Artificial intelligence (AI) is gradually changing medical practice. With recent progress in digitized data acquisition, machine learning and computing infrastructure, AI applications are expanding into areas that were previously thought to be only the province of human experts. In this Review Article, we outline recent breakthroughs in AI technologies and their biomedical applications, identify the challenges for further progress in medical AI systems, and summarize the economic, legal and social implications of AI in healthcare.

Artificial Intelligence in Healthcare

Clinical judgment is a critical aspect of physician performance in medicine. It is essential in the formulation of a diagnosis and key to the effective and safe management of patients. Yet, the overall diagnostic error rate remains unacceptably high. In more than four decades of research, a variety of approaches have been taken, but a consensus approach toward diagnostic decision making has not emerged. In the last 20 years, important gains have been made in psychological research on human judgment. Dual-process theory has emerged as the predominant approach, positing two systems of decision making, System 1 (heuristic, intuitive) and System 2 (systematic, analytical). The author proposes a schematic model that uses the theory to develop a universal approach toward clinical decision making. Properties of the model explain many of the observed characteristics of physicians' performance. Yet the author cautions that not all medical reasoning and decision making falls neatly into one or the other of the model's systems, even though they provide a basic framework incorporating the recognized diverse approaches. He also emphasizes the complexity of decision making in actual clinical situations and the urgent need for more research to help clinicians gain additional insight and understanding regarding their decision making.

A universal model of diagnostic reasoning.

If you ask people what the phrase “computers in medicine” means, they often describe a computer program that helps physicians to make diagnoses. Although computers play numerous important medical roles, from the earliest days of computing people have recognized that computers might support physicians by helping these people to sift through the vast collection of possible diseases and symptoms. This idea has been echoed in futuristic works of science fiction. In Star Trek, for example, medical workers routinely point devices at injured crew members to determine instantly what is the problem and how serious is the damage. The prevalence of such expectations, coupled with a general societal concern about the influence of computers on interpersonal relationships and on job security, has naturally raised questions among health workers. Just what can computers do today to support clinical decision-making? How soon will diagnostic tools be generally available? How good will they be? What will their effects be on the practice of medicine, on medical education, and on relationships among colleagues or between physicians and patients?

Clinical decision-support systems

From healthcare to criminal justice, artificial intelligence (AI) is increasingly supporting high-consequence human decisions. This has spurred the field of explainable AI (XAI). This paper seeks to strengthen empirical application-specific investigations of XAI by exploring theoretical underpinnings of human decision making, drawing from the fields of philosophy and psychology. In this paper, we propose a conceptual framework for building human-centered, decision-theory-driven XAI based on an extensive review across these fields. Drawing on this framework, we identify pathways along which human cognitive patterns drives needs for building XAI and how XAI can mitigate common cognitive biases. We then put this framework into practice by designing and implementing an explainable clinical diagnostic tool for intensive care phenotyping and conducting a co-design exercise with clinicians. Thereafter, we draw insights into how this framework bridges algorithm-generated explanations and human decision-making theories. Finally, we discuss implications for XAI design and development.

Designing Theory-Driven User-Centric Explainable AI

The differential diagnostic process attempts to identify candidate diseases that best explain a set of clinical features. This process can be complicated by the fact that the features can have varying degrees of specificity, as well as by the presence of features unrelated to the disease itself. Depending on the experience of the physician and the availability of laboratory tests, clinical abnormalities may be described in greater or lesser detail. We have adapted semantic similarity metrics to measure phenotypic similarity between queries and hereditary diseases annotated with the use of the Human Phenotype Ontology (HPO) and have developed a statistical model to assign p values to the resulting similarity scores, which can be used to rank the candidate diseases. We show that our approach outperforms simpler term-matching approaches that do not take the semantic interrelationships between terms into account. The advantage of our approach was greater for queries containing phenotypic noise or imprecise clinical descriptions. The semantic network defined by the HPO can be used to refine the differential diagnosis by suggesting clinical features that, if present, best differentiate among the candidate diagnoses. Thus, semantic similarity searches in ontologies represent a useful way of harnessing the semantic structure of human phenotypic abnormalities to help with the differential diagnosis. We have implemented our methods in a freely available web application for the field of human Mendelian disorders.

Clinical Diagnostics in Human Genetics with Semantic Similarity Searches in Ontologies

DXplain is an evolving computer-based diagnostic decision-support system designed for use by the physician who has no computer expertise. DXplain accepts a list of clinical manifestations and then proposes diagnostic hypotheses. The program explains and justifies its interpretations and provides access to a knowledge base concerning the differential diagnosis of the signs and symptoms. DXplain was developed with the support and cooperation of the American Medical Association. The system is distributed to the medical community through AMA/NET—a nationwide computer communications network sponsored by the American Medical Association—and through the Massachusetts General Hospital Continuing Education Network. A key element in the distribution of DXplain is the planned collaboration with its physician-users whose comments, criticisms, and suggestions will play an important role in modifying and enhancing the knowledge base. ( JAMA 1987;258:67-74)

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DXplain: An Evolving Diagnostic Decision-Support System

DXplain is a program which provides access to a medical diagnosis knowledge base via a nation-wide computer communications network This report describes the methods used in the knowledge acquisition phase of development and the initial evaluations of the program

DXplain: Experience with Knowledge Acquisition and Program Evaluation

MUMPS--an evolutionary commentary.

This paper describes the Medical Query Language (MQL), a “formal” language which enables unsophisticated users, having no background in programming or computer science, to express information retrieval and analysis questions of their data bases. MQL is designed to access any MUMPS data base. Most MQL applications to date have dealt with the COmputer STored Ambulatory Record (COSTAR) data base.

Medical Query Language.

A heavy reliance on the use of narrative text to record information in the COSTAR (COmPuter STored Pmbulatory Record) medical record has made automated analysis of the data difficult. Front-end microprocessors and new data capturing mechanisms have made it possible to develop methods which facilitate entry of medical data in codable form. A prototype of such a system developed at the Laboratory of Computer Science is described here.

J.A. Hupp

Papers

DXplain: An Evolving Diagnostic Decision-Support System

DXplain: Experience with Knowledge Acquisition and Program Evaluation

MUMPS--an evolutionary commentary.

Medical Query Language.

The capturing of more detailed medical information in costar