical implementation with Darly Solutions' experienced developers.

Medical imaging software development: Steps to follow

Custom development must follow clearly defined stages that most teams use. But, it can still be approached in various ways. Below, we outline how healthcare software development services are delivered in our company.

Concept formation 

Start your medical imaging software project with market analysis. Define the target audience, prioritize tasks the software should solve, and research competitors (to identify their strengths and weaknesses). Based on the insights, our medical imaging software development team assesses the functional requirements and evaluates the need for specific technologies to handle image processing. This ensures that the chosen solutions align with the project's technical needs and optimize the processing of healthcare-related images.

Planning

Once we agree on the conditions with all stakeholders, we will write a technical specification for your medical imaging software. This document will describe its functionality, interface, API, security, and integration requirements. We will also approve the tech stack and necessary integrations. Finally, we create a roadmap that defines the milestones and deliverables for each medical imaging software development project stage.

Prototyping

Now that everything is ready, we can begin creating user stories. They include handling DICOM file uploads and 3D models, among other key tasks. For UX/UI best practices of safe data, we follow the WCAG 2.1 guidelines. They ensure accessibility for users with varying technical skills. We also test prototypes with focus groups to see feedback on complex features, which is helpful for future design improvement. Finally, after the edits are done, we develop a full-fledged design.

Coding

The frontend has algorithms to process and analyze medical images. The backend ensures secure data transfer between the medical imaging software and storage. It also encrypts data and protects against vulnerabilities like SQL injections. These involve writing database queries for smooth software interactions and data storage interactions. And last but not least—we also integrate with your healthcare org's existing systems and services (if any).

Testing

Once the code for your medical imaging software is ready and all components have passed unit tests, we run complete test cases. We check for load, functional, non-functional, security, and usability issues.

Deployment

At this stage, we are choosing hosting for your medical imaging software (usually either cloud or local servers), setting up CI/CD, and training end users, for example, by providing them with documentation, training materials, or live courses. Once we've done it, we deploy the solution (first in the test environment and then—in the actual usage environment).

Support and updates

Finally, after the medical imaging software is deployed, we set up monitoring systems to track its performance and detect errors, fix post-release bugs, optimize it according to user feedback, and add new features if required.

Key tech specifications for medical imaging software development

Such software development can be complex, especially in its early stages. Basically, there is often no clear way to turn an abstract idea into actual requirements. 

So, let's examine all  the key tech specifications that are usually implemented in medical imaging software apps:

• Support for common medical image formats such as DICOM (including DICOM tags for metadata) and standards for storing, transmitting, and processing medical images (such as C-STORE, C-FIND, and C-MOVE).
• Compatibility with various devices (CT, MRI, ultrasound, etc.).
• Image processing can improve images by adjusting contrast brightness and applying filters. It can also segment them to highlight organs and tissues. Lastly, it can register them to compare scans over time.
• 2D and 3D visualization, including volume rendering (CT/MRI), support for iso-sections and reconstructions, and interactivity (e.g., rotation, zoom, and pan).
• Data security, including HIPAA and GDPR compliance, support for TLS (for data transfer) and AES-256 (for image and metadata storage) encryption standards, as well as access control with role-based authorization and two-factor authentication.
• PACS and EHR/EMR integration (e.g., via HL7/FHIR).
• Annotation (adding labels, arrows, and text comments) and providing real-time collaboration tools.
• PDF report generation and image export.
• Scalability (including horizontal scaling via the cloud), multi-threading, and hardware acceleration.
• WCAG 2.1 compliance and user interface customization.
• Logging and monitoring events (including loading, processing, and exporting scans), auditing user access, tracking system performance, and setting up failure notifications.
• Local deployment of software on physical servers (most likely, this will require ensuring compatibility with Linux and Windows OS).
• Setting up regular data backups and automatic recovery after system failures.

Of course, this is just a basic list of specifications. In practice, your project team will expand and refine the list of features while specifying the tools and technologies for the project's unique needs.

Medical imaging software development cost

When it comes to the development cost of medical image analysis software it depends on its complexity and the technologies used. Without data and business needs—it's hard to define the precise price, but on average, basic DICOM (Digital Imaging and Communications in Medicine) typically ranges from $30K to $300K. A customized version of Basic DICOM may cost $30K to $50K. Advanced customizations could cost $70K to $150K.

Implementation costs differ based on the size of the practice:

• Small practices typically cost $5K to $10K and take 1 to 2 weeks.
• Medium facilities cost $20K to $50K and take 1 to 3 months.
• Large enterprises may cost $100K to $200K and take 3 to 6 months.

Please complete this form to calculate the precise budget for your medical imaging software development idea. We will contact you shortly.

Challenges in medical imaging software development

Let's examine the main challenges encountered when developing medical imaging software.

• Regulatory compliance.
  Software handling sensitive data, like patient information, must comply with HIPAA, GDPR, FDA 21 CFR Part 11, and CE Marking regulations. Key security measures include code audits, RBAC, 2FA, and strong encryption (e.g., AES-256, TLS).
  To avoid fines, consult a local lawyer on medical standards.

• Integration with existing systems.
  Integrating PACS, EHRs, and other systems requires DICOM, HL7, and FHIR support. Also, medical organizations have very different established IT infrastructures, which makes it hard to unify their software. If you create a universal solution, you must provide some middleware. It will help users adapt to various services and systems.

• High performance and scalability.

Medical images, especially CT and MRI, are large. This can slow their processing and increase resource needs.
In this regard, you may need to implement lossless compression mechanisms for images and multithreading and parallel data processing algorithms. By the way, a common fix is to move your software to a cloud solution designed for healthcare businesses.

• The complexity of big data management.

Storing and processing massive data, like images and metadata, require a careful choice of databases and storage. In particular, this implies a preference for distributed databases and object storage.
For even greater reliability, do not forget to provide backup and auto-recovery.

• Risks associated with cyber attacks.

Cyber attacks that leak medical data are a serious problem for healthcare software. To solve it, you must implement constant monitoring. Also, set up regular security updates, including patches and OS updates.
Finally, train your staff on social engineering. It can reduce the risks of phishing attacks.
Providing a user-friendly interface.
Interfaces for doctors and medical personnel should be user-friendly and intuitive, requiring minimal technical training to operate efficiently. To achieve this goal, you must test hi-fi prototypes on the real target audience and perform subsequent optimizations. Also, do not forget to ensure your interface is created under the WCAG 2.1 guidelines.

The future of medical imaging software

Medical imaging software development will advance by adopting the newest technologies, process optimization, and increased integration with other medical systems. 

So, here are the core areas in which medical imaging software can be optimized:

• Speeding up diagnostic.
• Increasing image recognition accuracy.
• Costs reduction.
• Improving user experience.

This can be achieved through the implementation and development of the following technologies:

• Artificial intelligence and machine learning.

For highly accurate and automatic analysis of medical images and accelerated diagnostics.

• Cloud computing.

To provide quick access to medical images from anywhere in the world, process large amounts of data without the need to upgrade local infrastructure, and implement remote collaboration between healthcare specialists.

• VR/AR.

Medical imaging software development allows anatomy and pathologies to be studied using interactive 3D models and visualize the patient's anatomy before surgery.

• Quantum computing.

While most quantum computers are not yet available for widespread use, they will speed up processing large datasets and training neural networks for image recognition in a few years.

• Blockchain.

To guarantee the immutability and protection of data from medical imaging software while providing patients with comprehensive control over their medical information.

Our experience in medical imaging software development

This section covers the development of the PrismaORM brain scanner. This platform was crafted for chiropractors, neurologists, and neurosurgeons to monitor brain activity and brainwaves before, during, and after chiropractic treatments.

First, we assembled a team of eight experts to bring this vision to life. They worked closely with two external teams of medical imaging software engineers. We've pointed out a tech stack based on PostgreSQL, Typescript, React Native, Nest.js, Expo, Three.js, and SQLite. This tech of choice lets us build a platform that processes real-time data from brain activity helmets. The BLE protocol transmits this data. A tablet interface visualizes it. A key to the project's success was optimizing the user experience. This included better platform performance and integrating 3D models.

As a result—we've made a powerful tool that empowers medical professionals to conduct more precise diagnostics and offer more effective treatment recommendations. 

For a detailed look at this project, visit our portfolio.

Wrapping up

Now that you understand the specifics of medical image analysis software development, you can begin searching for a team to bring your project to life. We are a reliable provider of custom healthtech solutions, ensuring a smooth, transparent, and predictable collaboration. Simply fill out the form, and we'll get in touch as soon as possible to discuss your medical imaging software project in detail.

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Telehealth, simply put, is some type of delivery. It helps to provide health care services when patients and health professionals are separated by distance via remote technologies. Telehealth uses technologies for the exchange of information for the diagnosis and treatment of diseases and injuries for the patients. Live video conferencing, mobile health apps and remote patient monitoring (RPM) are examples of technologies used in telehealth.

It’s Healthtech time

Telehealth is improving the healthcare industry in many ways, the first of which is increasing its accessibility. Telehealth allows specialists to visit patients virtually from any place in the world by improving access as well as making a wider range of healthcare services available via telemedicine.

Today’s Telehealth environment consists of a global ecosystem of myriad digital solutions.  There are two alarming end-users trends that you should pay attention to when considering Telehealth design and solutions.

1. What doctors struggle the most with
2. What people looking for when they choose the healthcare providers

Providing virtualized healthcare to any place in the world is one of the best-known applications of telehealth. But the medical industry is using it in several other ways, including:

• Urgent help in distance. It can be a headache or sore throat. There are many medical complaints that aren’t life-threatening, but they need some professional attention. Virtual care services allow skilled health personnel to treat minor complaints, including providing appropriate prescriptions.
• Stay in contact. Qualified medical personnel can use messages, phone and video calls to follow up the patients after they are discharged from the hospital. This way, improves worker productivity from not having to take time off and travel to appointments and ensures each patient understands the importance and carries out recovery and treatment plans. Also, telehealth platforms can automate much of the communication process, including sending reminders to the patients.
• Updating online prescription. Telehealth provides the possibility to update online prescriptions even If you’ve been unable to get to your own doctor to refill a current prescription.
• Monitoring of patients with chronic conditions. For these patients, it is one of the best benefits of telehealth. Virtual care helps those who have mobility issues, mental illness, and other conditions that may prevent them from going to in person medical appointments.
• Facilitating care to rural areas. Telehealth is a great way to provide patients in places that are outside the current health delivery system with access to quality care. In the event of a medical emergency, telemedicine makes it possible to coordinate with specialists in other regions without wasting time to provide patients with effective treatment.
• Increased patient satisfaction. In addition to referrals, many patients assess and choose healthcare providers through online reviews. As such, you must have positive reviews that will attract new patients. Telemedicine helps improve patient satisfaction scores by providing convenience of care and reducing wait time. The providers have the opportunity to offer remote services to the patients and make it convenient for them to receive medical attention. Also, this process reduces in-hospital visits.
  

All right, let’s move on to the design

Telehealth platforms require a unique approach to service design. As you can imagine, it isn’t the same way, say, for a food delivery service.

Telehealth is not about technology, it’s about people

That’s a good reminder that you need to create space for telehealth that provides human connections and assistance. The healthcare industry is unique and complex, and it can be challenging to set up. Establishing fundamental principles to guide telehealth design will help us keep sight of the user experience and user journey throughout different healthcare systems.

In this article, we’ll highlight the important and unique challenges in the design faced by digital health companies and startups.

1. Research. To begin, you need a shared understanding of how you usually provide face to face care. You will need a solid understanding of the patient’s journey through your service.
2. Construction. Based on our research findings, you need to discuss them with your client. There is the moment when you draft innovative solutions and delve into user journeys. We identify the most promising ideas based on jointly developed options. Service design can be quite abstract. The main idea is to find key service touchpoints.
3. Strategize and develop Next, it is necessary to work with your clients to co-create a product roadmap and business strategy. Together with medical professionals, you develop an extensive plan of the envisioned telehealth design, listing interactions between users, new processes, and workflows. Also, don’t forget to define the physical and digital things that will be used. By the end of this step, you’ve created visual content and material to start the development process.
4. Taking it live. From our experience, we recommend turning digital and physical artifacts into minimum viable products (MVP).  MVP is essential to clinical trials. There is a product that has basic features and can be used to get feedback from the users.

After the main last modifications, it’s time for launch! At this stage, it’s important to provide testing to ensure that the envisioned workflow and interactions are happening as intended.

That’s it

To sum up, telemedicine is full of benefits for patients and healthcare providers. When people have had a taste of telehealth, they’re willing to continue using this convenient care option.

Telehealth’s future looks very bright, doesn’t it?

Also, it’s obvious to see continued strong growth and upgrade around devices, communication channels, telemedicine services, and telehealth platforms. Look for them to become increasingly user-friendly and convenient. As this happens, we can expect to see a resulting increase in users. After all, good design is the right way to improve engagement.

Designers will need to imagine themselves in both the patients’ and providers’ roles during the preparation for the start to create the design for telehealth. Every detail of a visit, from the method of scheduling appointments to the distribution of follow-up procedures, should be carefully planned to ensure the best outcomes and clear understanding.

We can expect to see digital pharmacies, virtual appointments, online triage tools, and remote monitoring gain in popularity. It may well become the new normal in healthcare.

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Hey, are you here to find a solution for a consolidated and easy-to-access “home” for your business information, which is usually transferred from different places, often unrelated to each other? Then, you probably need to think about creating a digital space where it will be integrated, cleaned, structured, and stored accordingly, for further use in your regular business operations. But what is this space? Let’s find it out right now.

What Is Data Warehousing?

In a nutshell, it is a repository where your business data comes from disparate sources, where it is brought to the proper quality, and where it is stored, providing individuals with the appropriate rights with easy and fast access to it. Typically, such repositories serve as one of the central components in automated analytical solutions, but, as practice shows, the range of their application can be much wider.

Key Components of a Data Warehouse

In general, its main components are considered to be the following:

• Sources, from which structured and unstructured information comes – these can be third-party databases, tables, systems, applications, etc.;
• Data Extraction, Transformation, and Loading (ETL) tools, which define scenarios for extracting, transforming, and loading the information;
• Data warehouse database, which is the main repository (usually in the form of a database management system, DBMS) with already prepared, i.e., cleaned and structured data, that can be used for analytics, reporting, and other business tasks without additional manipulation;
• Metadata repository, which includes the permissible data types and the rules according to which this data will be used;
• Query and reporting tools, which define algorithms for fast and unified access to data, as well as its analysis;
• Data mart, a subset of the data warehouse that is used for individual business tasks (this can imply, for example, marketing data warehouse design) which formation occurs either directly through the collection from disparate sources or pre-preparing within a consolidated space.

Core Principles of Data Warehouse Design

Now, let's talk about the main data warehouse design principles:

• A clear definition of business requirements and goals, as well as metrics that allow you to objectively assess the degree of their achievement;
• Understanding how information is integrated from multiple sources and what should be the unified format after its transformation;
• Choosing the right type of data modeling – these can be star schemas, snowflake schemas, fact tables, and so on;
• Choosing the right methods and data warehouse software to ensure the quality and consistency of information, in particular, the methods of cleansing, validation, and supplementing of data;
• Planning, scaling, and managing the performance through various methods of indexing, partitioning, compression, etc.;
• Choosing the right tools for metadata management to simplify and improve access to the information;
• Ensuring security and access based on policies and rules (usually, this is achieved through the implementation of advanced encryption algorithms, the introduction of access policies for different user groups, as well as providing reliable authentication and authorization methods);
• Implementing end-to-end monitoring for ensuring data quality and security, performance of processes occurring within the data warehouse, and so on.

Steps in Data Warehouse Design

In this section, we invite you to consider the key steps leading to a successful and agile data warehouse design.

Requirement Gathering

According to the principles to design a data warehouse we defined above, the key to effective data warehouse design is to gather business requirements and clear business goals that it should fulfill. It is also important to identify the requirements for security, scalability, and performance of the repository.

Data Modeling

This is not yet a practical stage, but it still requires the participation of specialists – in particular, it implies the identification of entities, their attributes, as well as possible relationships between them. After this, the most suitable type of DBMS implementation needs to be selected – for example, in the form of tables, columns, indexing, etc. After this, you have to make sure that the selected type performs all the tasks assigned to it according to the predetermined requirements.

ETL Process Design

We have already explained above what the ETL process means, and this stage actually involves choosing the right tools and scenarios for their use.

In particular, you will need to define methods for extracting information from disparate sources (these can be database queries, API calls, file transfers, etc.), methods for bringing data to a single format and ensuring its proper quality (since you will most likely have to deal with big data, there may be many inconsistencies), methods for aggregating data to create complex information structures, automation methods for bulk loading of data (this can be full or incremental loading, and so on) with the usage of temporary storage areas, methods for detecting and eliminating data errors, as well as methods for checking data for completeness and accuracy.

Database Schema Design

There are several well-known data warehouse design patterns, such as:

• Star, which has a fact table at its center and dimension tables associated with it around it;
• Snowflake, which is a more complex Star and also implies additional dimension tables that surround each base dimension table;
• Galaxy, which contains two fact tables and the dimension tables between them.

Data Integration

Now, you can start integrating the data using the previously defined ETL tools and technologies. At this step, you need to make sure that everything works as intended, and the data is transformed into the required unified format.

Data Storage Solutions

At this stage, you need to choose specific data storage solutions according to your requirements for the expected data volume, performance, scalability, and cost. Usually, the choice is made between relational databases, columnar databases, data lakes, and cloud data warehouse solutions.

Data Presentation Layer

Finally, to design data warehouse, you will need to understand what your data presentation layer will be – the layer at which end users will be able to seamlessly access the data and use it to solve specific business problems. This includes developing interfaces, dashboards, reports, and various data visualization tools.

Conclusion

Now that you have a clear guide to data warehouse design, you can begin implementing it with a full understanding of the principles and stages on which it is based. If you would like to delegate this comprehensive task to seasoned data warehouse development specialists, just contact us.