Medical-AI trial: an international consensus on AI-enabled emergency research for emerging infectious diseases — risk perception to decision-making (NCT07678619)
A study to build an international consensus on an AI system that supports emergency clinical research for emerging and re-emerging infectious diseases such as COVID-19, mpox, and dengue, which spread fast and carry high uncertainty. It targets the full chain of risk perception, situational assessment, intelligent decision-making, and comprehensive evaluation. 780 participants.
Trial overview (primary data)
- StatusEnrolling by invitation
- ConditionsEmerging Infectious Diseases, COVID-19, Influenza, Mpox (Monkeypox), Dengue Fever, Chikungunya Fever, Avian Influenza
- InterventionsOTHER: Multi-Agent Integrated Smart Toolkit for Emerging Infectious Diseases, OTHER: Routine Practices
- SponsorPeking University
- Target enrollment108 participants
- Period2026-08-10 〜 2027-07-31
Key points
- A study to build an international consensus on an AI system supporting emergency clinical research for emerging/re-emerging infectious diseases (COVID-19, mpox, dengue).
- It covers the full chain of emergency response: risk perception, situational assessment, intelligent decision-making, and comprehensive evaluation.
- Background is the fragmented information, delayed risk perception, experience-dependence, and decision inefficiency exposed by the COVID-19 response.
- Unlike a usual trial measuring treatment effect, its focus is the framework and consensus for running research and decisions; 780 by invitation.
- At the consensus-building and framework stage; it does not establish the effectiveness or superiority of a specific AI.
- The goal is a smart technology system covering risk perception, situation assessment, decision-making and comprehensive evaluation.
1Responding to emerging infectious disease
Emerging and re-emerging infectious diseases such as COVID-19, mpox, and dengue spread fast, have wide impact, and are poorly understood early on. In such events, how quickly danger is sensed, situations are assessed, and sound judgments are made under limited evidence can shape public health outcomes.
The COVID-19 experience also exposed challenges: information becomes fragmented, risk perception lags, assessment leans on individual experience, and complex decision-making grows inefficient.
2A system to support research and decisions
This study responds to those challenges with a framework where AI supports research and decision-making. Per the registry summary, the goal is to establish a smart-technology system covering the whole flow of risk perception, situational assessment, intelligent decision-making, and comprehensive evaluation.
Unlike a usual clinical trial that measures a treatment effect in individual patients, its focus is a framework for running emergency clinical research and decisions quickly and systematically, and the international consensus around it. It is an international effort enrolling 780 by invitation.
3Covering four stages as one flow
What this effort sets as its goal is not an individual technology but a system covering the whole flow of emergency response. Each stage carries its own difficulty.
- 1Risk perceptionHow quickly danger is detected. Delay in perception was a problem in COVID-19
- 2Situation assessmentAssessing on limited evidence, which tends to rest on individual experience
- 3Intelligent decision-makingTurning complex judgements efficiently, where fragmented information brings inefficiency
- 4Comprehensive evaluationAssembled as a smart technology system covering the whole flow
It differs in character from an ordinary clinical trial measuring treatment effect in individual patients: the focus is a framework for running emergency clinical research and decision-making quickly and systematically, and building international consensus around it. It covers 780 participants by invitation and sits at the stage of consensus and framework building.
Why it matters
Emergency infectious-disease response is shaped by how quickly and accurately research and judgment can be run. Reinforcing that foundation with AI and setting it as an international consensus is one design for public-health infrastructure that prepares for the next outbreak (this study is at the consensus stage, not an establishment of a specific AI effectiveness).
FAQ
Is this a trial of a drug or vaccine?
Why is an international consensus needed?
Sources (primary)
Source: ClinicalTrials.gov (U.S. NIH/NLM, public domain). This site does not provide medical advice. Verify the latest and exact details with the official source. This site is not endorsed or certified by the NIH/NLM.
- ClinicalTrials.gov (study record, original)
- NCT ID: NCT07678619