Artificial intelligence in healthcare refers to the application of artificial intelligence (AI) to medical and healthcare data in areas including disease diagnosis, treatment planning, patient monitoring, drug development, and clinical decision support systems.
The use of AI in healthcare has also raised ethical, technical, and regulatory concerns, including related to issues such as data privacy, automation of jobs, and amplifying already existing algorithmic bias. Studies have also examined concerns among patients, health professionals, and the public about trust and empathy in care involving AI.
Applications in healthcare systems Disease diagnosis Accurate and early diagnosis of diseases is still a challenge in healthcare. Recognizing medical conditions and their symptoms is a complex problem. AI can assist clinicians with data processing capabilities to save time and improve accuracy. Through the use of machine learning, artificial intelligence can substantially aid doctors in patient diagnosis through the analysis of mass electronic health records (EHRs). AI can help early prediction, for example, of Alzheimer's disease and dementias, by looking through large numbers of similar cases and possible treatments.
In 2023 a study reported higher satisfaction rates with ChatGPT-generated responses compared with those from physicians for medical questions posted on Reddit's r/AskDocs. Evaluators preferred ChatGPT's responses to physician responses in 78.6% of 585 evaluations, noting better quality and empathy. The authors noted that these were isolated questions taken from an online forum, not in the context of an established patient-physician relationship. Moreover, responses were not graded on the accuracy of medical information, and some have argued that the experiment was not properly blinded, with the evaluators being coauthors of the study.See 'Limitations' section of Ayers et al. (2023)
Large healthcare-related data warehouses of sometimes hundreds of millions of patients have been used as training data for AI models.
A 2025 meta-analysis in PLOS One found that the use of AI algorithms for detecting tooth decay was clinically justified.
Electronic health records AI algorithms have been created to evaluate individual patients' electronic health records, and to predict risks for diseases based on records and family histories. One general algorithm is a rule-based system that makes decisions similarly to how humans use flow charts. This system takes in large amounts of data and creates a set of rules that connect specific observations to concluded diagnoses. Thus, the algorithm can take in a new patient's data and try to predict the likelihood that they will have a certain condition or disease. Since the algorithms can evaluate a patient's information based on collective data, they can find any outstanding issues to bring to a physician's attention and save time. One study conducted by the Centerstone research institute found that predictive modeling of EHR data has achieved 70–72% accuracy in predicting individualized treatment response. These methods are helpful due to the fact that the amount of online health records doubles every five years. Physicians do not have the bandwidth to process all this data manually, and AI can leverage this data to assist physicians in treating their patients. AI can analyze electronic health records to identify patterns and provide evidence-based insights to support physicians in clinical decision-making.
Clinical documentation The use of generative artificial intelligence tools (a subset of AI) is growing as a support for clinical documentation, primarily for transcribing consultations and drafting medical notes. Early studies have shown that such tools, may assist clinicians with documentation tasks, reducing administrative burdens, which are known drivers of burnout. These tools may also improve accessibility for patients synthesizing complex data, and writing responses in a desired conversational style or literacy level ultimately improving the overall efficiency of healthcare services.
AlphaFold and drug discovery AlphaFold has the ability to predict protein structures based on the constituent amino acid sequence, expected to have benefits in the life sciences—accelerating drug discovery and enabling better understanding of diseases. Nobel laureate Venki Ramakrishnan called the result "a stunning advance on the protein folding problem", adding that "It has occurred decades before many people in the field would have predicted. It will be exciting to see the many ways in which it will fundamentally change biological research." In 2023, Demis Hassabis and John Jumper won the Breakthrough Prize in Life Sciences as well as the Albert Lasker Award for Basic Medical Research for their management of the AlphaFold project. Hassabis and Jumper proceeded to win the Nobel Prize in Chemistry in 2024 for their work on "protein structure prediction" with David Baker of the University of Washington.
Drug interactions Improvements in natural language processing led to the development of algorithms to identify drug-drug interactions in medical literature. Drug-drug interactions pose a threat to those taking multiple medications simultaneously, and the danger increases with the number of medications being taken. To address the difficulty of tracking all known or suspected drug-drug interactions, machine learning algorithms have been created to extract information on interacting drugs and their possible effects from medical literature. Efforts were consolidated in 2013 in the DDIExtraction Challenge, in which a team of researchers at Carlos III University assembled a corpus of literature on drug-drug interactions to form a standardized test for such algorithms. Competitors were tested on their ability to accurately determine, from the text, which drugs were shown to interact and what the characteristics of their interactions were. Researchers continue to use this corpus to standardize the measurement of the effectiveness of their algorithms.
Other algorithms identify drug-drug interactions from patterns in user-generated content, especially electronic health records and/or adverse event reports. Organizations such as the FDA Adverse Event Reporting System (FAERS) and the World Health Organization's VigiBase allow doctors to submit reports of possible negative reactions to medications. Deep learning algorithms have been developed to parse these reports and detect patterns that imply drug-drug interactions.
Telehealth Telehealth is the treatment of patients remotely. Pivovarov and Elhada (2015) propose using wearable devices to allow for constant monitoring of a patient and the ability to notice changes via AI that may be less distinguishable by humans.
A 2025 systematic review and meta-analysis of 15 studies comparing AI chatbots with human healthcare professionals in text-based consultations found that in a large majority of studies participants rated chatbot responses as more empathic than those from clinicians.
Workload management AI has the potential to streamline care coordination and reduce the workload. AI algorithms can automate administrative tasks, prioritize patient needs, and facilitate seamless communication in a healthcare team.
Applications in treatment Cardiovascular Artificial intelligence algorithms have shown promising results in accurately diagnosing and risk stratifying patients with concern for coronary artery disease, showing potential as an initial triage tool. Other algorithms have been used in predicting patient mortality, medication effects, and adverse events following treatment for acute coronary syndrome. Wearables, smartphones, and internet-based technologies have also shown the ability to monitor patients' cardiac data points, expanding the amount of data and the various settings AI models can use and potentially enabling earlier detection of cardiac events occurring outside of the hospital. A study in 2019 found that AI can be used to predict heart attack with up to 90% accuracy. Another growing area of research is the utility of AI in classifying heart sounds and diagnosing valvular disease. Challenges of AI in cardiovascular medicine have included the limited data available to train machine learning models, such as limited data on social determinants of health as they pertain to cardiovascular disease.
A key limitation in early studies evaluating AI were omissions of data comparing algorithmic performance to humans. Examples of studies which assess AI performance relative to physicians includes how AI is non-inferior to humans in interpretation of cardiac echocardiograms and that AI can diagnose heart attack better than human physicians in the emergency setting, reducing both low-value testing and missed diagnoses.
Dermatology Applications of artificial intelligence in dermatology include skin cancer detection, the assessment of skin conditions, and aesthetic applications.
AI models have been developed and evaluated for detecting and classifying skin cancer in clinical and dermoscopic images. A 2024 systematic review and meta-analysis found that AI models performed comparably to expert dermatologists in skin cancer classification, but called for further evaluation in real-world clinical settings. Another review found that clinicians had higher pooled sensitivity and specificity when using AI assistance, although most included studies were conducted in experimental settings. Limited dataset diversity, including the underrepresentation of darker skin tones, may also reduce generalizability across populations.
Deep learning has also shown potential for diagnosing and monitoring non-cancerous skin conditions, including acne, rosacea, eczema, and psoriasis, although its effectiveness in clinical practice remains uncertain. One algorithm, developed to identify rosacea from facial images, achieved accuracy rates of 88–90% in initial testing, though researchers noted limitations in identifying symptom types and affected areas in detail.
In aesthetic dermatology, AI has been applied to skin assessment, treatment planning, and the evaluation of cosmetic outcomes. AI has also been used to diagnose inflammatory skin conditions such as rosacea, where an AI tool was reported to achieve accuracy rates of approximately 88–90% in identifying the disorder.
It has also been suggested that AI could be used to automatically evaluate the outcome of maxillo-facial surgery or cleft palate therapy in regard to facial attractiveness or age appearance.
Gastroenterology AI can play a role in various facets of the field of gastroenterology. Endoscopic exams such as esophagogastroduodenoscopies (EGD) and colonoscopies rely on rapid detection of abnormal tissue. By enhancing these endoscopic procedures with AI, clinicians can more rapidly identify diseases, determine their severity, and visualize blind spots. Early trials in using AI detection systems of early stomach cancer have shown sensitivity close to expert endoscopists.
AI can assist doctors treating ulcerative colitis in detecting the microscopic activity of the disease in people and predicting when flare-ups will happen. For example, an AI-powered tool was developed to analyse digitised bowel samples (biopsies). The tool was able to distinguish with 80% accuracy between samples that show remission of colitis and those with active disease. It also predicted the risk of a flare-up happening with the same accuracy. These rates of successfully using microscopic disease activity to predict disease flare are similar to the accuracy of pathologists.
Infectious diseases AI has shown potential in both the laboratory and clinical spheres of infectious disease medicine. There were only a few examples of AI being used directly in clinical practice during the COVID-19 pandemic.