The Internet of Medical Things (IoMT): True Milestone

Firstly, an interesting fact: there are almost 700 programming languages, but we bet that most developers know and use nearly 15% of the list.

Different programming languages are suited for various projects (mobile and web applications, game development, distributed systems, etc.). Also, it is important to identify the most appropriate language specifically for your project.

In this article, we will look at each of the most popular programming languages according to PYPL and TIOBE rankings in detail — helping beginning developers and entrepreneurs select the right language for starting their digital projects.

Programming Languages Ranking

We tried to find out the most popular programming languages in the year 2024, using official information from PYPL, TIOBE, and GitHub rankings.

• PYPL – The more a language tutorial is searched, the more popular the language is assumed to be. The raw data comes from Google Trends.
• TIOBE – The ratings are based on the number of skilled engineers worldwide, courses, and third-party vendors. Popular search engines are also used to calculate the ratings.
• GitHub – The ratings are created using a yearly review of programming languages’ popularity.

PYPL Ranking 2024

The PYPL (PopularitY of Programming Language) Index is created by analyzing how often language tutorials are searched on Google.

The more a language tutorial is searched, the more popular the language is assumed to be. The raw data comes from Google Trends.

The PYPL index can help you decide which language to study, or which one to use in a new software project.

According to PYPL, 5 most popular programming languages are:

The TIOBE Ranking 2024

The TIOBE (The Importance of Being Earnest) index is an indicator of the popularity of programming languages. The index is updated once a month. The ratings are based on the number of skilled engineers worldwide, courses, and third-party vendors. Popular search engines such as Google, Bing, Yahoo!, Wikipedia, Amazon, YouTube, and Baidu are used to calculate the ratings.

It is important to note that the TIOBE index is not about the best programming language or the language in which most lines of code have been written.

According to TIOBE, 5 most popular programming languages are:

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The GitHub Ranking 2024

GitHub is one of the biggest code repositories that boasts a massive developer community spanning across the globe. The repository is also growing at a blazing speed. Nearly 70% of global Fortune 50 companies have lately made contributions to GitHub’s open-source codes.

GitHub also shares a yearly review of programming languages’ popularity. Here are the dynamics of changing the popularity of different languages according to the latest GitHub report:

Top 5 Most Popular Programming Languages in 2024

Understanding programming is crucial for advancing technology across sectors like IT, data analytics, and engineering. As technology evolves, certain programming languages emerge as essential due to their functionality, ease of use, and adaptability.

So, which programming languages will continue to be in demand in 2024 and beyond? We turn to the PYPL index for insights, which ranks languages by learning interest. Here's a quick look at the top 5 according to PYPL, focusing on why they stand out.

1. Python

PYPL rating — 28.59%

• Pros. Python is regarded as the easiest programming language to learn, because of its simple syntax, a large library of standards and toolkits, and integration with other popular programming languages such as C and C++. It is a popular programming language, especially among startups, and therefore Python skills are in high demand.

• Cons. Python’s cons include slower speed than compiled languages, limited concurrency with the GIL, high memory use, and complex debugging due to dynamic typing. It’s also less suited for mobile, embedded, and real-time applications, where efficiency and low latency are critical.

• Common Use Cases. Python excels in AI, data science, and financial sectors, with frameworks like TensorFlow making it a top choice for machine learning. It's behind major platforms like Instagram and Pinterest and powers apps such as Dropbox and Spotify, showcasing its wide applicability from cutting-edge tech to core web services.

2. Java

PYPL rating — 15.79%

• Pros. Java is a classic choice of many backend developers throughout the world and the only official language for Android app development. Java is designed to be a loosely coupled programming language, meaning that an application written in Java can run on any platform that supports Java. As a result, Java is described as the “write once, run anywhere” programming language.

• Cons. Java's downsides include less efficiency in cloud versus server settings and slower performance compared to languages such as C++. It also uses more memory, which can be a drawback in limited-resource situations. Moreover, Java's detailed syntax can result in longer codes, potentially extending development timelines.

• Common Use Cases. Java is widely used for creating business and Android apps, and it supports major websites like LinkedIn, Amazon, and Twitter. It's also used in server applications, handling big data with Hadoop, and web development using JSP and servlets, highlighting its wide-ranging use in software projects

3. Javascript

PYPL rating — 8.7%

• Pros. JavaScript is essential in front-end development for building interactive websites. With Node.js, Javascript can be used to power server-side content, supporting game and communication applications app development in the browser. Javascript has a lot of frameworks that allow stress-free integration with third-party apps. 

• Cons. Internet browsers can disable JavaScript code from running, as JavaScript is used to code pop-up ads that in some cases can contain malicious content. Additionally, its dynamic typing system can complicate debugging and maintenance.

• Common Use Cases. JavaScript is used extensively in website and mobile application development. Node.js allows for the development of browser-based applications, which do not require users to download an application. Popular apps built with JavaScript frameworks include Slack, Netflix, GitHub, Facebook, Skype, Uber, LinkedIn, Tidal, and more.

4. C#

PYPL rating — 6.77%

• Pros. C#, developed by Microsoft, is widely used in developing Windows applications, web services, and mobile apps. C# is also known for its strong type system, making code more robust and error-free. Thanks to its object-oriented nature, it's highly preferred for enterprise-level applications. Regular updates from Microsoft ensure it remains relevant and efficient for modern software development.

• Cons. While C# is incredibly powerful for Windows platform development, it's less versatile for programming applications on other operating systems, although .NET Core has made significant strides in cross-platform development. Some critics argue that C# can be less efficient for certain types of low-level programming compared to languages like C or C++.

• Common Use Cases. C# is key for creating desktop, web (via ASP.NET), and mobile applications (using Xamarin), especially for Microsoft products. Popular apps like Visual Studio IDE, games such as "Pokémon Go" and "Monument Valley" developed with Unity, and parts of Microsoft Office showcase C#'s versatility in software development.

5. C\C++

PYPL rating — 6.76%

• Pros. As one of the first programming languages ever developed, C has served as the foundation for writing more modern languages such as Python, Ruby, and PHP. It is also an easy language to debug, test, and maintain. C++ is an extension of C that works well for programming the systems that run applications, as opposed to the applications themselves. C++ also works well for multi-device and multi-platform systems. Over time, programmers have written a large set of libraries and compilers for C++.

• Cons. C/C++ has complex syntax and many features that can make it complicated for new programmers. C++ also does not support run-time checking, which is a method of detecting errors or defects while the software is running. C is not suitable for more modern use cases, such as websites or mobile applications.

• Common Use Cases. C++ has many uses and is the language behind everything from computer games to mathematical simulations. C is often used to program hardware, such as embedded devices in automobiles and medical devices used in healthcare. C/C++ is a basic programming language used for creating most existing operating systems, web browsers, and basic Microsoft products.

How to Choose a Programming Language for a Project?

Choosing the right programming language depends on what you need to achieve with your project. Don't just go by how popular a language is. It's better to ask for advice from developers who have worked on similar projects. They can help you pick a language that fits your project's needs, including how it works, its safety, and how it can grow. 

Thinking about which language to use? Feel free to contact us today to make a smart choice for your project's future.

If your website has existed for a long time, this is a reason to think about redesigning it. The fact is that web development trends are constantly changing, and the things that attracted users around five years ago may seem high and dry today. 

In particular, several years ago, the independent web resource GoodFirms conducted a survey among designers to find the reasons why users leave their websites. It turns out that about 40% of respondents were sure that this was due to the outdated design. Do you need to look for more explanations as to why you need a redesign? If you don’t, this article is for you. 

What Is a Website Redesign?

Website redesign is a modernization that involves changes in the design, content, and functionality of a specific web resource. There may be not only a change in color scheme but also in the website’s structure, which also implies a certain optimization of the HTML code.

Redesign can be done every two years, or less often, every three or four years. However, when the appearance of your website remains the same for five years or more, your users are likely to begin to believe that your business is idle and, therefore, will likely choose a more successful competitor.

Why Is a Site Redesign Important?

Let's imagine that when you launched your website first, it generally satisfied its visitors. However, after a few years, its traffic began to gradually decline, although the quality of your service and/or goods did not decrease. Most likely, the problem is that your website’s design is outdated. Although this may seem to be the only reason why you need a redesign, there are several other reasons that also lead to it:

• You are going to migrate your website to a new engine;
• Despite regularly taking SEO measures, your website is not ranking well in search engines;
• You are planning to expand your audience reach and scale your business;
• Your company has rebranded, and now, this should apply to your website as well;
• You are entering a new market;
• Your product range or the concept of its promotion has completely changed.

Why You Need a Website Redesign Project Plan

In general, there are many goals that you can achieve through a redesign. The benefits of website redesign include improving the user experience, rebranding, increasing reputation, optimizing traffic indicators, conversions, etc., involving the website in marketing campaigns, and so on. 

At the same time, whatever goal you pursue, its achievement must be consistent, and each step must be justified from the point of view of business and available resources. Otherwise, you will act chaotically, and the result of these actions will most likely not live up to your desires. To prevent this from happening, you actually need a project plan for website redesign.

How to Redesign a Website: Applying Key Principles for Success

Now, it's time to find out what five sequential phases the correct redesign strategy includes. 

Setting Clear Objectives

Any well-thought-out project plan for website redesign implies matching goals and needs to redesign tasks to be completed.

Understanding the Need for Redesign

At the very beginning, you will need to correctly identify the reasons for the redesign. For example, this could be an outdated design, a not-mobile-friendly user experience, the functionality update and complicated website structure related to it, etc. You need to describe all these reasons as precisely as possible to make it easier for your team of designers (and possibly developers) to decide on the scope of work.

Defining Your Goals

Once the reasons are clear, you can define SMART (specific, measurable, achievable, relevant, and time-bound) goals. Special cases of such goals may be increasing traffic, achieving better conversion rates, improving user experience, etc.

Research and Preparation

At this stage, you will have to complete more complex tasks that will lay a solid foundation for your team's further work. They will also help you understand how competitive your website is at the moment and which aspects of it have the highest priority.  

Analyzing the Current Website

Your website is probably already connected to some analytics system where you can evaluate the dynamics of its traffic, conversion rates, abandoned carts, and so on. Thus, you have to collect this data, evaluate its changes for better or worse over the last year, and compare these changes with seasonal and local trends in your niche to get the most objective assessment of your website.

Competitor Analysis

You will also have to analyze the websites of your main competitors to understand in what they are good at and what needs optimization. Through this analysis, you will be able to prioritize the tasks that your technical team will have to deal with.

Target Audience Identification

Refine your audience data – this is especially important if you're looking to enter new markets or scale your online business. However, even if your goals are less ambitious, you'll still need to refresh information about your potential customers – their demographics, pain points, goals, needs, etc.

Content Audit

Finally, you should conduct a global content audit – for this, you'll probably need the help of SEO experts. Specifically, if your website's main problem is low search engine rankings, it's likely due to poor-quality content that will need to be optimized or replaced with a new one.

Planning and Strategy Development

Actually, you are left with the last steps before you hand over your website rebuild project to your technical team.

Site Structure and Navigation

Based on the key indicators of your website displayed in the analytics, you will be able to determine which of your website’s navigation elements and structure components need to be changed.

Visual Design Considerations

If your current website seems visually outdated, you will need to identify relevant examples from your competitors and indicate which graphical aspects of their websites you like the most.

Technical Requirements

If you find that your website's performance is suffering or, for example, the website itself is not fully displayed on some mobile devices, optimizing these aspects will require the participation of web developers.

SEO and Content Strategy

If the problem is the poor quality of the content (lack of necessary keywords in the texts, low-quality images, outdated data, etc.), you will need to involve SEO specialists in the project as well.

The Design and Development Process

Now, it’s the practical part of your website redesign roadmap, which requires the involvement of technical specialists and the designers themselves.

Wireframing and Prototyping

Now that the project is in the hands of the designers, they can start creating wireframes according to your requirements for the updated version of the website. As your requirements become more detailed, they will build prototypes based on these wireframes.

Design Mockups

An intermediate stage between the development of wireframes and prototypes can be the creation of mockups. Essentially, these are medium-fidelity sketches of your project that, like wireframes, schematically demonstrate the main components of the website but already have a pre-defined color palette and some graphic objects. Typically, this intermediate stage is necessary for websites with a complex and/or non-trivial structure.

Development and Coding

If your site requires migration to a new CMS or you have discovered problems in the previous stages that can only be solved through changes in its program code, you will have to involve developers to eliminate these bottlenecks.

Testing and Quality Assurance

And, of course, don’t forget to staff your project team with quality assurance specialists who will check if the updated version of your website works correctly before it goes public.

Launch and Post-Launch Strategy

Finally, you can begin preparing for the website relaunch.

Preparing for Launch

If your updated website looks the way you like and all the inconsistencies have been ironed out, it makes sense to check its effectiveness on the representatives of its target audience. This way, you may get additional insights about optimization that can still be implemented before your website’s launch.

Launching the Website

Now, developers can upload a new version of the website to the hosting. It may require you to choose a new hosting provider or a new tariff plan from the old one – this makes sense if you plan to expand the reach of your audience.

Post-Launch Activities

This stage will last exactly until the next redesign. In particular, you will have to monitor the analytics of your update to see whether the new key indicators meet your original business requirements. You will also need to constantly work on your website's SEO by regularly uploading new, relevant content.

Conclusion

We hope that in this article, we have helped you understand how to website revamp, and now, you can begin this procedure as consciously as possible. If you are looking for specialists ready to take on not only the planning task but also the implementation of your redesign project, feel free to contact us, and our team will bring your most ambitious goals to life within your budget and timeline.

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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.