AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Physicians

Showing 251 to 260 of 276 articles

Clear Filters

Applying a Common Enterprise Theory of Liability to Clinical AI Systems.

American journal of law & medicine
The advent of artificial intelligence ("AI") holds great potential to improve clinical diagnostics. At the same time, there are important questions of liability for harms arising from the use of this technology. Due to their complexity, opacity, and ...

Objective Task-Based Evaluation of Artificial Intelligence-Based Medical Imaging Methods:: Framework, Strategies, and Role of the Physician.

PET clinics
Artificial intelligence-based methods are showing promise in medical imaging applications. There is substantial interest in clinical translation of these methods, requiring that they be evaluated rigorously. We lay out a framework for objective task-...

Demystifying machine learning: a primer for physicians.

Internal medicine journal
Machine learning is a tool for analysing digitised data sets and formulating predictions that can optimise clinical decision-making. It aims to identify complex patterns in large data sets and encode them into models that can then classify new unseen...

Public vs physician views of liability for artificial intelligence in health care.

Journal of the American Medical Informatics Association : JAMIA
The growing use of artificial intelligence (AI) in health care has raised questions about who should be held liable for medical errors that result from care delivered jointly by physicians and algorithms. In this survey study comparing views of physi...

Machine Learning: The Next Paradigm Shift in Medical Education.

Academic medicine : journal of the Association of American Medical Colleges
Machine learning (ML) algorithms are powerful prediction tools with immense potential in the clinical setting. There are a number of existing clinical tools that use ML, and many more are in development. Physicians are important stakeholders in the h...

Meeting the unmet needs of clinicians from AI systems showcased for cardiology with deep-learning-based ECG analysis.

Proceedings of the National Academy of Sciences of the United States of America
Despite their great promise, artificial intelligence (AI) systems have yet to become ubiquitous in the daily practice of medicine largely due to several crucial unmet needs of healthcare practitioners. These include lack of explanations in clinically...

Technology Can Augment, but Not Replace, Critical Human Skills Needed for Patient Care.

Academic medicine : journal of the Association of American Medical Colleges
The practice of medicine is changing rapidly as a consequence of electronic health record adoption, new technologies for patient care, disruptive innovations that breakdown professional hierarchies, and evolving societal norms. Collectively, these ha...

Artificial Intelligence and Clinical Decision Making: The New Nature of Medical Uncertainty.

Academic medicine : journal of the Association of American Medical Colleges
Estimates in a 1989 study indicated that physicians in the United States were unable to reach a diagnosis that accounted for their patient's symptoms in up to 90% of outpatient patient encounters. Many proponents of artificial intelligence (AI) see t...

Science Without Conscience Is but the Ruin of the Soul: The Ethics of Big Data and Artificial Intelligence in Perioperative Medicine.

Anesthesia and analgesia
Artificial intelligence-driven anesthesiology and perioperative care may just be around the corner. However, its promises of improved safety and patient outcomes can only become a reality if we take the time to examine its technical, ethical, and mor...