The healthcare sector, despite being the largest repository of data in the world, lags significantly behind other industries in adopting artificial intelligence (AI) technologies. This gap presents a substantial opportunity for innovation and investment.

According to Statista, the AI healthcare market, which was valued at $11 billion in 2021, is expected to soar to an impressive $187 billion by 2030. This rapid growth indicates a shift in the operations of medical providers, hospitals, pharmaceutical companies, and biotech firms, making now an opportune time for startups to engage with this technology.

In this article, we will explore practical AI use cases in healthcare, identify key challenges, and provide insights into the AI healthcare app development process. Whether you're developing new tools or improving existing ones, we will help make your idea real.

Artificial Intelligence in healthcare use cases

From diagnostics and treatment recommendations to operational efficiencies and patient management, Artificial Intelligence is steadily gaining ground in the healthcare sector. Here's a closer look at some of the uses of AI in healthcare.

AI in healthcare examples
AI in healthcare examples

Preventative health

AI, combined with the Internet of Medical Things (IoMT), plays a crucial role in helping people stay healthy and potentially reducing the frequency of doctor visits. Consumer health applications empowered by AI are actively encouraging healthier lifestyle choices and monitoring vital signs to catch early signs of trouble.

  • Wearable fitness trackers: Fitbit and Apple Watch use AI to monitor heart rate, activity levels, and other physiological metrics, and detect irregular heart rhythms and other potential health issues, prompting users to seek medical advice.
  • Personal health assistants: Ada Health helps users understand their symptoms and possible causes. Users can input their symptoms into the Ada app, and it provides information on potential health issues and guidance on when to seek medical advice.
  • Remote patient monitoring systems: Propeller Health uses a sensor-attached inhaler for asthma and COPD management. It monitors medication use and environmental factors to provide feedback and insights, helping patients manage their conditions more effectively and prevent exacerbations.


AI medical diagnosis is possible with the help of advanced data analysis and interpretation capabilities. This technology enhances the speed and accuracy of diagnosing diseases, making it an invaluable tool in modern healthcare.

  • Profound data analysis: Merative utilizes cognitive technology to quickly process and analyze vast amounts of health data, accessing a wide range of medical journals and records to support comprehensive and accurate diagnoses.
  • Search for advanced solutions: Google’s DeepMind Health works in collaboration with healthcare professionals to develop AI solutions that tackle complex medical challenges, employing advanced neural networks for enhanced diagnostic accuracy.
  • Early detection: The early detection of diseases, such as cancer, is one of the most important diagnostic things AI can help with. It enhances the accuracy of mammograms, detecting abnormalities with a 99% accuracy rate and significantly reducing the need for biopsies. AI also monitors early-stage heart disease through wearable technology, allowing for earlier interventions that can be more effective and less invasive. Prenuvo utilizes AI to conduct full-body MRI scans that facilitate the early detection of a wide range of cancers and other diseases.


AI supports the healthcare system by enhancing the management of chronic diseases and personalizing treatment plans, thereby improving patient outcomes and adherence to prescribed therapies. Predictive treatment models: GE HealthCare and Vanderbilt University Medical Center use AI to analyze medical records and predict patient responses to cancer immunotherapies, aiming to prevent ineffective treatments and reduce healthcare costs.

  • Advanced surgical procedures: AI-integrated robots perform complex surgeries with higher precision and control, minimizing risks like infections and reducing recovery times, thereby revolutionizing surgical practices.
  • Optimized clinical trials: Immunai uses AI to map the human immune system, aiming to optimize drug development and treatment strategies, particularly in cancer care. By partnering with pharmaceutical companies, Immunai seeks to refine clinical trial approaches and enhance the precision of medicine.
  • Gene editing: Profluent, an AI biotech startup, is harnessing the power of generative AI in healthcare to enhance CRISPR gene editing technologies. By applying AI models to gene editing, Profluent aims to precisely correct genomic errors and control gene activity, potentially changing treatments for genetic disorders and reducing the risks associated with gene therapy.
  • Fertility treatments: Alife, an AI fertility technology startup, integrates machine learning to optimize the in vitro fertilization (IVF) process. This technology provides clinicians, embryologists, and clinic managers with actionable insights to improve outcomes in fertility treatments. By leveraging AI, Alife aims to address the challenges faced by the increasing number of couples experiencing infertility, including older mothers and LGBTQ+ parents seeking to have children, thus enhancing the efficiency and success rates of IVF procedures.

Patient engagement and adherence

This technology enhances patient engagement, helps reduce dosage errors, and improves overall treatment adherence.

  • Virtual nursing assistants: The study shows that over 60% of patients do not mind using AI to get continuous access to the support that nurses offer. AI-driven virtual nursing assistants provide 24/7 support to patients, assist with medication management, and facilitate communication between patients and healthcare providers.
  • Dosage error reduction: AI can be used to detect mistakes in a patient's self-administration of medications. For instance, a study published in Nature Medicine revealed that up to 70% of patients do not follow their prescribed insulin regimen. An AI tool, functioning in the background similar to a Wi-Fi router, could help identify inaccuracies in the way a patient uses an insulin pen or inhaler.
  • Data structurization: Authenticx utilizes AI to transform unstructured data from decades-old health insurance conversations into structured, actionable insights. This approach helps tap into previously inaccessible information, enhancing decision-making and operational efficiency in healthcare.

End-of-life care

AI and robotic technologies not only provide companionship but also practical assistance to those in the late stages of life.

  • Humanoid robots for social interaction: Companies like Intuition Robotics offer products like ElliQ, which is a social robot designed to keep the elderly company, reducing loneliness and promoting engagement through friendly conversation and reminders for health-related tasks.
  • Robotic assistance for daily activities: Robots like those developed by Diligent Robotics, such as their Moxi robot, help in healthcare settings by delivering supplies and handling routine tasks, allowing staff to spend more time on patient care, including end-of-life care.


AI's role in streamlining administrative tasks in healthcare is growing, with several tools already making significant impacts.

  • Automated note-taking and data entry: Startups like Suki AI offer AI-powered tools that assist doctors with medical note-taking, automatically capturing information during patient interactions to streamline the administrative burden.
  • Enhanced information sharing: Merative offers AI-powered solutions that facilitate the integration and analysis of health data across different systems. This helps healthcare organizations improve efficiency and patient care by enabling seamless access to patient records and insights, thereby enhancing communication and coordination across various departments.


Medical professionals are always short of time and need effective strategies to improve their skills or learn something new. AI improves medical training with more interactive and accessible tools.

  • Simulation-based training: Osso VR provides surgical training in virtual reality, offering highly detailed and interactive simulations that help medical professionals practice operations in a controlled environment.
  • Mobile training applications: Touch Surgery offers a mobile app that features AI-driven surgical simulations, allowing practitioners to practice and refine their techniques on the go.


The California Biomedical Research Association notes that it typically takes 12 years for a drug to reach patients, with only one in 5,000 drugs that start preclinical testing gaining approval for human use. Additionally, the average cost to develop a new drug is around US $359 million. AI is significantly reducing the barriers and accelerating timelines in drug discovery and development.

  • Efficient drug development: Atomwise uses AI to predict molecule behavior and streamline the drug discovery process, helping to identify potential treatments faster than traditional methods. Moderna has recently partnered with OpenAI to further embed AI into its drug discovery processes. This collaboration aims to use AI's capabilities to enhance the speed and accuracy of developing new pharmaceutical treatments.
  • AI in clinical trials: Companies like Antidote use AI to match patients with appropriate clinical trials, speeding up the recruitment process and helping to bring drugs to market more efficiently. SELTA SQUARE integrates AI and automation into the PV process to monitor adverse drug effects from clinical trials through to a drug's market presence. This integration speeds up the process and increases accuracy, ensuring safer medications for global distribution.

Fraud prevention

Fraud in the healthcare sector costs $380 billion annually and consequently increases the medical premiums and out-of-pocket expenses for consumers. Algorithms are able to identify and prevent fraudulent activities, saving costs and protecting resources, thus, causing an overall positive economic impact of AI in healthcare.

  • Pattern recognition for fraud detection: Companies like Change Healthcare employ AI algorithms to analyze patterns in billing and insurance claims, detecting anomalies that may indicate fraudulent activities.
  • Real-time fraud monitoring: Shift Technology applies AI to detect fraud in healthcare insurance claims, providing real-time alerts and improving the accuracy of fraud identification processes.


For 83% of patients poor communication is the most negative aspect of their experience, which highlights a critical need for improved dialogue between patients and healthcare providers. AI capabilities in communication tools can enhance patient outcomes and satisfaction.

  • Advanced communication tools: Hurone AI improves treatment adherence by facilitating communication between clinicians and patients through AI-generated text messages.
  • Medical chatbots: Babylon Health offers an AI-powered chatbot that provides medical consultation based on user symptoms, guiding patients to appropriate care paths and helping to clarify health concerns before reaching a human doctor.

All these possible applications demonstrate a bright future for AI in healthcare. But are there any issues beneath the surface?

Challenges of AI in healthcare

Despite the tremendous potential of AI, there are several challenges that need to be addressed. Here's a breakdown of these issues and some potential solutions:

ai in healthcare challenges


Accuracy remains a significant challenge for AI in healthcare, primarily due to the complexity and subtlety of medical data. While AI systems often demonstrate high levels of accuracy, they are not foolproof and are susceptible to errors, such as misdiagnoses or overlooking critical patient information. For example, GPT-4 (one of the most capable LLMs nowadays) shows very high results, but still tends to hallucinate and give wrong answers:

gpt-4 val test results

These mistakes can have severe, potentially life-threatening consequences.

Currently, AI serves as a supportive tool in healthcare, enhancing the capabilities of medical professionals rather than replacing them. It assists in processing and analyzing data but still requires human oversight to manage its limitations and ensure patient safety.

Possible solution: Partner with medical professionals to continuously train and refine AI models. Focus on hybrid models that combine AI with human expertise to ensure high accuracy and reliability.


AI models can inadvertently develop biases based on the demographics of the data on which they are trained. If a dataset primarily includes information from a particular population group, the AI's recommendations and predictions may not perform as well for people outside of that group. This can lead to disparities in healthcare outcomes.

For example, a study published in Science in 2019 found that an algorithm used in U.S. hospitals was less likely to refer black patients to care management programs compared to white patients, despite similar health needs.

Possible solution: Actively source and include diverse datasets that represent various populations, conditions, and environments. Regularly test AI outputs for bias and adjust algorithms accordingly to ensure equitable health outcomes.

Lack of transparency and explainability

The "black box" nature of many AI algorithms, especially those involving complex deep learning, can make it difficult for both providers and patients to understand how decisions are made. This lack of transparency can undermine trust and confidence in AI systems, which is crucial for their acceptance and integration into healthcare decision-making processes. Additionally, this can be problematic in terms of compliance with legislation.

Possible solution: Develop and utilize tools that can track decision-making processes within AI models. Offer clear, accessible explanations for AI decisions to both healthcare providers and patients, enhancing transparency and trust. Provide training and resources to help stakeholders understand AI's capabilities and limitations, fostering a deeper trust and broader acceptance.

Privacy concerns

The use of AI in healthcare relies on sensitive patient data, raising significant privacy and security concerns. Data breaches in healthcare are not only costly but also damaging to patient trust. According to an IBM Security report, healthcare data breaches are the most expensive, averaging $7.13 million per incident in 2020. This underscores the urgent need for strict data protection measures.

Misuse or unauthorized access to health data can lead to severe consequences, including identity theft and personal harm. Laws like the Health Insurance Portability and Accountability Act (HIPAA) in the U.S. mandate strong safeguards to protect patient information. To address these concerns, healthcare organizations must implement advanced cybersecurity measures, ensure data anonymity, and maintain transparent data usage practices to preserve patient trust and meet legal requirements.

Possible solution: Implement extra security measures, such as encryption and secure access protocols. Obtain patient consent through transparent communication about how data will be used and safeguarded. Stay updated with and adhere to international data protection regulations.

Safety and liability

A 2020 study from the Journal of Medical Artificial Intelligence shows that the adoption of AI technology can be risky for medical providers due to legal issues after mistakes occur.

Key ethical questions include determining who is legally responsible for AI errors, how AI should be involved in end-of-life decisions, and preventing AI from reinforcing existing healthcare disparities or biases.

Addressing these issues requires clear legal guidelines to define AI accountability in healthcare, rigorous pre-clinical testing and validation, and ongoing monitoring to ensure AI systems are safe, unbiased, and equitable for all patients.

Possible solution: Develop a clear framework for accountability where responsibilities are shared between AI developers, healthcare providers, and other stakeholders. Secure insurance that covers AI-specific risks. Promote continuous learning and adaptation of AI systems to minimize errors and improve patient safety.

Standardization gap

A major challenge in healthcare AI is the lack of standardized rules, processes, and guidelines, known as the standardization gap. This gap hinders the effective implementation and scalability of AI technologies in diverse healthcare settings. Without a uniform framework, AI applications may not consistently follow best practices, leading to variations in patient care quality and safety.

  • Regulatory variance: Healthcare regulations vary significantly across and within countries, affecting everything from data privacy to patient safety standards. This variation complicates the deployment of AI systems globally, as developers must navigate a complex and often inconsistent regulatory landscape.
  • Divergent clinical practices: Clinical practices also vary widely between institutions, meaning AI systems designed for one setting may not be suitable for another without costly modifications.
  • Data inconsistency: The data used to train AI models typically come from varied sources with different collection and formatting methods. Lack of standardized data formats and quality checks can cause inconsistencies that affect the performance and accuracy of AI systems. For example, an AI trained on data from one demographic might underperform when applied to another if the data characteristics vary significantly.

Possible solution: Establishing complete and unified AI governance frameworks. WHO has contributed by outlining six principles for ethical AI in healthcare, including protecting autonomy, promoting safety, ensuring transparency, fostering accountability, ensuring equity, and promoting sustainable tools. Healthcare organizations should adopt these principles to guide AI development, ensuring tools are ethical, effective, and beneficial. Regular updates and collaborations with regulatory bodies will also help keep these guidelines relevant as AI technology evolves.

Switchover disruptions

Integrating AI into healthcare often causes switchover disruptions, particularly in the U.S., where such transitions can disrupt profitable operations and lead to financial strain and operational inefficiencies. These disruptions primarily arise from the initial costs, necessary operational changes, and staff training required to implement new technologies.

For instance, research shows that when electronic health records were introduced, medical practices experienced productivity losses and revenue declines during the adaptation phase. Additionally, compatibility issues between old and new systems can create data silos and operational bottlenecks, complicating the transition.

Possible solution: To mitigate these disruptions and facilitate smoother AI integration, focus on:

  • Enhancing provider support: Promote AI as a tool to augment healthcare providers' capabilities, reducing their administrative burden and increasing time for direct patient care.
  • Ensuring effective implementation: Before full-scale deployment, validate that AI applications improve outcomes and are well-understood by all stakeholders to prevent misuse and manage potential risks effectively.
  • Adhering to ethical standards: Follow established ethical frameworks to guarantee that AI applications are safe, unbiased, and privacy-conscious, thereby building trust among patients and the public.

Clinical integration and credibility

Startups developing AI solutions for healthcare frequently encounter challenges in integrating clinical expertise early in product design and in establishing credibility with healthcare executives. The absence of clinicians and frontline providers in the initial design stages often results in products that do not align with actual healthcare needs, diminishing their practical utility and making it difficult to demonstrate their value to healthcare executives who are crucial for their adoption.

Furthermore, variability in executives' understanding of AI technology complicates gaining their support without clear, evidence-based demonstrations of how AI can enhance clinical outcomes or efficiency. Healthcare startups using AI often struggle to establish credibility among clinical stakeholders who are skeptical of the technology's clinical relevance and utility. This skepticism is compounded by the startups' frequent failure to consult with frontline providers early in the development process, resulting in solutions that may not fully align with specific medical workflows or needs.

Possible solution: Startups should involve healthcare professionals early on by forming advisory boards, conducting focus groups, or integrating clinical experts into the development team. This ensures that the product is practical, user-friendly, and meets real clinical demands. As for credibility, it can be enhanced by demonstrating clear, evidence-based benefits of the technology. Engage knowledgeable champions within healthcare organizations who understand both the clinical and technological aspects and can advocate for innovation.

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AI healthcare app development process

Developing an AI application in healthcare involves a detailed and systematic approach to ensure the technology effectively meets the needs of your healthcare organization. Here’s a simplified breakdown of the process we follow:

steps to develop healthcare ai app

Discovery phase:

Initially, we sit down with you to deeply understand your healthcare goals, challenges, and the types of data you work with. This step is crucial as it helps us create AI solutions that are specifically tailored to address your unique needs and opportunities.

Collecting and preprocessing data:

After identifying your needs, we collaborate with you to gather and prepare the necessary data, which could include patient records, sensor outputs, and historical health data. This involves cleaning and normalizing the data to ensure it is ready for effective use in developing bespoke AI solutions.

Designing the AI architecture:

We then design the AI systems tailored to your specifications, which might range from predictive models forecasting patient outcomes to natural language processing engines for medical record transcription. The architecture is built to be scalable, accommodating growth and changes within your organization.

Integrating API and creating a prototype:

With the AI architecture established, we integrate it into your existing systems through robust APIs. This integration is followed by a prototyping phase where the AI models are initially tested in a controlled setup to interact with real-world data. This step provides a preliminary look at how the AI works and what improvements might be necessary.

Training and fine-tuning the model:

Following successful prototyping, we focus on training the AI models with the prepared data, and continuously refining them to achieve optimal performance. During this phase, our team diligently monitors and adjusts the models to ensure they are both accurate and efficient.


This step is essential to ensure the system's functionality, security, and performance. It involves rigorous checks such as functional testing to verify correct outputs and integration testing to ensure seamless component interaction. This helps guarantee that the AI application is safe, efficient, and compliant with healthcare regulations before it goes live.

Launch and maintenance:

Once the AI models have been thoroughly tested and meet industry standards, we fully implement them into your healthcare environment. Our support doesn’t stop after deployment; we provide ongoing assistance and regular updates to the AI models to handle new data and evolving healthcare practices effectively.

This structured development process ensures that the AI functionality not only integrates seamlessly with your operations but also enhances your ability to deliver high-quality healthcare.

Our experience in medical AI development services

We have worked on a variety of AI and healthcare projects for years. Let’s look at some of them.

Amazon Lex bot for doctor booking

Working with a client operating an online clinic SaaS solution, we developed a sophisticated chatbot leveraging Amazon Lex for text-based interactions and Amazon Connect for voice calls. This solution enables effortless booking of doctor appointments and symptom checking. Offering both pre-set and customized responses, the bot seamlessly transitions to human agents when required, ensuring a smooth user experience. Its conversational AI capabilities enable contextual responses, while post-interaction feedback collection enhances service quality.

Greenice chatbot

Greenice AI assistant

We upgraded our website's customer support by incorporating a GPT-4 enabled chatbot, specifically designed to offer detailed insights into our services and facilitate client call scheduling. This chatbot amalgamates Greenice-specific phraseology, behavioral protocols, and functional abilities such as estimating prices and arranging appointments. Its proficiency in analyzing user questions and delivering accurate, pertinent responses has notably streamlined customer interactions.

Healthcare job platform development

We contributed to the establishment of a job board tailored for medical professionals and organizations in the United States. This platform enables healthcare employers to post vacancies at no cost and only pay for qualified candidates they select. Offering geographic job search functionality along with various filters, the website facilitates efficient job matching within the healthcare sector.

Custom medical HRMS

Employing AWS technologies, we designed a comprehensive Human Resources Management System (HRMS) catering to the recruitment, training, and administration needs of a health services agency. This solution significantly improved staff management efficiency and can be deployed as Software as a Service (SaaS), offering scalability and accessibility.

Home service management software

We engineered a sophisticated system to streamline the coordination of healthcare professionals' services with clients' needs. Offering features like scheduling, reporting, invoicing, and patient record management, this HIPAA-compliant system optimizes caregiving processes and ensures comprehensive service delivery.

Custom CRM for mental health counselors

Addressing the need for streamlined data processing in a mental health center, we developed a bespoke CRM solution. This tailored system improved management efficiency, simplified administrative tasks, and ensured data security. Additionally, it offers features like appointment scheduling, treatment plan modification, integration with Electronic Health Records (EHRs)/Electronic Medical Records (EMRs), and comprehensive reporting capabilities.

Dentists' job board platform enhancement

We assisted in enhancing and maintaining a job board platform catering specifically to dentists' needs. This platform facilitates the discovery of both long-term and temporary job opportunities, alongside efficient management of invoices and appointments, thereby aiding dental practices in swiftly filling open positions.

Physical therapy medical application

physiotec website

Our team created software empowering physiotherapists to craft and oversee personalized Home Exercise Programs (HEP) as part of rehabilitation processes. Central to this solution is an electronic database managing these programs, augmented with a mechanism for tracking patients' progress, ensuring optimal therapeutic outcomes.

Assisted living facility management software

We engineered software to automate routine tasks for caregivers in assisted living facilities, allowing them to focus more on patient care. This comprehensive solution includes features such as resident and staff profile management, activity tracking, notifications, messaging, and permission management, enhancing operational efficiency and resident satisfaction.


We've highlighted the significant potential of Artificial Intelligence in medicine, from improving diagnostics and treatment to optimizing administrative tasks. Yet, the journey of integrating AI into healthcare possesses challenges like privacy concerns, the need for standardization, and ensuring safety and credibility. Addressing these challenges will be critical in building robust, trust-worthy AI systems that can significantly improve patient care.

As for the development process, it starts by understanding your needs and then moves on to collecting and organizing data, designing the AI, and integrating it into systems. We make sure everything works well through thorough testing and keep improving the system after launch to meet evolving healthcare demands. This ensures the AI fits smoothly into your healthcare practices.

If you still have questions or need a consultation on your project - we are here to help.

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Inna Lebedeva

Inna Lebedeva is a market researcher and writer at Greenice web development company. She investigates IT niches and writes articles for entrepreneurs who want to launch their business in those niches. Utilizing our experienced Greenice team, and intensive market research, Inna provides in-depth analysis to business owners, enabling them to make informed decisions.

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Olexandr Moklyak

Olexandr is responsible for developing both the back-end and front-end at Greenice. He is well-versed in various technologies, but he particularly enjoys working with Laravel. He is also in charge of several R&D projects experimenting with GPT.

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