Healthcare systems are under growing pressure from rising patient data volumes and increasing demand for personalized care.
Healthcare AI applications have emerged as a powerful solution to these problems by optimizing processes, enhancing diagnostic accuracy, and improving patient outcomes.
A recent study shows that hybrid teams of human clinicians and AI systems make more accurate medical diagnoses, largely because they tend to make different and complementary errors that help correct one another. These findings indicate strong potential of AI to enhance patient safety and promote more equitable healthcare.
Patient Care
1. Assisted diagnosis & prescription
AI-powered chatbots can assist patients with self-diagnosis for mild conditions or help doctors with diagnosis based on symptoms, medical history, and diagnostic data.
A study designed to evaluate how well ChatGPT can diagnose conditions and how often it recommends seeing a doctor found mixed results regarding its diagnostic reliability.
Over five days, the researchers asked ChatGPT the same questions about five common orthopedic conditions. The answers were marked as correct, partially correct, incorrect, or as a list of possible diagnoses. The accuracy and consistency of the answers were measured, and ChatGPT’s ability to accurately diagnose orthopedic conditions was inconsistent.
Also, its recommendations to seek medical care were not always strong. ChatGPT could be useful as a first step, but there’s a risk in relying on it for self-diagnosis without proper medical advice.
Real-life example: DxGPT
DxGPT is an augmented intelligence tool designed to support clinical diagnosis by providing a structured differential diagnosis rather than open-ended text.
It generates five diagnostic hypotheses with symptoms for and against each, using advanced language models within a controlled framework intended to ensure relevance and safety.
The system aims to reduce cognitive load, counter common diagnostic biases, and introduce rare diseases into the differential diagnosis more consistently than general-purpose AI tools. Its development involves continuous collaboration with clinicians, hospitals, and scientific organizations. The tool’s performance is monitored through ongoing evaluations using complex clinical case sets drawn from question banks, published rare-disease cases, and real hospital cases.
Initial validation studies, including work with Sant Joan de Déu Hospital, suggest accuracy levels comparable to clinical experts. However, the system is not intended to provide autonomous diagnoses and must be interpreted by qualified professionals.
DxGPT emphasizes strict data-protection practices, including automatic anonymization, in-memory processing, zero retention of personal information, and compliance with GDPR, HIPAA, and the emerging EU AI Act.
2. Customer service chatbots in healthcare
Customer service chatbots can answer patient questions about appointments, billing, or medication refills.
This can improve the speed and accuracy of diagnoses, reduce the workload on healthcare providers, and allow for better allocation of resources. Doctors can focus on more complex cases, while AI tools provide initial assessments or second opinions for routine ones.
3. AI agents in healthcare
AI agents assist in healthcare by automating tasks, enhancing decision-making, and improving patient care. They analyze medical data for diagnosis, suggest personalized treatments, predict outcomes, and manage administrative tasks.
AI also enables real-time monitoring and virtual consultations, boosting efficiency and reducing errors.
Real-life example: Sully.ai
Parikh Health, led by Dr. Neesheet Parikh, has greatly enhanced its operations and patient care through the integration of Sully.ai with its Electronic Medical Records (EMRs).
The AI-driven check-in system personalizes patient interactions, while automation of front desk tasks allows staff to focus more on patient care.
This collaboration with Sully.ai reduced operations per patient by 10x and cut the time spent on administrative tasks, such as patient chart management, from 15 minutes to just 1-5 minutes. This has led to a 3x increase in efficiency and speed.
Additionally, the platform has reduced physician burnout by 90%, enabling more focused and meaningful patient interactions.
4. Prescription auditing
AI technology helps healthcare providers reduce prescription errors by analyzing prescriptions for drug interactions, incorrect dosages, or potential patient allergies.
This reduces the risk of adverse drug events, a significant source of complications and costs in healthcare.
5. Pregnancy management
AI systems can be employed to monitor the health of both mother and fetus through wearable devices and remote monitoring systems.
These tools leverage data from vitals and other metrics to predict and diagnose potential complications early. This improves pregnancy outcomes and reduces maternal and infant mortality rates.
6. Real-time prioritization triage
AI-based prescriptive analytics can analyze patient data such as symptoms, medical history, and vitals to help healthcare professionals prioritize cases in real time.
Real-life example: Lightbeam Health
Lightbeam Health leverages predictive analytics to foresee health risks in patients.
It analyzes over 4,500 factors, including clinical, social, and environmental determinants, to identify hidden risks. The system also provides prescriptive recommendations for targeted interventions that improve patient outcomes, such as reducing readmissions and emergency visits.
Real-life example: Wellframe
Wellframe enables healthcare professionals to deliver personalized, interactive care programs directly to patients through a mobile app. The platform’s clinical modules are built based on evidence-based care to ensure that patients receive guidance from proven medical practices.
The app also supports real-time communication between care teams and patients for continuous monitoring and immediate intervention when needed.
Healthcare professionals can customize the experience for each patient while addressing individual health conditions, such as chronic disease management or post-discharge follow-up.
Wellframe’s AI technology provides patients with tailored care plans and also equips clinicians with data insights through a dashboard. This real-time information helps prioritize high-risk patients and facilitates more efficient healthcare delivery.
Wellframe enables better patient outcomes through these capabilities, supports preventive care, and provides more personalized relationships between patients and their care teams.
7. Real-time triage
Integrating AI for prioritization ensures that the most critical cases are treated first, thereby enhancing emergency room efficiency and leading to improved patient outcomes.
Real-life example: Enlitic
Enlitic’s patient triaging solutions leverage AI technologies to enhance the efficiency of healthcare systems by scanning incoming medical cases and assessing them for multiple clinical findings.
These findings are then prioritized, ensuring that the most urgent cases are routed to the appropriate healthcare professionals in the network. This process allows healthcare professionals to address high-priority cases faster, improving overall patient care and reducing delays in diagnosis and treatment.
By utilizing AI to automate the triage process, Enlitic’s solutions help reduce the manual burden on clinicians and manage workflows, specifically in radiology. The platform also increases health data quality by standardizing medical imaging data, which ensures that images are correctly labeled and routed.
8. Personalized medications and care
AI enables the development of personalized treatment plans by analyzing individual patient data, including genetic information, lifestyle, and medical history. Personalized medicine helps improve treatment efficacy, reduce side effects, and lower healthcare costs by avoiding unnecessary treatments and focusing on the best outcomes for each patient.
AI in healthcare tools can help users find the best treatment plans based on their patient data, thus reducing cost and increasing the effectiveness of care.
Real-life example: Aitia
The company uses machine learning to match patients with the treatments that are most effective for them.
Real-life example: Oncora Medicals
Oncora can analyze and learn from health systems’ data to enable personalized treatment, specifically for cancer patients.
9. Patient data analytics
Healthcare analytics solutions can derive insights from clinical data to provide healthcare professionals with recommendations for improving patient care, identifying at-risk populations, and optimizing resource allocation. This approach helps reduce care costs while enhancing patient outcomes through more informed decision-making.
Real-life example: Delphi-2M
Delphi-2M is a generative transformer model designed to predict the progression of diseases across an individual’s lifetime. Unlike traditional single-disease models, it captures multimorbidity by analyzing over 1,000 conditions at once. Built on a modified GPT-2 architecture, it encodes age, predicts both the next disease and its timing, and accounts for co-occurring diagnoses. Trained on UK Biobank data and validated on Danish records, it achieved an average AUC of 0.76. It performed well in predicting mortality (AUC 0.97), with accuracy remaining useful up to 10 years.
Beyond forecasting, Delphi-2M can generate long-term disease trajectories and create synthetic datasets that preserve clinical patterns while protecting privacy. Its interpretability methods revealed comorbidity clusters and quantified how earlier conditions shape later risks, such as digestive diseases increasing the risk of pancreatic cancer and subsequent mortality. External validation confirmed generalizability, though training data biases and limited coverage of older populations introduced constraints.
Despite these limitations, Delphi-2M shows potential for precision medicine, early screening, and system-level planning. Anticipating individual risks and projecting disease burdens can inform both patient care and healthcare policy. Future extensions may integrate genomic, imaging, and wearable data to strengthen clinical and public health applications further.
Real-life example: Zakipoint Health
Zakipoint Health provides a comprehensive dashboard designed to give a transparent view of each member’s healthcare risks and costs. This approach enables tailored interventions to improve health outcomes.
The platform leverages predictive analytics to identify cost drivers and risk factors, helping healthcare systems to reduce healthcare risks and achieve cost savings.
10. Surgical robots
Robot-assisted surgeries combine AI and collaborative robots. These tools assist with procedures that require precision and repetition, such as laparoscopic surgery.
These robots can follow predefined movements without fatigue and achieve high precision. This helps reduce the risk of human error, speeds up recovery times, and allows surgeons to perform more complex procedures with high accuracy.
Figure 1: Robotic surgery example.
10. Assistive robotics
Assistive robotics in healthcare enhances patient care and supports medical professionals by performing tasks using sensors, actuators, and intelligent control systems.
Assistive robotics applications include exoskeletons that aid rehabilitation for stroke or spinal injury patients and robotic medication dispensers that ensure accurate dosing. Telepresence robots enable remote consultations, and robotic nursing assistants like Robear help lift or move patients safely.
These technologies improve efficiency, accuracy, and patient outcomes in various clinical settings.
Real-life example: The LUCAS 3
The LUCAS 3 is a mechanical chest compression system developed by Stryker. It delivers consistent, high-quality compressions during cardiopulmonary resuscitation (CPR), helping maintain blood flow in cardiac arrest patients (See the image below).
The device is portable, battery-powered, and designed for use in ambulances, hospitals, or emergency scenes.
It reduces the physical burden on responders and improves CPR outcomes by ensuring uninterrupted compressions, even during transport or defibrillation.
Figure 2: LUCAS 3 chest compression system.
Medical imaging and diagnosis
12. Early diagnosis
AI can analyze medical records, lab data, and imaging results to detect early signs of chronic diseases such as cancer, diabetes, or cardiovascular conditions. Early diagnosis leads to timely interventions, which can improve patient outcomes and reduce long-term treatment costs.
Real-life example: Google Health’
Google Health’s breast cancer screening research indicates that its AI model can detect signs of breast cancer with accuracy similar to that of radiologists.
The system is trained on large numbers of de-identified mammograms to learn patterns associated with cancer and is being evaluated in real clinical settings. Collaborative efforts involve patients, clinicians, and health professionals, as well as partnerships with institutions such as Northwestern Medicine, Imperial College London, several NHS trusts, and the Japanese Foundation for Cancer Research.
These studies examine how the model might help prioritize higher-risk cases, act as a second reader in screening workflows, and support more consistent and inclusive detection across diverse populations.
Real-life example: Ezra
Ezra leverages AI while analyzing full-body MRI scans to support clinicians in the early detection of cancer.
13. Medical imaging insights
AI-driven tools can enhance the analysis of medical images (e.g., X-rays, MRIs, CT scans) by identifying patterns that human radiologists may miss. These insights help in diagnosing diseases earlier and more accurately.
AI is also being used to diagnose COVID-19 from imaging data, enabling quicker identification of critical cases that need ventilator support.
AI-powered medical imaging is also widely used in diagnosing COVID-19 cases and identifying patients who require ventilator support.
Real-life example: Huiying Medical
Huiying Medical, a medical device company located in China, created an AI imaging solution capable of detecting COVID-19 using CT chest scans. According to the company, this solution could benefit areas lacking access to RT-PCR, the standard COVID-19 testing method.
Huiying developed the AI algorithms using CT data from over 4,000 coronavirus cases. The system examines ground-glass opacity (GGO) in the lungs, a sign of partial air space filling, along with other indicators, to assess the likelihood of COVID-19 infection.
Real-life example: SkinVision
SkinVision’s app enables patients to detect early signs of skin cancer by using their smartphones. By allowing users to take high-quality photos of their skin, focusing on suspicious moles or lesions, the apps can analyze them with AI algorithms.
This analysis provides an instant risk assessment, which can help identify potential concerns such as melanoma, squamous cell carcinoma, or basal cell carcinoma.
SkinVision’s algorithms have been trained on a vast database of dermatological images to help distinguish between high-risk and low-risk skin conditions. For high-risk assessments, the app recommends professional medical consultation.
Research and development
14. Drug discovery
AI accelerates drug discovery by analyzing large datasets from medical research, historical treatment data, and biological pathways. This leads to faster identification of promising drug candidates and reduces the cost and time required to bring new drugs to market.
AI technology can also predict the efficacy of drugs, which would lead to better outcomes in clinical trials.
Real-life example: Boltz-ABFE
Boltz-ABFEis an AI method that combines deep learning predictions with free energy simulations. Like AlphaFold, which predicts protein structures, Boltz models predict protein–ligand complexes.
By integrating these AI-based predictions with physics-grounded calculations, Boltz-ABFE extends the scope of FEP to earlier stages of drug discovery, enabling researchers to evaluate candidate molecules more efficiently while maintaining accuracy.
How Boltz-ABFE works
- Uses Boltz-1 and Boltz-2, AI models trained to predict protein–ligand complexes directly from protein sequences and ligand information.
- Applies structure refinement to fix issues such as bond order errors, stereochemistry mistakes, and steric clashes.
- Employs a re-docking process where ligands are adjusted with docking software to improve geometry and accuracy.
- Enhances reliability by removing low-confidence regions and including binding partners when necessary.
Results from benchmarks
- Tested on four proteins (TYK2, CDK2, JNK1, P38) from the FEP+ benchmark set.
- Produced binding energy estimates often within 1 kcal/mol of experimental results.
- In some cases, matched or outperformed simulations that started from crystal structures.
- Showed sensitivity to structural details, making correction steps like re-docking important.
Real-life example: NuMedii
Biopharma company NuMedii has built the AIDD (Artificial Intelligence for Drug Discovery) technology that leverages Big Data and AI to rapidly discover connections between drugs and diseases at a systematic level.
Real-life example: Insilico Medicine
Insilico Medicine, a biotech firm headquartered in Boston and Hong Kong, has announced a milestone in AI-driven drug development.
Their lead compound, rentosertib, designed entirely using artificial intelligence, has demonstrated promising results in a mid-stage clinical trial for idiopathic pulmonary fibrosis (IPF), a progressive and currently incurable lung disease.
In the study, patients receiving the highest dose of rentosertib showed notable improvements in lung function. Biomarker analyses confirmed that the drug effectively targeted a specific protein associated with IPF, as predicted by Insilico’s AI algorithms.
15. Gene analysis and editing
AI assists in analyzing genetic data to understand genetic variations and predict the effects of gene editing.
This technology also helps researchers predict how specific gene edits might impact disease risk or treatment outcomes, enabling more precise and effective genetic therapies.
Real-life example: SOPHiA GENETICS
SOPHiA GENETICS offers geneticists the SOPHiA DDM™ platform, which uses AI to improve genomic analysis. The platform automates the detection, annotation, and prioritization of complex variants in next-generation sequencing (NGS) data, leading to quicker and more accurate insights.
It integrates into existing lab environments, facilitates collaboration through a global expert network, and includes tools like Alamut™ Visual Plus for detailed variant analysis.
The MaxCare Program also offers support with on-site consultations, training, and performance evaluations to ensure successful implementation.
16. Device and drug comparative effectiveness
AI can evaluate and compare the effectiveness of different medical devices or drugs by analyzing clinical outcomes and patient data.
This helps healthcare providers make more informed choices about the most effective treatments while reducing trial-and-error in medical interventions.
Real-life example: 4Quant
4Quant leverages big data analytics and deep learning technology to extract meaningful insights from images and videos, supporting the design and optimization of experiments. Their platform applies machine learning algorithms to process vast amounts of visual data for researchers and healthcare professionals to analyze complex information effectively.
By automating the extraction of actionable insights from imaging data, 4Quant allows users to identify key components and patterns that are most relevant to their specific experimental needs. This can be particularly valuable in fields such as scientific research, medicine, and industrial applications, where analyzing visual data is critical to decision-making.
4Quant’s solutions also offer customization based on specific user requirements for more targeted analyses. This approach reduces the time and effort required to analyze large datasets and improves the precision and quality of the insights.
Healthcare management
17. Brand management and marketing
AI platforms can analyze healthcare market perception and patient demographics to help medical professionals optimize their marketing strategies. Hospitals and healthcare organizations can enhance their brand reputation by tailoring messages and targeting the right segments.
18. Pricing and risk
AI models can predict the optimal pricing for treatments and services by analyzing competition, market demand, and patient outcomes.
This helps healthcare providers set competitive yet profitable prices while reducing patients’ financial burden and optimizing operational margins.
19. Market research
AI can be used to gather competitive intelligence on other hospitals or healthcare providers. This data enables hospitals to benchmark their services, identify areas of improvement, and adapt to changes in the healthcare market landscape.
Real-life example: MD Analytics
MD Analytics is a health and pharmaceutical marketing research solution. The tool offers a wide range of quantitative and qualitative research solutions tailored to each phase of a product’s lifecycle.
Their services cover clinical trials, market assessments, patient journey analysis, and buying process evaluations. Pre-launch solutions include demand forecasting, concept testing, pricing research, and patient support program assessments. Post-launch and growth phases focus on customer engagement, sales force evaluations, multi-channel optimization, and tracking KPIs.
20. Operations
Process automation technologies such as intelligent automation and RPA can manage healthcare operations such as scheduling, billing, and reporting. By automating routine tasks, healthcare providers can free up staff to focus on patient care while reducing administrative costs.
Real-life example: Comet
Comet is Epic’s medical intelligence system, designed to help clinicians, patients, and healthcare systems make better, data-driven decisions by predicting likely outcomes in a patient’s healthcare journey.
Trained on over 100 billion de-identified medical events within Epic Cosmos, it models time-ordered sequences such as diagnoses, lab results, medications, and encounters to simulate possible future scenarios, such as disease progression, readmission risk, or length of hospital stay.
Built on similar to large language models, Comet generates plausible health trajectories and summarizes them into actionable insights integrated into clinical workflows. Unlike traditional tools, it moves care planning from a reactive to an anticipatory approach by presenting a range of possible outcomes, helping teams allocate resources, plan discharges, and manage risks with greater confidence.
Operating under strict privacy and security standards, Comet has demonstrated successful performance across a wide range of evaluated cases. Starting in 2026, researchers in participating organizations will be able to explore Comet in a virtual lab to refine its use cases, marking a step toward more personalized, proactive, and secure healthcare decision-making.
21. Fraud detection
AI tools can analyze patterns in healthcare claims to detect fraudulent activities such as false claims or over-billing. This helps healthcare organizations minimize losses from fraud and ensures that resources are used more efficiently for patient care.
Real-life example: Markovate
A national health insurance provider faced increasing fraudulent claims and data breaches, leading to financial losses and compromised patient privacy.
Markovateimplemented an AI-based fraud detection system that analyzed claims data, flagged suspicious behaviors, and integrated seamlessly with the provider’s infrastructure, ensuring HIPAA compliance and safeguarding sensitive patient data.
The results are:
- 30% reduction in fraudulent claims within six months.
- 25% improvement in data security.
- 40% faster claims processing, boosting efficiency.
Hyperautomation in healthcare
Hyperautomation is an emerging approach to digital transformation that involves automating as many business processes as possible while digitally augmenting those that cannot be fully automated.
Hyperautomation combines AI, RPA, and computer vision technologies for end-to-end process automation in healthcare.
Here are hyperautomation use cases in healthcare:
22. Health insurance processing
By leveraging NLP methods and AI/deep learning models, a hyperautomation approach can help health insurance businesses:
- Minimize manual work during preauthorization and claims processing,
- Reduce human errors,
- Detect and prevent healthcare fraud more accurately,
- Ensure customer satisfaction with shorter claims cycles.
23. Regulatory compliance
Healthcare providers, health insurance companies, pharmacies, and other healthcare entities must comply with regulations such as HIPAA in the US and GDPR in the EU.
Hyperautomation can help ensure regulatory compliance for healthcare organizations:
- Intelligent bots can log every action in healthcare systems and document the activity log when demanded.
- AI/ML models can be used to predict potential healthcare fraud,
- Automating internal audit processes can help evaluate risks and internal controls more efficiently and frequently.
Future of healthcare AI use cases
For the future of AI in healthcare, a machine learning-based solution can be built in areas where significant training data is available, and the problem statement can be formulated clearly.
In these areas, AI can benefit healthcare providers by enabling data-driven decision-making and time & cost savings.
Principal Analyst
Cem Dilmegani
Principal Analyst
Cem’s work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem’s work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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Sıla Ermut
Industry Analyst
Sıla Ermut is an industry analyst at AIMultiple focused on email marketing and sales videos. She previously worked as a recruiter in project management and consulting firms. Sıla holds a Master of Science degree in Social Psychology and a Bachelor of Arts degree in International Relations.
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