ArticleCureus2026
The AI-Augmented Scientific Congress Ecosystem (AISCE): Reimagining Scientific Congresses in the Age of Artificial Intelligence.
Article in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.