How LaaS (Lending as a Service) Is Revolutionizing Fintech

Digitalization fosters the development of all sectors of the economy, making them simpler and more responsive to people's needs. If previously customers and services interacted mostly in person, spending a significant amount of time on each side, now it is possible to shorten the distance to effective solutions. AI developments make it possible to solve typical problems quickly and effectively. In fact, only complicated and non-standard issues are left for a support department.

Many users and business owners are still a bit skeptical about AI developments. In this overview, we will cover the widespread and effectual AI instruments that can strengthen engagement shortly, provided that they are arranged properly. Let's take a deeper look at how two of the most popular AI instruments function. In addition, we will also cover why the apps fit nearly any commercial enterprise. It is definitely now already – the future belongs to them as well.

What is a Chatbot?

This is a specialized software app designated with the purpose of ascertaining the intent of a person's message and picking the most relevant answer in return. Thanks to the conversational interface, chatbot analyzes any input provided by a visitor and highlights the core phrases that mark the user's intent. If you have doubts about whether this is possible to make accurately, there is a simple point here. Statistics and analytics help to shortlist the most common requests and elaborate on the responses to the point. This is the basic original approach. 

The further tech development enabled the creation of a separate AI enabled chatbot system. It is trained on extensive volumes of data and implements machine learning (ML). The latter allows these apps to “brainstorm” an extended range of non-scripted, organic replies to the requests that visitors input. This advancement is coherent with other hardware and apps that a company utilizes.

What Is Conversational AI?

A conversational AI is actually a comprehensive development that includes chatbots too. This means that it covers text and voice altogether. A conversational bot is frequently treated as an upgraded version of a classical textbot.

In addition to ML, conversational AI has implemented natural language processing (NLP). It helps the tools to figure out the viewer's intent correctly and proceed with a deep interaction, respectively. The core advantage of integrating conversational AI is the possibility to emphasize and expand maximally the essence of the message conveyed. That is a point that classical chatbots fail to deliver under any terms. If a company devotes extra resources to enhancing its AI instruments, the latter operates maximally close to human behavior.

Overall, the classification of types of conversational AI fully illustrates the highlighted points:

• AI textbots – handle a textual conversation with the visitors;
• Voice assistants – more intelligent dialog that stands on a spoken language procession;
• Virtual assistants – all-inclusive support with resolving multiple assignments, with an emphasis on textual and voice inputs jointly.

Chatbots (CB) vs Conversational AI (CAI): Brief Comparison

While exploring the nature of the said technological upgrades, it is worth noting that there are far more differences between them that do not lie on the surface.

A classical chatbot and a conversational bot pursue the common aim of optimization and, therefore, guarantee a positive effect on dealing with the website viewers. However, an AI voice bot upgrades the overall communication with the visitors since it adds a voice in addition to the textual support. Therefore, the second one guarantees extra positive outcomes, unlike typical chatbot apps.

Why Choose Conversational AI Over Traditional Chatbots

On balance, a conversational AI-based bot obviously can guarantee extra positive outcomes compared to classical tools, including:

• Better engagement in the communication with the visitors – classical textbots will fail to show the answers outside the scope of the databases of replies; CAI will proceed and succeed with addressing all the inquiries; in other words – CB is rigid while CAI possesses more flexible algorithms.
• Contextual analysis – CB will fail to do this again limiting its effort to the intent only, CAI will cover both equally well. The latter tool delivers customized replies.
• Multitasking – typically, users input several issues to solve and want to know everything at once. Under such terms, CB will recognize one intent only, typically, the first one, but it fails to do so with the rest of the issues. On the other side, CAI will analyze and reply to all the intents that a user indicated in one's message, attributed to the essence of what has been communicated. 
• Scalability – CB is not integrated with the remainder of the tools that a company uses, therefore, they may operate a bit haphazardly. On the other side, CAI is more integrated into the entire IT architecture that a company has built. In addition, it is possible to assess CAI's performance results more precisely.

There is also one scalable and impressive outcome – it is estimated that chatbot conversation apps have facilitated getting the retail sector extra $12 billion in 2023.

Which Solution Suits Your Business Best

It is not likely possible to craft a one-fits-all conversational chatbot solution since, in this case, we survey a very flexible technology. Indeed, it can be modified, highlighting the goals and tastes that a business communicates. This tool can truly address effectively all the challenges that a service provider may face while interacting with its customers. 

Overall, there are several filters to apply when building an AI solution tailored to your actual needs:

• Commercial plans – typically, AI solutions are requested when it is necessary to (i) upgrade the current level of user happiness, or (ii) enter a new level of operation. AI developers address each case individually to make the tech solutions 100% workable as owners think this fits best. 
• Nature of dealings with the visitors and clients – pre-evaluation of the client database and preceding dealings are always made before integrating tech solutions. To this end, it is worth noting that for standard requests and straightforward dialogs with visitors, textbots may suffice. Variable and compound dialogs will demand conversational AI, of course. 
• Viewer's profile – a target audience should be assessed precisely in this course. Nuanced and contextual dialog is very important for better user satisfaction. 
• Budget – it is crucial to find a balance between the funds needed and the earnings AI tech solutions promise to bring.
• Measurable outputs – textbots are rough to measure the effect of their performance on a business. Conversational AI solutions succeed in turning the statistics into gains, which are scalable, of course.

Closing Line

Almost any instrument can be both helpful and irritating. The way the work is arranged always affects the AI's performance and results. If this is done well, AI chatbots and conversational AI can work wonders and make your visitors love you.

Merely brainstorm ideas; perhaps they may even inspire the next developments within the IT sector, ensuring the next upgrade of user engagement. We are ready to ease your life if you find that a bit complicated. Our vast expertise and impressive portfolio enable us to elaborate on and bring to life even the most extraordinary solutions that perfectly align with a business's nature and purpose. Contact us, and we will offer some truly fitting ideas for your business to help it grow further.

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.

In recent years, mental health has taken center stage globally. Currently, approximately 792M people globally have mental issues and lack access to traditional therapy due to financial constraints or societal stigma. The best way to solve the issue — is to go digital. That is where mental health app development comes into play. It cuts costs and makes mental health available to everybody around the globe. 

As to the market, in 2024 alone, the app market saw expansion to approximately $7.38B. In a decade, forecasts promise growth up to nearly 10% annually, scorching $17.52B. 

If you are ready to build a new solution — now is the perfect time to start. Below, we will explain how to develop a mental health app, share standout mental health app ideas, and highlight key factors to keep in mind.

Why mental health apps are in demand

Even though people have faced mental disorders throughout the entire history, only in recent years solving these problems become normal and generally accepted. And it won’t stop. Let’s numbers speak louder — according to 2019 alone, the global capitalization for the global antidepressant market was estimated at $11.67B. By 2027, the market promises to grow to a record $18.29B. 

Of course, depression is not the only issue. According to the World Health Organization (WHO), there is a growing tendency for PTSD, anxiety, and OCD (due to economic instability, military conflicts, pandemics, as well as personal problems). 

Additionally, the fast-paced world daily brings panic attacks, triggers, and burnout (prevalent among doctors, teachers, and IT workers). Youngsters are prone to issues, too; among the most “popular” — are low self-esteem and social isolation.

Financial constraints and a lack of free time put hefty pressure on those who need a traditional approach to addressing mental problems. Then, an in-person visit to a psychologist or psychiatrist becomes a real luxury. But what if there is a more efficient, accessible alternative? That’s where developing your mental health app comes in.

Business and monetization models of mental health applications

For mental health app development, the following monetization models have proven to be the most profitable:

• Fee for the services of real psychologists.
  According to this model, the application acts as a platform, connecting users with professional psychologists, coaches, or therapists (similar to a marketplace with reviews, ratings, cost of services, and other attributes). In this case, the application earns the commission from each transaction (usually 15-30%).
• Subscription-based model.
  This model has a fixed fee for regular access to content and/or services for a certain period (month, year, etc.). If your app has diverse content and functions, offer multiple pricing models. This lets users pick a plan that suits their needs best.
• Freemium.
  These models give free access to basic functionality, with particular features to be paid. Usually, there are personalized recommendations, access to exclusive courses, advanced progress analytics, etc.
• One-time purchase.
  This model involves a one-time purchase of the application, after which the user has access to all its functionality.
• In-app purchases.
  Here, the basic functionality is available for free, but additional materials or services are paid (personalized plans, unique selections of tracks for meditation, etc.).
• Ad-supported model.
  In this case, app is free but includes built-in advertising (banners, videos, native advertising, etc.). At the same time, to improve the user experience, you can offer an option to turn off ads for an extra fee.
• B2B model.
  This model translates into you selling licenses to companies with internal programs to support employee mental health.
• Selling merch.
  This model focused on branded wellness merchandise promotion via scented candles, self-improvement guides, and mindfulness notebooks.

Top mental health apps on the market

Now, let's explore the most popular mental health apps. Perhaps they'll give you extra inspiration for your mental health app development project.

Headspace

Endorsed by the editors of the New York Times and 2.8 million users globally, Headspace is a product of the mental health company of the same name. Its core functionality includes meditations dedicated to improving sleep quality, relaxation, and cultivating mindfulness. They also offer courses on meditation, managing borderline emotional states, and more. Moreover, the platform has scientifically proven results in stress reduction and a user-friendly interface.

Calm

The Calm app is loved for high-quality content and a simple and intuitive interface. by 4.5 million (2023). And it is rightfully so. This app spins everything, from meditation sessions and effective breathing exercises to audio programs that are good for falling asleep faster. As for monetization, there is a free version with limited access.  Full access to functionals is only available with a paid subscription.

BetterHelp

BetterHelp has over 2 million users and connects with licensed therapists via text, phone, and video. Patients must pay a fee to continue their subscription and can also volunteer to donate to others. This app goes far beyond mood issues. It covers complex disorders and symptoms, such as clinical depression and PTSD.

Talkspace

Talkspace provides online therapy with licensed therapists through a tiered subscription model (with health insurance coverage). Moreover, this app is ideal for managing medication treatment and has many formats for direct communication with psychotherapists (including cases of emergencies).

Moodfit

Founded in 2018, Moodfit has become the top mood assistant for more than a million people worldwide. It has personalization options, customized mood data analysis through visualization tools, and mood-improving recommendations. In addition, the app has cognitive-behavioral therapy exercises as a handy and entertaining way to deal with mental issues. 

Woebot

Woebot is a free AI-driven bot founded by Dr. Alison in 2017.  It is based on text format and has cognitive behavioral therapy methods. Initially, this software was intended for youngsters in college and graduate school, but today, people of all ages use it.

Sanvello

Sanvello, created by Dale Beermann and Chris Goettel, provides evidence-based CBT techniques and tools to support mental well-being. Since 2015, the app has offered free features and premium content through subscription or insurance.

Key components of a mental health app

Let's determine what mental health app features should be included by default.

Registration/authentication/authorization system 

The main functionality of this system includes two types of registration: full-fledged, with the user’s email, and fast, through social networks (this is implemented through the OAuth 2.0 protocol, which is suitable for integration with Google, Apple, and Facebook). 

For better protection of user data, use two-factor authentication and store hashed passwords. This can be done, for example, using bcrypt. Additionally, allow password recovery via email or SMS and register log-in/logout events for a data security audit.

User profile

Include the functionality for adding, saving, and changing personal information (name, age, mental health goals), maintaining the user’s activity history (exercise completed, courses, sessions with psychologists, etc.), and privacy settings (public/anonymous profile). Typically, profile data is stored in a relational database (PostgreSQL/MySQL); it is also necessary to set a clear separation between public and private user data.

Home screen and navigation

The home screen should feature a clickable menu, allowing users to update content based on their settings. You can also add push notifications (for example, using Firebase Cloud Messaging). As for the menu, place meditations, psychological tests, stretching, and yoga exercises in text, video, and/or audio format. You can also include real-time communication with specialists via text, voice, and video chats.

Use cloud platforms such as AWS S3 to ensure scalable content storage. If your application is media-rich — add media streaming via HLS. 

Finally, localize content for users from different regions if you plan to maximize your target audience. 

In-app payment system

If you plan to monetize your app through subscription payments or in-app purchases, integrate App Store and Google Play to process payments via the In-App Purchase API. You can deploy the web version via Stripe. Once everything is ready, provide access control based on the paid features.

Analytics and recommendations

Place functionality for tracking user progress and boosting their motivation (e.g., mood changes) and recommendations based on analytics. The latter makes sense to implement using machine learning models (e.g., based on TensorFlow).

What to consider before creating a mental health app?

The basic functionality of a mental health app goes far beyond making it popular and profitable. Therefore, to maximize your chances of success, consider the following factors before creating a mental health app.

Competitive environment

Usually, it involves the user interface, content accessibility, and the quality of the exercises/recommendations. Notably, the Calm application (sleep stories) and BetterHelp (online chat with therapists) are prime examples of such. 

Analysis of bad ratings — matters too. In particular, users usually complain about the high subscription costs, lack of personalization, navigation problems, and negative experiences with customer support service. You can use it to make a competitive feature. 

Target audience

Your target audience — above all. This can be young people (18-35 years old) who need an effective solution for stress management and improving sleep quality. Or the elderly might be searching for a more convenient professional in-home/palliative care format. Finally, it could be corporate clients who want to implement programs for the mental support of their employees. 

Ultimately, user needs may vary. Based on the insights, determine the preferred content format (e.g., meditation, CBT, DBT, video sessions) and presentation style.

Regulatory requirements and user data privacy

The most common and comprehensive regulations are GDPR and HIPAA regulations, which govern the processing of personal data and medical information. To comply with them under the mental health app development process, you must implement data encryption (via SSL/TLS) and ensure transparent privacy policies.

Marketing strategy

To attract a broad user base, create a unique selling proposition. This can be achieved through AI diagnostics, integration with wearable devices via IoT, or simply offering a lower price than competitors. Next, point out the comprehensive marketing strategy. Select the right promotion channels: social media, partnerships with therapists, or advertising through user reviews. is essential for developing a comprehensive marketing strategy. Additionally, to retain users — regularly update content. 

Testing and feedback

It’s better to subject an app with a diverse target audience to beta testing. To do so, find the first users who could be your potential consumers, gather feedback, and optimize the app based on their input.

Financial costs and ROI

Finally, estimate the project budget. It should include development, marketing, updates, customer support, and infrastructure maintenance. Given the costs, you must also estimate how long it will take till your project is profitable. It might be useful for future investors.

What are the main phases of mental health app development?

Now that you have so many insights at hand, you can start the mental health app development process. Let's find out how this happens in practice.

Ideation 

First, define the problem. It is the mental health issues that trouble your audience and their goals. Obviously, your app should cover it. The solution might vary. It could reduce stress, help with sleep, or heal depression. Moreover, in the very first stage — conduct a competitive analysis. This will help identify niche leaders, find the target audience, and formulate a USP.

Planning 

At this stage of the mental health app development process, you will already have enough information to determine the main functionality of your teletherapy application. Select a technology stack (or at least decide whether it will be a native or cross-platform solution), and clarify the business model. Based on this, you can calculate an approximate project budget.

Design 

From now on, your mental health app developers can start creating prototypes and gradually turn them into a full-fledged design. Note that to develop mental health apps, you must use WCAG 2.0 guidelines, which ensure all users can access the app's functionality. Once the mental health app design is complete, test usability with real users to refine the navigation.

Development 

The mental health app development process is divided into frontend and backend. The first includes the implementation of the user part of the software, and the second – everything that happens on the server side. As individual modules are ready, your mental health app developers will need to perform unit and integration testing to prevent difficult-to-fix bugs in the future.

Testing 

Once the entire codebase is ready, QA specialists and testers will create test cases to ensure each function works correctly and the app performs well under high user load. This testing also ensures that all user actions within the app are secure and protect their privacy.

Release

Now that testing is complete, and all detected bugs are fixed, you can publish the application in the App Store and/or Google Play (your development team will first need to check whether it meets all platform requirements). After publishing, you can launch an advertising campaign to attract the first users, as well as set up tracking tools like Google Analytics and Amplitude to analyze user behavior.

Post-release support

At this mental health app development stage, you must check your application's speed and stability regularly. You will also need to update/expand its functionality once in a while and optimize what already exists based on feedback from real users. An equally important point is ensuring compatibility with newer versions of iOS/Android.

Challenges in mental health app development

Various challenges can arise during the mental health app development process. Let’s analyze them:

• Compliance with regulations.
  Mental health apps often handle and maintain personal information, including medical data, which imposes obligations on you as a developer to implement GDPR, CCPA, or HIPAA compliance.
• Balance between personalization and privacy. 

While personalization — is a key feature of these apps, it sometimes conflicts with privacy requirements. To address this, implement advanced mechanisms for anonymizing user data, which will reduce the need to send sensitive information to the server.

• Evidence base.
  The content provided by your app must be scientifically sound and effective for users. To do this, you should either accompany all your treatment plans with individual recommendations from real therapists or initially use approaches with proven effectiveness.
• User retention.
  Many users abandon apps after a few days of use, so your app may need additional retention mechanics (such as gamification and adaptive push notifications) that won’t make users feel guilty or overwhelmed.
• Monetization efficiency.
  Most users search for free or cheap health-related apps. At this point, you need to figure out the balance between user accessibility and the financial sustainability of your software.

How much do developers charge to build a mental health application?

The budget for developing a mental health app development depends on its complexity. So, we can't give exact numbers without first discussing your idea with your development team. But the approximate price ranges from:

• $140K for a teletherapy app.  
• $70K for a simple mood-tracking app with self-monitoring features.

Back-end features, like a database and admin panel, are often overlooked in cost estimates. Developers typically include these in the overall pricing for mental health applications.

If you want to get an estimate of your project, please contact us. 

Mental health app design: Best practices

Now, let's analyze several mental health apps (some of which have already been presented to you above) whose design has pleasantly surprised us. Maybe they will help you think better through your mental health app development process. 

Headspace

This app is praised for its simple and minimalistic interface, with bright illustrations and smooth animations that literally "nudge" users to meditate with its help. It is also adapted for beginners and is generally perceived as intuitive. This approach to design minimizes anxiety in users and motivates them to meditate regularly.

Calm

The aesthetics of the Calm app lie primarily in the author's images of nature, a soft color scheme, and smooth animations. Together, they create an atmosphere of relaxation  - that’s what you need to reduce stress. The app also stands out from its competitors with its "sleep stories" that help people with high anxiety fall asleep faster. 

Reflectly

Reflectly's unique design uses artificial intelligence to offer a personalized experience. A clear, user-friendly interface, dynamic prompts, and mood analytics complement this. Together, these features make the app an ideal choice for audiences of all ages.

Daylio

Daylio's intuitive interface is accompanied by simple icons for tracking the user’s mood and activity. Moreover, all these application elements are easy. Developers ensured that performing target actions doesn’t take too long. The application also has a built-in report generator with visualization elements so that users can monitor their progress.

Happify

The Happify app has a gamified interface. It simplifies and informalises the process of improving the psycho-emotional state. The gamification principles here are based on scientific methods to improve mood. So, it does not add to users' cognitive load.

Mental health app ideas to succeed in 2025

In this section, let’s look through the most viral mental health app development ideas so you can pinpoint your project to success:

• Integration of AI.
  AI assistants can help analyze moods and behavior patterns, give personalized recommendations on how to improve well-being, and remind them of any regular upcoming activities, like a daily mood rate system.
• Social interaction.
  This can be implemented via in-app groups of users with similar mental problems where they could share their healing experiences and receive mutual support.
• Integration with wearable devices.
  Through the IoT, you can analyze heart rate, assess stress levels, determine sleep quality, and create a common mental health map.
• AR and VR tools integration.
  It might be useful for relaxation (to enhance the immersion effect).
• "Instant help".
  Through the screen for quick access to ready-made materials and an online chat with a psychotherapist.
• Content specially adapted for children and adolescents.
  For example, these could be games for managing emotions or recommendations for teenagers on dealing with anxiety and stress;
• B2B solutions for companies.
  in particular, these could be psychotherapeutic assistance to prevent burnout.

Highlights of our experience in delivering healthcare solutions

Finally, we'd like to share our experience developing a mental health app. A client approached us to expand their services to the web, specifically to support small businesses recovering from the recession caused by the coronavirus pandemic. The objective was to create a platform to attract a target audience seeking health-related products and services.

For this project, we assembled a team of ten specialists. We chose Ionic and Vue.js for the frontend tech stack, with Laravel for the backend. The result was an online platform featuring a simplified login process, the ability to schedule and reschedule meetings between clients and trainers, an intuitive search for events and classes, role-based access, and an integrated payment system for online payments (covering group and individual courses, as well as event participation). We also implemented live session connections via Google Meet and Zoom. The solution was complemented by a clean, minimalist design with a calming color scheme, ensuring a pleasant and user-friendly experience.

If you want to learn more about this project, please follow this link.

Final thoughts

With this comprehensive guide, you can confidently start looking for specialists who would bring your idea to life. Don’t go any further, as you are already in the right place! Just fill out this form, and our mental health app development team will contact you as soon as possible!