Artificial intelligence in healthcare

Artificial intelligence in healthcare is the application of artificial intelligence (AI) to analyze and understand complex medical and healthcare data. In some cases, it can exceed or augment human capabilities by providing better or faster ways to diagnose, treat, or prevent disease. As the widespread use of artificial intelligence in healthcare is still relatively new, research is ongoing into its applications across various medical subdisciplines and related industries. AI programs are being applied to practices such as diagnostics, treatment protocol development, drug development, personalized medicine, and patient monitoring and care. Since radiographs are the most commonly performed imaging tests in radiology, the potential for AI to assist with triage and interpretation of radiographs is particularly significant. Using AI in healthcare presents unprecedented ethical concerns related to issues such as data privacy, automation of jobs, and amplifying already existing algorithmic bias. New technologies such as AI are often met with resistance by healthcare leaders, leading to slow and erratic adoption. There have been cases where AI has been put to use in healthcare without proper testing. A systematic review and thematic analysis in 2023 showed that most stakeholders including health professionals, patients, and the general public doubted that care involving AI could be empathetic. Meta-studies have found that the scientific literature on AI in healthcare often suffers from a lack of reproducibility.

== 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 its data processing capabilities to save time and improve accuracy. Through the use of machine learning, artificial intelligence can be able to 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. Large healthcare-related data warehouses of sometimes hundreds of millions of patients has 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 === Electronic health records (EHR) are crucial to the digitalization and information spread of the healthcare industry. Now that around 80% of medical practices use EHR, some anticipate the use of artificial intelligence to interpret the records and provide new information to physicians. One application uses natural language processing (NLP) to make more succinct reports that limit the variation between medical terms by matching similar medical terms. For example, the term heart attack and myocardial infarction mean the same things, but physicians may use one over the other based on personal preferences. NLP algorithms consolidate these differences so that larger datasets can be analyzed. Another use of NLP identifies phrases that are redundant due to repetition in a physician's notes and keeps the relevant information to make it easier to read. Other applications use concept processing to analyze the information entered by the current patient's doctor to present similar cases and help the physician remember to include all relevant details. Beyond making content edits to an EHR, there are AI algorithms that evaluate an individual patient's record and predict a risk for a disease based on their previous information and family history. 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.

=== 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.

=== Telemedicine === The increase of telemedicine, the treatment of patients remotely, has shown the rise of possible AI applications. AI can assist in caring for patients remotely by monitoring their information through sensors. A wearable device may allow for constant monitoring of a patient and the ability to notice changes that may be less distinguishable by humans. The information can be compared to other data that has already been collected using artificial intelligence algorithms that alert physicians if there are any issues to be aware of. 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. Another application of artificial intelligence is chat-bot therapy. Some researchers charge that the reliance on chatbots for mental healthcare does not offer the reciprocity and accountability of care that should exist in the relationship between the consumer of mental healthcare and the care provider (be it a chat-bot or psychologist), though. Some examples of these chatbots include Woebot, Earkick and Wysa. Since the average age has risen due to a longer life expectancy, artificial intelligence could be useful in helping take care of older populations. Tools such as environment and personal sensors can identify a person's regular activities and alert a caretaker if a behavior or a measured vital is abnormal. Although the technology is useful, there are also discussions about limitations of monitoring in order to respect a person's privacy since there are technologies that are designed to map out home layouts and detect human interactions.

=== 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. This enables healthcare providers to focus more on direct patient care and ensures the efficient and coordinated delivery of healthcare services.

== Clinical applications ==

=== 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 research 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. In cardiovascular tissue engineering and organoid studies, AI is increasingly used to analyze microscopy images, and integrate electrophysiological read outs.

=== Dermatology === Medical imaging (such as X-ray and photography) is a commonly used tool in dermatology and the development of deep learning has been strongly tied to image processing.

Han et al. showed keratinocytic skin cancer detection from face photographs. Esteva et al. demonstrated dermatologist-level classification of skin cancer from lesion images. Noyan et al. demonstrated a convolutional neural network that achieved 94% accuracy at identifying skin cells from microscopic Tzanck smear images. Concerns have been raised, however, regarding the limited diversity of datasets, particularly the underrepresentation of darker skin tones, which may reduce generalizability across populations. In addition to skin cancer detection and analysis of tissue samples of histological smears, AI has been applied to chronic and aesthetic dermatology. Consumer-facing platforms have extended these methods into digital health, offering remote evaluation and personalized treatment. For example, MDalgorithms has developed mobile applications such as MDacne, which uses a proprietary database of nearly one million acne images to grade acne severity from smartphone selfies and generate customized treatment regimens. AI has also been applied to inflammatory skin conditions such as rosacea, where an AI-powered diagnostic tool was reported to achieve accuracy rates of approximately 88–90% in identifying the disorder, highlighting the potential for automated systems to support clinical assessment. Similarly, MDhair applies AI analysis to scalp photographs to personalize hair loss treatments, with clinical trials reporting reductions in shedding, increased density, and improved scalp hydration. A large study involving over one million individuals, described by Dermatology Times, further emphasized the value of AI-based systems in collecting extensive demographic and clinical data on skin and hair health, enabling the identification of population-level trends that may inform both research and patient care. This illustrate the broader applications of AI beyond diagnosis, including treatment personalization, remote monitoring, and enhanced access to dermatologic care. Nevertheless, independent reviews have emphasized that many studies rely on context-free images rather than full clinical examinations, and performance comparisons often fail to distinguish between trainees and board-certified dermatologists. According to some researchers, AI algorithms have been shown to be more effective than dermatologists at identifying cancer. However, a 2021 review article found that a majority of papers analyzing the performance of AI algorithms designed for skin cancer classification failed to use external test sets. Only four research studies were found in which the AI algorithms were tested on clinics, regions, or populations distinct from those it was trained on, and in each of those four studies, the performance of dermatologists was found to be on par with that of the algorithm. Moreover, only one study was set in the context of a full clinical examination; others were based on interaction through web-apps or online questionnaires, with most based entirely on context-free images of lesions. In this study, it was found that dermatologists significantly outperformed the algorithms. Many articles claiming superior performance of AI algorithms also fail to distinguish between trainees and board-certified dermatologists in their analyses. 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. During the COVID-19 pandemic, AI has been used for early detection, tracking virus spread and analysing virus behaviour, among other things. However, there were only a few examples of AI being used directly in clinical practice during the pandemic itself. Other applications of AI around infectious diseases include support-vector machines identifying antimicrobial resistance, machine learning analysis of blood smears to detect malaria, and improved point-of-care testing of Lyme disease based on antigen detection. Additionally, AI has been investigated for improving diagnosis of meningitis, sepsis, and tuberculosis, as well as predicting treatment complications in hepatitis B and hepatitis C patients.

=== Musculoskeletal === AI has been used to identify causes of knee pain that doctors miss, that disproportionately affect Black patients. Underserved populations experience higher levels of pain. These disparities persist even after controlling for the objective severity of diseases like osteoarthritis, as graded by human physicians using medical images, raising the possibility that underserved patients' pain stems from factors external to the knee, such as stress. Researchers have conducted a study using a machine-learning algorithm to show that standard radiographic measures of severity overlook objective but undiagnosed features that disproportionately affect diagnosis and management of underserved populations with knee pain. They proposed that new algorithmic measure ALG-P could potentially enable expanded access to treatments for underserved patients.

=== Neurology === The use of AI technologies has been explored for use in the diagnosis and prognosis of Alzheimer's disease (AD). For diagnostic purposes, machine learning models have been developed that rely on structural MRI inputs. The input datasets for these models are drawn from databases such as the Alzheimer's Disease Neuroimaging Initiative. Researchers have developed models that rely on convolutional neural networks with the aim of improving early diagnostic accuracy. Generative adversarial networks are a form of deep learning that have also performed well in diagnosing AD. There have also been efforts to develop machine learning models into forecasting tools that can predict the prognosis of patients with AD. Forecasting patient outcomes through generative models has been proposed by researchers as a means of synthesizing training and validation sets. They suggest that generated patient forecasts could be used to provide future models larger training datasets than current open access databases.

=== Oncology === AI has been explored for use in cancer diagnosis, risk stratification, molecular characterization of tumors, and cancer drug discovery. A particular challenge in oncologic care that AI is being developed to address is the ability to accurately predict which treatment protocols will be best suited for each patient based on their individual genetic, molecular, and tumor-based characteristics. AI has been trialed in cancer diagnostics with the reading of imaging studies and pathology slides. In January 2020, Google DeepMind announced an algorithm capable of surpassing human experts in breast cancer detection in screening scans. A number of researchers, including Trevor Hastie, Joelle Pineau, and Robert Tibshirani among others, published a reply claiming that DeepMind's research publication ...

(Article truncated for display)

Source

This content is sourced from Wikipedia, the free encyclopedia. Read full article on Wikipedia

Category

Artificial Intelligence - Computer Science