Features, expert commentary, and syndicated coverage spanning clinical AI safety, injury surveillance, global cancer burden, infodemiology, and health equity – from the BBC and Scientific American to Clinical Leader, JMIR, and Medscape. A body of work that earns media attention because it produces findings that matter outside academia.
Expert commentary on the systemic gap between one-time AI validation and the continuous monitoring infrastructure that clinical research sites and trial sponsors actually need. The piece addresses how AI tools drift after deployment – when patient populations shift, data pipelines change, or model inputs evolve – and why point-in-time validation is insufficient for tools embedded in ongoing clinical decision-making. Draws on pharmacovigilance frameworks and real-world AI deployment experience to propose an embedded safety monitoring approach analogous to post-market drug surveillance.
Read full feature →National emergency department data showing a threefold increase in e-scooter and e-bike injuries, with alcohol involvement and teenage riders identified as key risk concentrations. The underlying research used NEISS surveillance data to characterise the injury surge and its demographic distribution – findings that reached Scientific American, Medscape, U.S. News, and multiple international outlets within 48 hours of publication.
Read →BBC News coverage on environmental toxic gas exposure risks, drawing on population-level epidemiological evidence to characterise exposure burden and the public health response implications for at-risk communities.
Read →Extended interview with JMIR on the intersection of open access publishing, research rigor, and the role of AI in accelerating – and potentially distorting – the scientific literature. Covers the tension between publication speed, methodological quality, and the epistemic risks of AI-generated content in academic writing.
Read →Expert panel commentary on how large-scale AI studies – with sample sizes in the millions – are inflating statistical significance to the point of meaninglessness, and why clinical relevance, effect magnitude, and decision-impact must replace p-value thresholds as the primary evidence criterion in the AI era.
Read →In-depth video interview tracing the research trajectory from clinical pharmacy practice in India and UAE through graduate training to building EPIAIDEA – covering what it means to do public health research that actually changes decisions, not just fills journals.
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