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This first article in a series describes the history of artificial intelligence in medicine; the use of AI in image analysis, identification of disease outbreaks, and diagnosis; and the use of chatbots.

The authors address the issues that must be confronted if we are to integrate the use of wearable digital health technologies into clinical care in a way that provides an enduring benefit to patients.

Key Clinical PointsNavigating and Communicating about Serious Illness and End of LifePartnering with patients as they navigate serious illness requires effectively communicating prognostic information while responding to the emotions generated by the conversation.Clinicians should expect, and have the skill, to engage in a continuum of conversations that allow patients to integrate prognostic information cognitively and emotionally.Patients oscillate between expressions of intense hopefulness and more realistic aspirations; this a normal and expected part of the process.Facilitating patient exploration of their hopes and worries allows them to grieve, understand their priorities, and build coping skills for living with a serious illness.As patients integrate prognostic information, clinicians should discuss what is most important to the patient given the likely illness trajectory and incorporate these goals and values into a recommendation about medical care, including care at the end of life.

The editors announce both a series of articles focusing on AI and machine learning in health care and the 2024 launch of a new journal, NEJM AI , a forum for evidence, resource sharing, and discussion of the possibilities and limitations of medical AI.

Burnout and Depression among PhysiciansDiminished well-being among physicians is of growing concern. The authors review measures of burnout and depression, causal factors, and interventions to improve well-being.

Many learners are more facile with the use of large language models in medicine than their supervisors are. The authors provide an approach to clinical supervision that can mitigate the perils and amplify the promise of AI.

Key PointsMedical Artificial Intelligence and Human ValuesAs large language models and other artificial intelligence models are used more in medicine, ethical dilemmas can arise depending on how the model was trained. A user must understand how human decisions and values can shape model outputs. Medical decision analysis offers lessons on measuring human values.A large language model will respond differently depending on the exact way a query is worded and how the model was directed by its makers and users. Caution is advised when considering the use of model output in decision making.

Challenges at the interface of medical statistics and AI are population inference vs. prediction, generalizability, reproducibility and interpretation of evidence, and stability and statistical guarantees.

Advances in AI for Infectious-Disease SurveillanceThis article in the AI in Medicine series addresses the use of AI and machine-learning tools to identify and track disease outbreaks and monitor mitigation strategies.

CAP is an acute lung infection that causes 1.5 million hospitalizations in the United States each year. Most outpatients with mild CAP can be treated empirically without diagnostic testing for bacteria.