Ming‐Yuan Huang
Biographic Data
| ID | 7719418 |
|---|---|
| NAME | Ming‐Yuan Huang |
| GIVEN NAMES | Ming‐Yuan |
| FAMILY NAME | Huang |
| SIGNATURE | HUANG M Y |
| AFFILIATIONS | Ministry of Higher Education |
| ORCID | 0000-0002-1501-9893 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Strengthening novice diagnostic problem solving through explicit metacognitive scaffolding in emergency medicine education
BACKGROUND: Metacognition is central to clinical diagnostic problem solving, yet few instructional models provide actionable guidance for embedding explicit metacognitive support into clinical training. OBJECTIVE: This study aimed to introduce and evaluate the Metacognition-Integrated Instruction for Novice Diagnostic (MIND) learning model, a structured framework that integrates explicit metacognitive prompts into authentic diagnostic tasks, and …
Empowering medical students with AI literacy
The integration of artificial intelligence (AI) in healthcare necessitates AI literacy within medical education.As AI's role in health care expands, understanding algorithm transparency, accountability and bias is crucial.However, incorporating AI education into an already dense curriculum poses challenges.A structured, efficient course covering both technical and ethical aspects of AI is essential to prepare future clinicians for AI-enabled heal…
Exploring medical students’ metacognitive and regulatory dimensions of diagnostic problem solving
Solving clinical problems requires an individual to apply not only domain-specific medical knowledge and cognitive skills for reasoning, but also to be consciously aware of, monitor, and evaluate their thinking processes (i.e., metacognition). The purpose of this study was to map critical metacognitive dimensions of clinical problem solving and to explore the structural relationships among them, which may help frame a conceptual framework and bet…
No prominent works on this page.
Exploring medical students’ metacognitive and regulatory dimensions of diagnostic problem solving
Solving clinical problems requires an individual to apply not only domain-specific medical knowledge and cognitive skills for reasoning, but also to be consciously aware of, monitor, and evaluate their thinking processes (i.e., metacognition). The purpose of this study was to map critical metacognitive dimensions of clinical problem solving and to explore the structural relationships among them, which may help frame a conceptual framework and bet…
Empowering medical students with AI literacy
The integration of artificial intelligence (AI) in healthcare necessitates AI literacy within medical education.As AI's role in health care expands, understanding algorithm transparency, accountability and bias is crucial.However, incorporating AI education into an already dense curriculum poses challenges.A structured, efficient course covering both technical and ethical aspects of AI is essential to prepare future clinicians for AI-enabled heal…
Strengthening novice diagnostic problem solving through explicit metacognitive scaffolding in emergency medicine education
BACKGROUND: Metacognition is central to clinical diagnostic problem solving, yet few instructional models provide actionable guidance for embedding explicit metacognitive support into clinical training. OBJECTIVE: This study aimed to introduce and evaluate the Metacognition-Integrated Instruction for Novice Diagnostic (MIND) learning model, a structured framework that integrates explicit metacognitive prompts into authentic diagnostic tasks, and …
Clinical Reasoning and Diagnostic Skills (2 works) · Cognition (2 works) · Innovations in Medical Education (2 works) · Medical education (2 works) · Medicine (2 works) · Metacognition (2 works) · Psychology (2 works) · Artificial Intelligence in Healthcare and Education (1 work) · Biomedical and Engineering Education (1 work) · Cognitive psychology (1 work)