Wilson Goh

Abstract Regulatory frameworks ensure the trustworthiness of artificial intelligence in medicine (AI-MD). Yet, developers’ perspectives on these frameworks remain underexplored. We surveyed 122 AI-MD developers online, examining their awareness, familiarity, and adoption of regulatory frameworks, alongside their views on ethical principles and stakeholder responsibilities. About half (57.4%, n = 70) were aware of any frameworks while reporting moderate familiarity.

Conflict of interest: None. Ng, I.K.S., Goh, W.G.W., Thong, C., Leung, A.J. and Teo, K.S.H. (2026), Workplace-based assessments: common pitfalls and mitigating strategies. Intern Med J. https://doi.org/10.1111/imj.70398 Data availability statement Data sharing is not applicable to this article as no datasets were generated or analysed during the current study. References 1, . Workplace-based assessment: a review of user perceptions and strategies to address the identified shortcomings.

‘Quiet quitting’ has become a popularised concept since the recent COVID-19 pandemic, originally coined by corporate recruiter Bryan Creely in a viral Tiktok video.1 It is a highly prevalent phenomenon, with a recent Gallup survey reporting >50% of quiet quitters among employed workers in the United States.2 Unlike former ‘hustle cultures’3 or the ‘996’ Chinese work culture (9 a.m. to 9 p.m. work shifts, 6-day work week),4 which are models that prioritise work over personal life, ‘quiet...