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. By leveraging Machine learning and Deep learning techniques, AI systems can analyze vast amounts of clinical information, identify patterns, and assist clinicians in making more accurate and timely decisions. The integration of AI into healthcare has the potential to improve patient outcomes, reduce administrative burdens, and accelerate medical research, but it also introduces significant ethical, technical, and regulatory challenges that must be addressed.
The use of AI in healthcare has raised concerns about data privacy, the automation of certain clinical jobs, and the amplification of existing algorithmic biases. Studies have examined how patients, health professionals, and the public perceive trust and empathy in care that involves AI, with many emphasizing the need for transparency and human oversight. As AI tools become more prevalent, healthcare systems must balance innovation with safeguards to ensure equitable and safe implementation.
Disease Diagnosis
Accurate and early diagnosis of diseases remains a challenge in healthcare. Recognizing medical conditions and their symptoms is a complex problem that often requires synthesizing information from multiple sources. AI can assist clinicians with data processing capabilities to save time and improve accuracy. Through the use of machine learning, AI can substantially aid doctors in patient diagnosis by analyzing mass electronic health records (EHRs). For example, AI can help in the early prediction of Alzheimer's disease and dementias by examining large numbers of similar cases and potential 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, citing 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 argued that the experiment was not properly blinded, with the evaluators being coauthors of the study.
Large healthcare-related data warehouses, sometimes containing 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, demonstrating the growing evidence base for AI-assisted diagnostics.
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 approach 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 these algorithms can evaluate a patient's information based on collective data, they can identify 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 achieved 70–72% accuracy in predicting individualized treatment response. These methods are helpful because 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 AI 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 that are known drivers of burnout. These tools may also improve accessibility for patients by 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, developed by Google DeepMind, has the ability to predict protein structures based on the constituent amino acid sequence, which is 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, alongside 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 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) proposed 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, AI chatbots performed comparably or better in terms of patient satisfaction and clinical accuracy, though the authors emphasized the need for further research on safety and long-term outcomes.
Ethical and Regulatory Considerations
The deployment of AI in healthcare raises significant ethical and regulatory issues. Data privacy is a primary concern, as AI systems often require access to sensitive patient information. The automation of certain tasks may lead to job displacement among healthcare workers, and algorithmic bias can perpetuate or worsen existing health disparities. Regulatory bodies are working to establish frameworks that ensure AI tools are safe, effective, and equitable. For example, the U.S. Food and Drug Administration has issued guidance on the use of AI in medical devices, and the European Union's proposed AI Act includes specific provisions for high-risk applications in healthcare.
Future Directions
As AI continues to evolve, its integration into healthcare is expected to expand. Advances in Large language models and Transformer (architecture) architectures are enabling more sophisticated clinical decision support and patient interaction. However, the success of these technologies will depend on addressing the challenges of data quality, interpretability, and trust. Collaboration between AI researchers, clinicians, and policymakers will be essential to realize the full potential of AI in improving global health outcomes.