The Psychology Behind UX/UI Design

The Psychology Behind UX/UI Design

Daria Lymanska

CEO & Founder

June 14, 2024

min read

If you are working on a digital solution, one of its integral stages will be UX/UI design. While trivial projects usually have “beaten” paths for creating navigation and visuals, any deviation from the standard can lead to dissatisfaction among end users and increase the entry threshold for them. In this regard, no matter how sophisticated the logic of your project is, it is important to understand the usability principles maintained by human psychology to ensure its excellent user experience.

Theories of Perception in UX/UI Design

Generally speaking, there are several popular theories of perception in UX/UI design:

Gestalt psychology aimed at gaining an understanding of how people associate individual objects with things that are meaningful to them. The main gestalt principles include figure-ground (according to which some objects, depending on their visual appeal, are perceived as key navigation elements, while others serve as background), similarity (according to which elements with identical visual characteristics are perceived as interconnected), proximity (according to which closely placed objects are also perceived as related to each other), and closure (which states that incomplete objects, if sufficiently detailed, can be perceived by users as complete).
Affordance theory, claiming that to ensure intuitiveness, users should receive some cues from the interface from the very beginning on how to deal with a particular element – one way to do this is to use skeuomorphism;
Hick's Law, which states that the time it takes a particular user to make a decision is logarithmic to the number of available options (which means that to maximize intuitiveness, the user's choice should always be limited);
Fitts' Law, stating that the time it takes a user to reach a desired part of the interface depends on its size and distance (which means that to ensure a great user experience, you need to place interactive elements closer to the user's starting point);
Signal Detection Theory, claiming that users can subconsciously distinguish between meaningful cues (calls to action) and background noise, meaning that they should be visually separated.

Cognitive Load: Its Effects on UI Design

Now let's move from theory to practice and consider individual insights extracted from the above theories of psychology in UX.

In particular, the first insight is the minimization of cognitive load, that is, the efforts of the human brain that must be applied to perform a specific target action. Thus, the importance of reducing the amount of information or tasks provided to the user becomes clear – otherwise, there is a high probability that the user will begin to act erroneously.

In practice, such brevity can be achieved by correctly grouping interrelated elements and removing elements whose functionality duplicates each other.

Emotional Response & UX Design

Another valuable insight from UX design and psychology is to establish an emotional connection with the end user through your interface. This is a global task that begins with user behavior analysis and identifying their desires, pain points, and intentions and ends with transforming the obtained information into design elements that resonate with them.

Ultimately, you will have to walk in the shoes of the average consumer of your software solution to ensure the proper level of empathy with your emotional design. In terms of practical implementation of the emotional response, such user empathy can be achieved through the integration of “user experience stories” that can be presented by a service provider to users as they get acquainted with the digital solution, as well as the use of the right color palette, forms, audio, pop-ups, and text.

The Role of Memory in User Interaction

Since, according to cognitive psychology in design, the average person can concentrate on one object for up to 20-30 minutes, it is important to take this aspect into account if your software solution involves long-term interaction with it.

Specifically, you can reduce the load on your users' memory by implementing elements and scenarios that work and occur in a way that is familiar to them. Otherwise, if something does not function as expected, it can lead to users’ confusion and erroneous actions.

Understanding User Behavior Through Heuristics

Heuristics are specific UI design principles that can predict user psychology. Here are some of them:

Visibility of system status, which implies the importance of informing end users about what is happening with your software solution at the moment through progress bars, loading animations, or status messages;
Match between the system and the real world, which emphasizes the importance of correspondence between the system's behavior and the knowledge and experience that users received before they became familiar with your software solution;
User control and freedom, which implies the need to provide end users with the ability to freely navigate your software solution and manage negative scenarios;
Consistency and standards, which determine the significance of logical connections between elements, terminology, and layouts;
Error prevention, which consists of preventing erroneous actions by end users by providing them with unambiguous and understandable instructions;
Recognition over recall, which states that to ensure better intuitiveness, users should “recognize” familiar patterns from the real world in elements and scenarios of your interface.

Thus, you have to perform a heuristic evaluation to check whether your solution is good in terms of usability. Also, note that in reality, there are many more psychology UI design heuristics, but the above list will be sufficient for a basic understanding of what is meant by the term excellent user experience.

The Impact of Attention on User Experience

Many designers, in order to encourage the user to perform a particular action, abuse pop-up notifications and other features to attract attention, thereby increasing the cognitive load, the inadmissibility of which we have already discussed earlier.

Instead, you can use such approaches as the correct building of a visual hierarchy, optimization of the color palette, breaking down complex processes into several simple ones, etc. And, of course, do not forget to appeal to the memory of users, which already contains some experience of interaction with other digital solutions and can automate some processes of dealing with them due to the familiarity of the mental models implemented.

User Motivation & Reward Systems in UX/UI Design

Finally, from the point of view of UX psychology, many UX/UI designs benefit from gamification, which motivates users to move toward the target action. In general, gamification is aimed at boosting user engagement and creating an exciting user experience that they don’t want to interrupt. Its main elements are rewards and recognition.

At the same time, it is crucial to note that some reward systems are too confusing for the average user and, thus, create unnecessary cognitive load instead of motivation. Moreover, gamification is not considered a universal approach that could be used in any project. Therefore, you have to always start with the appropriateness and the affordance of implementing this concept clearly and simply for the consumer.

Conclusion

We hope that with the help of this article, you were able to understand the importance of UX design psychology and how it is implemented in UI/UX principles like heuristics, emotional response, lowering cognitive load, user motivation, and others. If you are interested in creating a digital solution that fully meets all these psychological principles, feel free to contact us.

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Remote Patient Monitoring Software Development: Best Practices & Cost | Darly Solutions

January 16, 2025

min read

With the global aging population and the rise of chronic diseases, the remote patient monitoring market is booming as ever. From 2023 to 2024, it grew from $30.97B to $36.31B. And it won't stop here. Insider Intelligence predicts 70.6M US patients will use software for remote patient monitoring by 2025.

We can't overstate the importance of remote patient monitoring software. Such apps improve access to care, reduce hospital readmissions, and help save up to 60% on hospital visits, which results in higher-quality treatment in the comfort of patients' homes.

And benefits don't end up here. With ongoing digitalization via IoT and modernization of the healthcare system, don't wait for the best time for remote patient monitoring software development. The best time is now.

We've detailed remote patient monitoring apps below and covered their benefits, architecture, and top examples. You'll also see how Darly Solutions crafted an app for the health tech company. Read on, and stay tuned.

RPM monitoring software—is a digital tool for wearables, mobile apps, and medical devices that collects, analyzes, and shares patient data with medical personnel. Heart rate, blood sugar, and oxygen levels are among the most vital metrics. RPM software scores help catch health issues early, manage chronic conditions, and aid post-operative recovery.

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Step-by-step strategies for building remote patient monitoring software that ensures reliability, security, and usability

How remote patient monitoring software works

From heart rate to blood glucose, each RPM system is designed to monitor a specific aspect of the body. However, elements included in any remote patient monitoring software development are typically the same. This is how the RPM system works:

Collects and exchanges data.
Once calibrated, the devices send the data from the patient to the provider. The devices must have a Wi-Fi or LTE adapter for successful transfer. The data can also be sent via Bluetooth to IoT mobile apps via RPM devices.
Centralizes storage.

The provider securely stores the data collected from various sources, including patient devices, analytics systems, and e-medicine.

Analyze.
The data is processed using advanced AI for management and analytics. Alerts are sent to caregivers, providers, and patients as needed, per their preferences.
Broadcasts.

Remote monitoring systems become telemedicine platforms when integrated with communication features like video conferencing, simplifying doctor-patient interaction.

Sends emergency notifications.
When doctors identify health issues requiring daily monitoring, they oversee the data collected by the RPM system. Notably, it can notify emergency services if a patient needs urgent care. For non-urgent cases, patients can receive instructions for the next steps. However, HIPAA and similar regulations may restrict including sensitive patient information in certain notifications to protect privacy.

Why build remote patient monitoring software? Key advantages

Nevertheless, the RPM is part of the broader automation revolution; it has universal healthcare benefits overall. Let's examine the main of them:

Better access to health services

With remote patient monitoring software, medical services have no geographical boundaries. Patients can get timely medical attention at home and manage their health, fostering a greater sense of control under health conditions.

Improved patient outcomes

A study found that RPM cut hospital admissions for chronic care complications by 19% to 41%, which results in improved patient outcomes.

Cost-effective care with reduced readmissions

The average cost of hospital readmission is about $15,200, which makes the prevention of readmissions through remote patient monitoring software financially beneficial for both patients and providers. Even more, it is a well-established fact that early detection generally translates to lower healthcare costs.

Better client engagement and satisfaction

Over 94% of patients are satisfied with RPM's care. It sparks their curiosity about their health and boosts engagement. Such an increased sense of involvement often inspires commitment to treatment plans.

Early detection and timely intervention

The CDC reports that RPM can cut chronic disease complications by 40%. This highlights the need for swift intervention to reduce health risks.

Components of remote patient monitoring software

There is no one-fit RPM soft, as it differs in nature. However, each patient monitoring system generally has 3 to 5 basic components. Let’s delve into them:

Personal monitoring devices

From continuous glucose and heart rate monitors to blood pressure cuffs, digital scales, and peak flow meters.

Patient-side mobile applications

Prioritize user experience, making it easy for patients to access their health data. Additionally, they offer features like medication reminders and secure chats with healthcare providers.

Cloud database

Cloud repositories allow healthcare providers to store large patient data securely. They are scalable and accessible, adapting to growing datasets for long-term monitoring.

Hospital-side web app

These applications are part of hospital management software. They ensure compliance, enable integration, and support decision-making. Additionally, they offer reporting, notifications, and analytics to improve patient care.

Other modules

These comprise decision support systems, reporting tools, notification systems, and analytics platforms.

Essential features in remote patient monitoring solutions

Here are the essential features of a remote patient monitoring (RPM) solution:

Real-time tracking of patient vital signs

Wearable sensors, fitness trackers, blood pressure monitors, and glucose meters perfectly gather and handle real-time health metrics. Then, this data is sent to healthcare providers and added to Electronic Health Records (EHRs) for timely interventions and informed decisions preventing complications.

Intuitive interfaces and user-friendly features

Machines must be easy to set up, a breeze to wear, and simple to operate. Meanwhile, patients should access their data effortlessly through a smooth web or mobile app to track their progress. This connection allows seamless communication with healthcare providers, ensuring everyone's on the same page.

Accessibility features

Ultimately, create a system with all users in mind. Consider accessibility guidelines like WCAG and CVAA to pave the way for inclusive innovation. Encompass voice control for effortless data entry, navigation, and text-to-speech features to support people with dyslexia and other disabilities. Moreover, a patient-first design shines with screen reader compatibility, high-contrast modes, and a crystal-clear interface.

Personalized risk alerts and reminders

For swift and customizable alerts, the RPM system must adjust thresholds. For example, alerts can be sent when vital signs fall outside preset limits. This enables timely intervention to reduce the risk of adverse events.

Safe data transmission and storage

Patient data security is a top priority in remote patient monitoring software development. Thus, apps must comply with all regulations like HIPAA, as they protect the privacy and security of patient data and save from hefty fines.

Smooth EHR integration

EHR integration allows the automatic transfer of RPM data, giving healthcare providers access to complete patient histories for better decision-making and outcomes.

Reports and analysis of patient health data

Advanced analytics and reporting in remote patient monitoring software development provide valuable data insights for healthcare professionals (for data-driven decisions) and patients (to motivate healthier choices).

The architecture of a remote patient monitoring system

Remote health monitoring systems are designed with a multi-layered architecture. Basically, it has five core components that enable uninterrupted data flow within the system. Let’s break into the point of the whole structure:

Core architecture of a remote patient monitoring system, featuring IoT integration, real-time alerts, and telehealth support

Smart-connected sensors for health tracking

The sensor layer comprises remote monitoring devices such as EKGs, sleep trackers, smartwatches, and blood pressure monitors. Then, they transmit this data to the dedicated mobile app via Bluetooth or to the cloud via the Internet.

Patient interface

Patient-facing RPM apps collect real-time health data from connected medical devices, allowing manual entry of symptoms and medication intake. RPM apps often include data visualizations for tracking health trends and may have integrated telemedicine features, such as video conferencing for appointments, follow-ups, prescriptions, and virtual consultations.

Cloud RPM server

Cloud services form the core of RPM systems, providing secure storage, processing, and data analysis from devices and the RPM app. This engine also integrates with EHR systems to access patient health information (PHI) in FHIR format. It can identify trends, such as early signs of complications.

Medical staff dashboard

Patient data flows bidirectionally between the medical staff interface and the cloud RPM server, enabling effective management by care teams.

Data visualization module.
Via charts and graphs that show trends in vital signs, medication adherence, and other data, the module gives providers a 360-degree view of the patient.
Alert management module.
The system promptly alerts clinicians to critical situations and device failures in real-time.
Reporting module.
The module generates custom reports on patient data, which hugely help track progress and gain insights over time.
Communication module.
This module has two-way messaging, video chat, and/or telephony integration and is made for secure, remote communication between clinicians and patients.
Appointment management module.

This module handles appointment scheduling and patient-provider communication, allowing both patients and providers to request appointments.

Depending on the organization's needs, a doctor-facing RPM interface can include other modules, such as patient management, care plans, and patient education.

Admin web panel

A key part of the RPM architecture is a control dashboard. It helps with admin tasks like configuring roles, managing patients, and setting alarms.

Remote patient monitoring software development: Key steps

Once you have a clear idea for your software product, find an experienced dev company to implement it. When contacting your tech partner, you don't need detailed software requirements. A high-level understanding of the functionalities is enough to get started for any custom healthcare software development services stages. Let’s break them down:

Key steps of remote patient monitoring software deployment, ensuring data security, user-friendly design, and scalability.

Step 1: Requirement collection and analysis

The process starts by refining your product vision. Then, define project goals based on the health conditions your RPM solution will address. Your team will then identify core functions, pick the best tech stack, and plan the app's architecture. If integration with systems like EHRs is required, your tech partner will ensure compatibility.

Step 2: Outlining security and compliance requirements

Remote patient monitoring software must follow HIPAA and FDA rules. The development team implements audit trails, encryption, secure cloud storage, and APIs for secure communication to ensure data security and privacy. They also define a testing strategy for the pre- and post-production phases.

Step 3: Prototyping and design

To create an intuitive, user-first staff interface, UX/UI teams conduct user research to define user personas and journeys. Based on this research, designers create a prototype reflecting both the initial vision and user needs, gathering feedback on the user experience. The team prioritizes a clean, engaging, and accessible design. Based on feedback, they will refine the prototype and send the final designs to the dev team.

Step 4: Development and integration

Tech experts will use best practices to develop your RPM system iteratively to meet regulatory compliance. This work may include front and backend components, patient and doctor apps, and integration of medical devices.

Step 5: Testing

When it comes to RPM development—thorough software testing is as important as the development itself. After testing, developers should prioritize IoMT device compatibility. They must also ensure smooth integration with the RPM system for the most stability.

Step 6: Ongoing improvements

After deployment, the dev team monitors and tunes the RPM system to meet business and tech needs. They provide training and support for patients and healthcare professionals.

Beyond monitoring and troubleshooting, your tech team will prepare a roadmap for software updates. This will enhance the system's features and security in the future.

RPM Software Deployment: Challenges and Practical Solutions

Let us explore the most notable challenges organizations encounter while deploying their RPM.

Regulatory compliance

RPM's compliance with HIPAA and GDPR relies on strong data security and privacy. It must meet standards like ISO 27001 and IEC 62304 for this.

What to do:

Develop a comprehensive compliance framework according to all requirements like HIPAA, GDPR, FTC (Federal Trade Commission), FDA regulations, and more.
Conduct regular audits and assessments outlining clear policies and protocols for staff.
Provide ongoing staff training to ensure they are aware of their responsibilities.

Data security and privacy

Healthcare organizations handle sensitive patient data (vitals, medical history, personal details, reports, treatment plans). Unauthorized access poses the risk of data breaches and subsequent fines that could total millions of dollars.

What to do:

Use encryption techniques of RPM-collected data, telehealth appointment recordings, and other sensitive patient health data.
Implement strong authentication and role-based access.
Regular audits and security assessments.
Conduct employee and patient training.
Adopt role-based access control (RBAC)

Technology integration and compatibility

Outdated systems may lack compatibility, hindering integration and impacting data accuracy and security. Integrating EHR and clinical systems can also be difficult, affecting data sharing and patient care. Ensuring interoperability for seamless data exchange adds another layer of complexity.

What to do:

Assess current infrastructure compatibility.
Follow interoperability standards and protocols.
Adopt HL7 and FHIR standards.

Cost-benefit analysis

RPM implementation presents a cost-benefit challenge for healthcare organizations. While offering benefits like improved outcomes and enhanced care, the substantial upfront investment in hardware, software, infrastructure, licensing, staff training, regulatory compliance, and data security must be carefully weighed.

What to do:

Conduct thorough cost assessments.
Estimate ROI and potential cost savings.
Strategically allocate resources.

Data precision and accuracy

Inaccurate data transmission from RPM devices (e.g., wearables) poses a significant challenge, potentially leading to misdiagnosis and compromised treatment. Several factors can affect data accuracy, including device calibration, signal interference, network issues, and device quality.

What to do:

Choose quality sensors and wearables.
Regularly calibrate devices.
Implement quality assurance measures.

Lack of patient engagement

Some patients fear switching to modern healthcare and its tech just because they are used to in-person hospital visits. Thus, using the devices, checking vital signs, and messaging the doctor may be hard for the patients.

What to do:

Prioritize a user-friendly interface.
Craft tutorials for device usage and RPM data analytics interpretation.
Incorporate gamification elements (e.g., badges, progress tracking) to incentivize engagement.
Bridge language barriers for diverse patient populations by providing support in multiple languages.

RPM software development cost

The cost of developing Remote Patient Monitoring (RPM) software varies based on complexity, features, and the development team's location. Here’s a breakdown of the estimated costs associated with RPM software development:

Basic RPM application ranges from $50K–$70K.
Standard RPM software ranges from $100K–$400K or higher, depending on the specific requirements and features.
Costs can be $500K or more for advanced solutions with complex systems and integrations.

Both building an in-house tech team and outsourcing software development come with significant costs: salaries/recruitment expenses versus hourly rates and vendor fees. Without proper research, you risk overpaying for a software solution—whether through in-house hiring costs or outsourcing fees—or settling for a product that doesn't meet your requirements.

To calculate the precise budget for your RPM software development idea—please fill out this form. We will contact you shortly.

Best RPM software

Here are some popular remote patient monitoring use cases that might inspire you for your own:

CoachCare

CoachCare was founded over a decade ago, now supports over 3,000 organizations, and serves over 150,000 patients. Boasting a remarkable RPM, it claims a success rate exceeding 95%. It advocates for RPM coverage and collaborates with groups like CMS and AMA. CoachCare's tech lets patients log vital health metrics via an app. Its simplicity boosts engagement. Experts recognize its support of the healthcare industry.

Healee

Healee is a health tech company founded in 2017, now handles over 700,000 visits per month, both onsite and virtual, and supports 5,000 physicians across 200 clinics. With EHR, e-prescription, and billing integrations, the Healee platform streamlines healthcare delivery in wellness, mental health, chronic care, and women's health.

Doctolib

Founded in Paris in 2013, Doctolib is a top healthcare booking platform. It serves over 400,000 providers and 50 million users. It handles 70 million monthly bookings in France, Germany, and Italy. It offers appointment scheduling, telemedicine, and patient messaging. These services reduce missed appointments by 60% and save doctors 1.5 hours per week on administrative tasks.

Senseonics

This company revolutionizes blood glucose monitoring. The system features a sleek, removable transmitter and a sensor expertly implanted under the skin by a healthcare pro. Connected to a mobile app, the sensor offers real-time insights, alerting patients through their smartphones or smartwatches when their glucose levels are too high. Sharing data with healthcare providers and family members becomes a breeze, ensuring optimal treatment strategies.

Currently, around 6,000 users benefit from the Eversense CGM system. By 2025, Senseonics aims to double that number.

With a smart implantable sensor, Senseonics’ Eversense CGM provides real-time glucose monitoring and instant mobile alerts

Teladoc

Established in 2006, this Hoboken, New Jersey-based powerhouse is revolutionizing healthcare. With an extensive network of licensed professionals, HRS proudly claims the #1 spot in KLAS’ ‘Top Remote Patient Monitoring Companies’ from 2020 to 2023.

HRS offers its innovative PatientConnect platform, featuring over 90 tailored RPM plans for healthcare providers. Beyond monitoring, HRS embraces holistic care, providing behavioral health services like therapy and psychiatry, ensuring a comprehensive touch on every aspect of patient wellbeing.

Teladoc’s digital platform connects patients with doctors, offering telehealth, mental health support, and remote monitoring

Improving patient outcomes with RPM software: A case by Darly Solutions

In particular, the health tech company approached us about remote patient monitoring software development. They faced challenges with patient tech literacy, especially among the elderly. There was a risk of data overload for providers. Patient engagement was inconsistent. There was a need for system integration, like with EHRs. Plus, they had to ensure data privacy and security (HIPAA compliance).

Since the deadline was tight and there were a lot of high-precision requirements—we created a detailed plan for the project, assigned UX/UI design, web development, QA, and project management tasks, and pinpointed the tech stack.

Technology stack

We understood that choosing the right technology stack was crucial for success. After thorough consideration, we selected:

Angular: For building dynamic web applications.
HTML/CSS: For structuring and formatting the user’s interface.
Docker: To streamline deployment through containerization.
ngRx: For effective state management.
TailwindCSS: To enable rapid UI development.
NGX Pagination: For efficient handling of large datasets.
JSON: For smooth and efficient data transfer between client and server.

Our efforts paid off. We launched the product on time, meeting all design, functionality, and quality standards. Our client now has a competitive product that is well-placed to thrive in the innovative telemedicine sector.

Read the details of the case here.

Wrapping up

Rising public interest, health concerns, and tech advances drive the RPM market to new heights in solutions and revenue. As we noted earlier, these technologies enable 24/7 health monitoring. They also provide remote populations with access to quality treatments.

Doctors, nurses, and other healthcare staff should focus less on routine, manual tasks in the next few years. They should focus on saving lives and providing treatment, which RPM can support perfectly. It can greatly reduce burnout and ease stress on global healthcare systems while driving market share to higher results and stakeholder benefits.

This is a great time if you're considering developing your RPM solution.

Top Machine Learning Healthcare Projects in 2025 | Darly Solutions

February 3, 2025

min read

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.

AI in healthcare market size with global forecast (2023-2032)

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.

IoT in healthcare market size (2022 – 2032)

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.