Minyang Chow
Biographic Data
| ID | 8648333 |
|---|---|
| NAME | Minyang Chow |
| GIVEN NAMES | Minyang |
| FAMILY NAME | Chow |
| SIGNATURE | CHOW M |
| AFFILIATIONS | Group Clinical Education National Healthcare Group Singapore |
| ORCID | 0009-0004-9398-7656 |
| VERIFIED | Yes |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 5 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2025 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Accurate contextual reporting amidst dynamic AI curricula development
We read Zainal and colleague's article on a digital-age clinical artificial intelligence (AI) ethics competence framework with interest.1 We agree with their central argument—AI ethics must be addressed explicitly and practice-grounded in medical training. However, the paper's reporting of contextual information raises concerns of general importance. Table 1 describes the context of AI and ethics integration in Singapore's three medical schools. …
Coping with generative AI's (GenAI) perpetuation of epistemic uncertainties
Data sharing not applicable to this article as no datasets were generated or analysed during the current study
Empowering educators to create AI‐based pedagogical apps
When I say … productive struggle
In contemporary medical education, our aim is to cultivate adaptive expertise, understood as the capacity to combine efficient routine performance with flexible, innovative responses under clinical uncertainty.1, 2 ‘Productive struggle’ has emerged as one route to this goal,3 yet the term is often used ambiguously. In high-workload cultures, the phrase risks becoming a rhetorical shield normalising unexamined difficulty. We therefore clarify ‘pro…
From technology adopters to creators
Integrating theoretical knowledge with the practical skills essential for clinical practice remains a significant challenge in clinical education. Conventional teaching strategies often fall short in preparing clinicians to navigate the unpredictable, urgent, and multifaceted nature of clinical decision-making, while also providing limited support for the development of cognitive heuristics essential to forming independent clinical judgment. To a…
No prominent works on this page.
From technology adopters to creators
Integrating theoretical knowledge with the practical skills essential for clinical practice remains a significant challenge in clinical education. Conventional teaching strategies often fall short in preparing clinicians to navigate the unpredictable, urgent, and multifaceted nature of clinical decision-making, while also providing limited support for the development of cognitive heuristics essential to forming independent clinical judgment. To a…
Accurate contextual reporting amidst dynamic AI curricula development
We read Zainal and colleague's article on a digital-age clinical artificial intelligence (AI) ethics competence framework with interest.1 We agree with their central argument—AI ethics must be addressed explicitly and practice-grounded in medical training. However, the paper's reporting of contextual information raises concerns of general importance. Table 1 describes the context of AI and ethics integration in Singapore's three medical schools. …
Coping with generative AI's (GenAI) perpetuation of epistemic uncertainties
Data sharing not applicable to this article as no datasets were generated or analysed during the current study
Empowering educators to create AI‐based pedagogical apps
When I say … productive struggle
In contemporary medical education, our aim is to cultivate adaptive expertise, understood as the capacity to combine efficient routine performance with flexible, innovative responses under clinical uncertainty.1, 2 ‘Productive struggle’ has emerged as one route to this goal,3 yet the term is often used ambiguously. In high-workload cultures, the phrase risks becoming a rhetorical shield normalising unexamined difficulty. We therefore clarify ‘pro…
Clinical Reasoning and Diagnostic Skills (3 works) · Curriculum (2 works) · Explainable Artificial Intelligence (XAI (2 works) · Innovations in Medical Education (2 works) · MEDLINE (2 works) · Artificial Intelligence (1 work) · Artificial Intelligence (1 work) · Artificial Intelligence in Healthcare and Education (1 work) · Coding (social sciences) (1 work) · Computer Science (1 work)