Authors
Joobin Khadamy
Published in
Cureus. Volume 18. Issue 7. Pages e112331. Epub Jul 09, 2026.
Abstract
Scientific congresses remain central to medical education, innovation, networking, and clinical consensus, but they are increasingly challenged by rising abstract volumes, hybrid formats, reviewer fatigue, fragmented programming, repeated speaker networks, and limited post-congress knowledge transfer. This editorial proposes the AI-Augmented Scientific Congress Ecosystem, a human-supervised model in which artificial intelligence supports the congress lifecycle rather than replacing scientific committees, reviewers, moderators, or societies. Near-term applications include abstract triage, reviewer matching, duplicate and similarity screening, program scheduling, hall allocation, attendee guidance, multilingual access, live transcription, question clustering, retrieval-grounded literature support, and post-congress synthesis. More advanced future applications include live session AI-generated debate prompts, speaker discovery, future topic prediction, congress knowledge graphs, consensus-draft generation, and consent-based digital legacy archives for medical educators. A key paradox is that congresses often contain unpublished clinical experience, early data, surgical insights, and expert debate that may precede the indexed literature on which many AI systems depend. Therefore, AI should help structure emerging knowledge, not replace expert interpretation. Ophthalmology is a suitable model field because it is visual, quantitative, technology-driven, and already engaged with AI in imaging, diagnostics, surgical planning, simulation, and education. Responsible implementation requires source grounding, auditability, privacy protection, bias monitoring, consent, conflict-of-interest governance, and final human decision-making.
PMID:
42571443
Bibliographic data and abstract were imported from PubMed on 09 Aug 2026.
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