A Phase 1/2 interventional study of Point-of-care large language model decision support (ChatGPT-5) in Critical Illness, Sepsis and Acute Respiratory Failure (ARF), sponsored by MetroWest Artificial Intelligence Research Workgroup. Not yet recruiting at 1 site in United States. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2025-12-18.
Sponsored by MetroWest Artificial Intelligence Research Workgroup · Phase 1/2, Interventional, and Treatment
This is a prospective, unmasked, randomized, multicenter clinical trial evaluating the impact of point-of-care large language model (LLM)-based decision support on diagnostic accuracy and clinical outcomes in adult medical intensive care unit (MICU) patients.
Consecutive adult ICU admissions at participating community hospitals (initially MetroWest Medical Center and St. Vincent Hospital) will be screened for eligibility. Eligible patients will be randomized 1:1 to standard care or an AI-assisted group. In both arms, initial evaluation and management will follow usual practice. For patients randomized to AI assistance, de-identified admission data (history and physical, labs, imaging reports, and other relevant documentation) will be formatted and submitted to a state-of-the-art LLM (ChatGPT-5) at the time of admission. The AI-generated differential diagnosis and therapeutic recommendations will be provided to the admitting team for consideration. For the standard care arm, LLM output will be generated but not shared with clinicians.
After discharge, a masked chart review will determine the "ground truth" primary diagnosis and extract outcomes including: Primary Outcome - a composite of medical errors (from time of ICU admission through day 7 of ICU stay, or ICU discharge, whichever comes first); Secondary Outcomes - 90-day mortality, ICU and hospital length of stay, and ventilator-free days.
The rapid development of large language models (LLMs) such as ChatGPT has created new opportunities and risks for their use in medicine. Although early studies suggest high diagnostic accuracy in complex clinical scenarios and ICU admissions, the impact of LLMs on real-world clinical outcomes and the optimal mode of physician-AI interaction remain uncertain. Published work from our group showed that ChatGPT-4 achieved diagnostic accuracy comparable to board-certified intensivists for ICU admissions in a retrospective study. However, prospective, randomized data on clinical outcomes are lacking.
This trial will evaluate a pragmatic paradigm for integrating LLMs at the time of ICU admission (point-of-care AI). All eligible adult MICU admissions at participating sites will be prospectively randomized to: (1) standard care, or (2) AI-assisted care in which an LLM receives standardized, de-identified admission data and returns a proposed primary diagnosis, ranked differential diagnosis (up to five conditions), suggested additional information, and prioritized therapeutic interventions. Admitting clinicians in the AI-assisted arm will be asked to review and optionally incorporate the AI recommendations and will complete a brief questionnaire regarding perceived utility and any changes in diagnosis or management.
A masked clinical adjudication panel will perform longitudinal chart review to define the "ground truth" primary diagnosis and assess error rates and outcomes. The primary endpoint is a composite of medical errors. The specific time frame will be from the time of ICU admission through day 7 of ICU stay, or ICU discharge, whichever comes first. Secondary endpoints will include 90-day mortality, ICU and hospital length of stay, and ventilator-free days. Other exploratory secondary endpoints will be considered. The trial is designed to enroll approximately 1000 patients across multiple MICUs, with interim analysis at 12 months to assess feasibility, integrity, and futility. The study is minimal risk, uses de-identified data for AI queries, and does not alter standard diagnostic testing or therapeutic options.
2,416 studies on the registry are indexed under Sepsis; 541 are open to participants now.
This study's planned enrollment of 1,000 is above the median of 102 across 1,165 interventional studies indexed under Sepsis.
Browse Sepsis studies →This is the only study on the registry with MetroWest Artificial Intelligence Research Workgroup as lead sponsor.
Compared within Sepsis, this study's primary condition as ClinicalTrials.gg classifies it; the registry's first condition term is Critical Illness. The counts are of registry records indexed under each term, refreshed daily. How we classify →
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Exclusion Criteria:
Patients receive usual ICU care per local practice. De-identified admission data may be processed and submitted to the LLM for research purposes, but AI output is not shared with treating clinicians and does not influence real-time management.
Patients receive standard ICU care plus point-of-care LLM-based decision support at admission. De-identified admission data are formatted and submitted to an LLM (ChatGPT-5). The model returns a primary diagnosis, ranked differential diagnosis list, suggested additional information, and prioritized therapeutic recommendations. This output is provided to the admitting team for consideration in ongoing management.
Other: Point-of-care large language model decision support (ChatGPT-5)
Use of a large language model (ChatGPT-5) to analyze de-identified ICU admission data (history, physical examination, laboratory results, imaging reports, and other documentation) at the time of admission. The model generates diagnostic and therapeutic recommendations that are shared with clinicians in the AI-assisted arm only.
Composite of Medical Errors
Proportion of patients with at least one clinically important diagnostic or therapeutic error identified by masked chart review (e.g., missed or delayed critical diagnosis, major guideline-discordant therapy with potential for harm).
Time frame: From the time of ICU admission through day 7 of ICU stay or ICU discharge, whichever comes first.
90-day All-Cause Mortality
All-cause mortality within 90 days of index ICU admission, as determined by chart review and available follow-up records.
Time frame: 90 days from ICU admission.
ICU Length of Stay
Total number of days spent in the ICU during the index hospitalization.
Time frame: From ICU admission to ICU discharge (up to 90 days).
Ventilator-Free Days
Number of days alive and free from invasive mechanical ventilation during the first 28 days after ICU admission.
Time frame: Up to 28 days after ICU admission.
Hospital Length of Stay
Total number of days from hospital admission to hospital discharge during the index hospitalization.
Time frame: From hospital admission to hospital discharge (up to 90 days).
Plan to share: Yes — De-identified individual participant data will be retained for reanalysis by the study team. There is no current plan for routine public sharing of individual participant-level data, but de-identified datasets may be shared with qualified investigators upon reasonable request and appropriate data use agreements.
Supporting information: Study protocol, Statistical analysis plan, Clinical study report
Start is estimated for Jan 2026; primary completion for Dec 2028, 35 months later.
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This study is not yet recruiting, as verified in Dec 2025. You cannot join it, but the record below documents what was studied.
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