Medically Reviewed: Dr Hanif Chattur Image Credit: Canva Thomas Davenport, president’s distinguished professor of information technology and managementA and Ravi Kalakota, managing directorB Author information Copyright and License information PMC Disclaimer Key Takeaways Improved Diagnosis and Treatment: AI can analyze medical images, detect diseases earlier, and recommend personalized treatment plans. Drug Discovery and Development: AI can accelerate drug discovery, reduce costs, and increase success rates. Personalized Medicine: AI can analyze patient data to tailor treatments and prevent diseases. Enhanced Patient Care: AI-powered virtual assistants can provide 24/7 support, monitor patient health, and improve patient engagement. Operational Efficiency: AI can streamline administrative tasks, reduce paperwork, and optimize resource allocation. Introduction Artificial intelligence (AI) and related technologies are increasingly prevalent in business and society, and are beginning to be applied to healthcare. These technologies have the potential to transform many aspects of patient care, as well as administrative processes within provider, payer and pharmaceutical organisations. There are already a number of research studies suggesting that AI can perform as well as or better than humans at key healthcare tasks, such as diagnosing disease. Today, algorithms are already outperforming radiologists at spotting malignant tumours, and guiding researchers in how to construct cohorts for costly clinical trials. However, for a variety of reasons, we believe that it will be many years before AI replaces humans for broad medical process domains. In this article, we describe both the potential that AI offers to automate aspects of care and some of the barriers to rapid implementation of AI in healthcare. Types of AI relevance to healthcare Artificial intelligence is not one technology, but rather a collection of them. Most of these technologies have immediate relevance to the healthcare field, but the specific processes and tasks they support vary widely. Some particular AI technologies of high importance to healthcare are defined and described below. Machine learning – neural networks and deep learning Machine learning is a statistical technique for fitting models to data and to ‘learn’ by training models with data. Machine learning is one of the most common forms of AI; in a 2018 Deloitte survey of 1,100 US managers whose organizations were already pursuing AI, 63% of companies surveyed were employing machine learning in their businesses.1 It is a broad technique at the core of many approaches to AI and there are many versions of it. In healthcare, the most common application of traditional machine learning is precision medicine – predicting what treatment protocols are likely to succeed on a patient based on various patient attributes and the treatment context. The great majority of machine learning and precision medicine applications require a training dataset for which the outcome variable (e.g onset of disease) is known; this is called supervised learning. A more complex form of machine learning is the neural network – a technology that has been available since the 1960s has been well established in healthcare research for several decades and has been used for categorization applications like determining whether a patient will acquire a particular disease. It views problems in terms of inputs, outputs and weights of variables or ‘features’ that associate inputs with outputs. It has been likened to the way that neurons process signals, but the analogy to the brain’s function is relatively weak. The most complex forms of machine learning involve deep learning, or neural network models with many levels of features or variables that predict outcomes. There may be thousands of hidden features in such models, which are uncovered by the faster processing of today’s graphics processing units and cloud architectures. A common application of deep learning in healthcare is recognition of potentially cancerous lesions in radiology images. Deep learning is increasingly being applied to radiomics, or the detection of clinically relevant features in imaging data beyond what can be perceived by the human eye. Both radiomics and deep learning are most commonly found in oncology-oriented image analysis. Their combination appears to promise greater accuracy in diagnosis than the previous generation of automated tools for image analysis, known as computer-aided detection or CAD. Deep learning is also increasingly used for speech recognition and, as such, is a form of natural language processing (NLP), described below. Unlike earlier forms of statistical analysis, each feature in a deep learning model typically has little meaning to a human observer. As a result, the explanation of the model’s outcomes may be very difficult or impossible to interpret. Natural language processing Making sense of human language has been a goal of AI researchers since the 1950s. This field, NLP, includes applications such as speech recognition, text analysis, translation and other goals related to language. There are two basic approaches to it: statistical and semantic NLP. Statistical NLP is based on machine learning (deep learning neural networks in particular) and has contributed to a recent increase in accuracy of recognition. It requires a large ‘corpus’ or body of language from which to learn. In healthcare, the dominant applications of NLP involve the creation, understanding and classification of clinical documentation and published research. NLP systems can analyse unstructured clinical notes on patients, prepare reports (eg on radiology examinations), transcribe patient interactions and conduct conversational AI. Rule-based expert systems Expert systems based on collections of ‘if-then’ rules were the dominant technology for AI in the 1980s and were widely used commercially in that and later periods. In healthcare, they were widely employed for ‘clinical decision support’ purposes over the last couple of decades and are still in wide use today. Many electronic health record (EHR) providers furnish a set of rules with their systems today. Expert systems require human experts and knowledge engineers to construct a series of rules in a particular knowledge domain. They work well up to a point and are easy to understand. However, when the number of rules is large (usually over several thousand) and the rules begin to conflict with each other, they tend to break down. Moreover, if the knowledge domain changes, changing the rules can be difficult and time-consuming. They are slowly being replaced in healthcare by more approaches based on data and machine learning algorithms. Physical robots Physical robots are well known by this