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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.

Perhaps you are one of those people who believe that any experienced software engineer can be involved in any project. But in reality, it doesn’t work like that: depending on your task, you may need different expertise from the core members of your development team. Below, we will highlight existing types of software development and briefly consider their specifics.

Types of Software Development

The division of software development into types primarily depends on the platforms for which the created project is intended, that is, the user environment where it will be deployed. This could be a web browser, cloud, mobile, desktop, IoT, or embedded operating system. There are also platform-independent solutions that can be run on several operating systems without any restrictions. 

Ultimately, the deployment environment turns out to be one of the main factors influencing the choice of the project’s technology stack, and, therefore, the developers themselves, who have the appropriate expertise.  

Web Development

Web development involves creating different types of software – web applications and websites – that run through an Internet browser. Along with this, you have to understand that to implement your web solution, you will need several specialists at once whose scope of work will be concentrated within the following subtypes of web development.

Frontend Development

Frontend development concerns the creation of that part of the project with which the end user directly interacts. In particular, this consists of creating a user interface as well as functionality that is performed without the participation of a server and database.

Backend Development

Backend development lies in the implementation of the business logic of the project and its connections with the database/databases. This is its server part, which is hidden from the eyes of end users.

Full-Stack Development

Full-stack development involves a combination of frontend and backend development, but unlike them, it requires the participation of more general specialists who are well-versed in both server and browser technologies.

Mobile App Development

Mobile app development is aimed at the implementation of projects for mobile operating systems – usually iOS and Android. At the same time, there are also cross-platform projects that work equally well regardless of what OS is installed on the end user’s device.

iOS App Development

iOS app development involves creating applications completely tailored to work on the iOS operating system. Such projects require developers to have knowledge of Objective-C, Swift, and Java programming languages, as well as platform-native technologies and tools.

Android App Development

Similarly, Android app development is aimed at projects tailored to the specs of the Android mobile OS. To do this, developers need to have strong programming skills in Java and Kotlin, and be able to work with the Android Toolkit.

Cross-Platform App Development

Cross-platform development allows developers to cover several mobile platforms at once. In this case, they create a universal code base based on a single technology stack, regardless of which OS will run on user devices. From a business perspective, cross-platform projects allow you to achieve faster launch and lower budget since instead of several separate applications and teams that will work on them, you can hire just one that will create a software solution that can be simultaneously deployed on numerous platforms.

Embedded Software Development

Embedded software development involves creating solutions to control machines or devices that are more primitive than computers, called embedded systems. This software development model is completely tailored to the specific hardware on which the software solution runs and has significant restrictions on the consumption of its resources.

Desktop Application Development

Desktop application software development consists of creating applications that are used to work offline on user PCs and laptops. Instead of applying the computing power of servers on the hosting provider's side, they rely entirely on the resources of the user device on which they are installed.

DevOps and Continuous Integration/Continuous Deployment (CI/CD)

DevOps is a set of interaction practices between developers, testers, and other IT specialists in a project team needed to increase their performance and improve the quality of the work results. Typically, DevOps is applied to launch new versions of software regularly. In turn, CI/CD is one of the DeOps techniques, which consists of automating the software development process to ensure a recurrent development life cycle with a continuous delivery of the program code.

IoT (Internet of Things) Development

IoT development, also called M2M development, is aimed at creating software for systems where devices exchange data with each other without human intervention. These could be physical security systems, smart home and smart city solutions, logistics systems, and so on.

Cloud Development

Cloud development involves creating software that is run on a remote web server using a subscription-based delivery model. From the point of view of end users, this approach ensures the high availability of such software products since they only need to have access to the Internet.

Augmented Reality (AR) and Virtual Reality (VR) Development

While the development of augmented reality solutions involves using the camera of a smartphone or tablet on the user's device (this is necessary to overlay digital objects in the user's real environment), virtual reality solutions also need the use of special wearable devices, such as VR glasses. 

Blockchain Software Development

Blockchain software development involves the creation of decentralized platforms, consensus protocols, smart contracts, and other blockchain-related solutions. Although this type of development became popular thanks to crypto projects, today, it covers a long list of niches, from logistics and healthcare to insurance and finances.

API Development

API development is the creation of software tools that allow independent software components to interact with each other using a set of definitions and protocols. Typically, APIs are used to integrate systems, services, and applications to ensure seamless and secure data exchange between them.

Choosing the Right Development Type

At the very beginning of the article, we already indicated that the deployment environment is the main factor influencing the types of software products that you should focus on creating. However, there are also a number of other aspects that will help you make the correct choice. These may include the following:

• Project needs and objectives – they will help your team to define not only the tech stack but also the most suitable software development methodologies (usually, they are related to Agile)
• Your business niche – it can be finance, healthcare, entertainment, e-commerce, industrial, education, logistics and supply chain, or something else
• The resources you have – the specialists you’ve already hired, the project budget, access to data that will operate your software solution, etc.

As for small projects, they are usually dedicated to web development, mobile development, or desktop development — in such software development types, the talent pool is usually wide, and you will be able to choose specialists with the optimal ratio between expertise and rates. However, don't forget that choosing development services that are too cheap may mean you end up having to redo the project from scratch because it doesn't meet your expectations.

Trends in Software Development

Finally, let's take a brief look at the technologies that will be trending in the coming years and which ultimately can provide a strong competitive advantage to your project:

• Artificial intelligence and machine learning – today, these technologies find a wide variety of applications, from personalized search results and chatbots to smart analytics and forecasting
• Augmented and virtual reality – we have already mentioned these two trends above, so we would like to emphasize that their implementation becomes more and more affordable to project owners 
• Internet of Things – we also talked about this trend above; its relevance is explained by the possibility of on-site data collection from devices to which the user does not have physical access
• Personalized user experience – in general, it can be achieved not only with the help of machine learning but also through predefined algorithms
• Blockchain – it’s needless to say that the number of blockchain software development projects is growing every day, and currently, they cover not only the crypto industry

Conclusion

Now, after reading this article, you will be able to understand what type of project you can classify as and what specialists you will need in your project team. At the same time, whichever of the above-described examples of software development you choose, you can always contact us, and we are guaranteed to implement your business idea according to your requirements.

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With more than 2.7 billion smartphone users across the world in 2020, it’s no surprise that the mobile app industry is expended with no signs of stopping. Nowadays, smartphones have become a useful and convenient instrument that offers multiple benefits such as communication through emails and social networking, web surfing, entertainment, GPS and various other productivity apps.

In fact, studies show that the average American checks their phone every 12 minutes. 10% of people check their phones every four minutes.

We use our phones every day at work, at home, on the street, in bed, while we are eating, in our cars. You might be reading this article from a mobile device right now.

What are people doing on their phones? Well, 90% of mobile time is spent on apps.

According to these facts, many companies considering mobile apps are facing a common challenge — which app development approach to choose? Should we build a native, mobile web, or hybrid app? More importantly, which is the most appropriate app type for you and your business?

Let’s find out what are native, mobile web, and hybrid apps, analyze the differences between them, as well as the advantages and disadvantages of each. After reading, you will be able to understand which approach will be the right one for your business idea.

Web Apps

A web app is a software program that uses web technologies like HTML5, CSS, JavaScript. It exists entirely within browsers such as Safari and Chrome and is essentially a mobile website. It is therefore not possible to download them from an app store.

Also, the web app is not connected with the device’s operating system and does not use its storage. Unlike an iOS or Android app, there is no software development kit (SDK) for a developer to work with. There are templates and frameworks for developing web apps like Angular, React, and Vue.js that you can use to get a quick start.

So, here are some pros of web application

• Developing a web app can be simple and quick. So, web apps are a cost-effective way to put your product in the hands of a lot of users.
• Web apps can be built for all platforms as long as they can run in an appropriate web browser.
• The user doesn’t have to manage updates manually. The newest version always loads when a user opens a web app.

And some cons:

• Searching for an app might be difficult because the user doesn’t find it in any app store.
• A browser is required to run a web app. It means that users have to take more steps to use a web app, whether that’s searching for the page or typing in a URL.
• Web apps can be slower and lack a lot of benefits. They only work online and they don’t have access to as many phone features like push notifications, contacts, etc.

However, as browsers and web apps become more advanced, PWAs (progressive web apps) offer a wider functionality like:

• Sending push messages
• Using some touch screen elements
• Using some of the device’s hardware

So, If your startup is on a budget and doesn’t require complex functionalities or access to operating system features, then building a web app can be the most suitable solution.

Native Apps

A native app is a software application that is built for specific platforms and is written in languages that the platform accepts. For example, Swift and Objective-C for native iOS apps, Java or Kotlin for native Android apps and C# for the most part for Windows Phone apps. Native apps are also built using the specific Integrated Development Environment (IDE) for the selected operating systems.

Advantages of the native application:

• Native apps offer the fastest, most reliable and most responsive experience to users.
• Good integration with the hardware of the device (e.g. camera, GPS, phone, touch screen)
• It comes with the push notification functionality.
• Great design and user experience possibilities. For example, the implementation of sophisticated UI/UX design and animation.
• These apps can be downloaded from app stores and installed on mobile devices, where they can be placed as a designated icon on the home screen.
• Native apps are also more suitable for working offline.

Disadvantages:

• Need more than one codebase. iOS apps will not run on Android and the opposite, so you will have to work with different codebases for every platform you choose to build in.
• Native apps are usually more expensive in developing, especially for companies that need apps on multiple OS platforms. For both platforms, it will require two separate developers or teams.
• A lot of time is spent on development. Also, it takes time to update the app for different platforms.
• Require constant updates to keep with the current OS versions.
• Expensive to keep your native app updated
• Both Apple App Store and Google Play get a 30% commission from every in-app payment.

To sum up, a bigger budget is required if you want to build your app for multiple platforms (i.e. iPhones and Android) and to keep your native app updated. Native apps are not the best option for simple applications.

By offering a great user experience, better performance and accessibility, native apps are able to offer users a more personalized product.

Hybrid apps

Hybrid apps combine the power of native and web apps to reap the benefits of both. Actually they are made, so they look and feel like native applications, but like web apps, are built with JavaScript, HTML, and CSS. Also, there is a shell that is downloadable and loads the code using a WebView.

Advantages of the hybrid apps:

• Hybrid apps don’t need a web browser like web apps.
• With a hybrid app, you only have one codebase to manage. So, instead of building two apps like with native apps, you’re building one app, and it works on both platforms.
• Hybrid app developers are often less expensive than native application developers. You will probably require half the number of developers two native apps would have required. Or, with the same number of developers, a hybrid app could be published in half the time.
• Hybrid is a great option for developers that create visually intense applications, for instance, games
• Hybrid apps let you retain the same ability to access device features as with native apps.
• Easy to work with, maintain and upgrade
• Because of their native infrastructure, hybrid apps can work offline.
• Hybrid apps are easier to scale to another platform. Once you’ve built for one platform, you can launch on another, like Windows Mobile.

The downside:

• Getting your hybrid app to run appropriately on each platform generally takes substantial work. Sometimes, the total cost might become comparable to that of fully native apps. It all depends on how close you want to get to the “native user experience” or how simple your app is.
• Hybrid apps can be slower than native apps.
• Need to customize to a large number of exciting browsers
• Inability to implement complex features (e.g. in-app analytics, charts, diagrams)
• These hybrid systems make bug fixing more difficult.
• Hybrid apps load in a WebView, so performance can be worse than native applications.

Say you have an idea for an app, and you don’t know if people will like it or not. If you understand that a web app doesn’t really allow you to test this app, the simple version of the hybrid app will be a good solution for you.

You give to people to download and use the app on their device, although you are short on resources. Usually, a startup project is decided to create a hybrid app that in the startup world, this is called an MVP, or minimum viable product.

Conclusion

There are many different directions in which you can take your app, all of which have their pros and cons. The choice between web, native, and hybrid development is dependent on a number of factors, including business needs, app requirements, developer skill, budget and timelines. What’s important is to spend enough time thinking and analyzing before you start building because the apps are expensive enough. So, you may only have one go at getting it right. Find out the information as much as you can about the different kinds of apps and the development stages.

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