AI Medical Compendium Topic:
Clinical Competence

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Applications of interpretability in deep learning models for ophthalmology.

Current opinion in ophthalmology
PURPOSE OF REVIEW: In this article, we introduce the concept of model interpretability, review its applications in deep learning models for clinical ophthalmology, and discuss its role in the integration of artificial intelligence in healthcare.

Surgical data science and artificial intelligence for surgical education.

Journal of surgical oncology
Surgical data science (SDS) aims to improve the quality of interventional healthcare and its value through the capture, organization, analysis, and modeling of procedural data. As data capture has increased and artificial intelligence (AI) has advanc...

From Utopia Through Dystopia: Charting a Course for Learning Analytics in Competency-Based Medical Education.

Academic medicine : journal of the Association of American Medical Colleges
The transition to the assessment of entrustable professional activities as part of competency-based medical education (CBME) has substantially increased the number of assessments completed on each trainee. Many CBME programs are having difficulty syn...

AI-ssessment: Towards Assessment As a Sociotechnical System for Learning.

Academic medicine : journal of the Association of American Medical Colleges
Two decades ago, the advent of competency-based medical education (CBME) marked a paradigm shift in assessment. Now, medical education is on the cusp of another transformation driven by advances in the field of artificial intelligence (AI). In this a...

How the use of the artificial intelligence could improve surgical skills in urology: state of the art and future perspectives.

Current opinion in urology
PURPOSE OF REVIEW: As technology advances, surgical training has evolved in parallel over the previous decade. Training is commonly seen as a way to prepare surgeons for their day-to-day work; however, more importantly, it allows for certification of...

Performance and educational training of radiographers in lung nodule or mass detection: Retrospective comparison with different deep learning algorithms.

Medicine
The aim of this investigation was to compare the diagnostic performance of radiographers and deep learning algorithms in pulmonary nodule/mass detection on chest radiograph.A test set of 100 chest radiographs containing 53 cases with no pathology (no...

Robotic-Assisted Surgery Training (RAST) Program: An Educational Research Protocol.

Surgical technology international
Technology has had a dramatic impact on how diseases are diagnosed and treated. Although cut, sew, and tie remain the staples of surgical craft, new technical skills are required. While there is no replacement for live operative experience, training ...

The Importance of Incorporating Human Factors in the Design and Implementation of Artificial Intelligence for Skin Cancer Diagnosis in the Real World.

American journal of clinical dermatology
Artificial intelligence (AI) algorithms have been shown to diagnose skin lesions with impressive accuracy in experimental settings. The majority of the literature to date has compared AI and dermatologists as opponents in skin cancer diagnosis. Howev...

Using Machine Learning to Evaluate Attending Feedback on Resident Performance.

Anesthesia and analgesia
BACKGROUND: High-quality and high-utility feedback allows for the development of improvement plans for trainees. The current manual assessment of the quality of this feedback is time consuming and subjective. We propose the use of machine learning to...

From the Dexterous Surgical Skill to the Battlefield-A Robotics Exploratory Study.

Military medicine
INTRODUCTION: Short response time is critical for future military medical operations in austere settings or remote areas. Such effective patient care at the point of injury can greatly benefit from the integration of semi-autonomous robotic systems. ...