Teaching Doctors in Training to Reason With Artificial Intelligence (TEACH-AI)
Teaching Internal Medicine and Family Medicine Residents to Reason With Generative AI: A Multi-Site Randomized Controlled Trial (TEACH-AI)
The purpose of the TEACH-AI study is to assess whether a brief, structured workshop on artificial intelligence can improve the performance of medicine doctors in training (i.e. residents) in their diagnostic and management reasoning.
In this multi-site randomized controlled trial, internal medicine and family medicine residents are assigned either to receive an in-person workshop on safe, effective LLM use before a standardized AI-assisted assessment, or to complete the same assessment before receiving the workshop. Residents will review clinical cases that are fully synthetic, no protected health information is used, using a password protected LLM interface.
調査の概要
詳細な説明
Large language models (LLMs) have rapidly entered routine use in medical education and clinical practice. Prior randomized trials have shown that, although LLMs can outperform individual clinicians on some reasoning benchmarks, providing physicians with access to an LLM does not necessarily improve their diagnostic or management performance, and LLMs alone may perform better than physician-plus-LLM teams. These findings suggest that human-AI collaboration is fraught, non-trivial and may require explicit training.
TEACH-AI is a pragmatic, multi-site, two-arm randomized controlled trial embedded within protected residency didactic time. The primary objective is to determine whether a single in-person workshop on the basics of LLMs and best practices of prompting/verification strategies improves residents' performance on an AI-assisted assessment, compared with residents who complete the simulation before receiving the workshop. All participants will ultimately receive the same workshop and the same assessment (either workshop-first vs assessment-first).
The simulation consists of multiple fully synthetic vignettes delivered via a password protected LLM interface. Residents interact freely with the LLM using natural-language prompts and then submit structured final responses regarding aspects such as leading diagnosis, differential, management plan, and/or justification. The platform will record prompts, model outputs, final answers, and timing. Vignette scoring combines correctness of the final diagnosis or management plan. Scoring is conducted by blinded faculty using standardized rubrics and then any discrepancies will be resolved through multiple rounds of discussions.
The trial will enroll up to 200 residents across four ACGME-accredited programs (internal medicine at Beth Israel Deaconess Medical Center, Stanford University, and Cambridge Health Alliance; family medicine at AdventHealth Orlando).
研究の種類
入学 (推定)
段階
- 適用できない
連絡先と場所
研究連絡先
- 名前：Jacob M Koshy, MD, MPH
- 電話番号：617-754-4677
- メール：jkoshy@bidmc.harvard.edu
研究場所
-
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California
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Palo Alto、California、アメリカ、94305
- Stanford University
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コンタクト：
- Kevin Keet, MD
- 電話番号：650-498-4559
- メール：kkeet@stanford.edu
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主任研究者：
- Kevin R Keet, MD
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Florida
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Orlando、Florida、アメリカ、32804
- AdventHealth
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コンタクト：
- Raj Mehta, MD
- 電話番号：407-845-8383
- メール：raj.mehta.md@adventhealth.com
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主任研究者：
- Raj Mehta, MD
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Massachusetts
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Boston、Massachusetts、アメリカ、02215
- Beth Israel Deaconess Medical Center
-
コンタクト：
- Jacob M Koshy, MD, MPH
- 電話番号：617-754-4677
- メール：jkoshy@bidmc.harvard.edu
-
主任研究者：
- Jacob M Koshy, MD, MPH
-
Cambridge、Massachusetts、アメリカ、02139
- Cambridge Health Alliance
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コンタクト：
- Priyank Jain, MD
- 電話番号：617-665-1000
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主任研究者：
- Priyank Jain, MD
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参加基準
適格基準
就学可能な年齢
- 子
- 大人
- 高齢者
健康ボランティアの受け入れ
説明
Inclusion Criteria:
- Participants must be licensed physicians and have started at least post-graduate year 1 (PGY1) of medical training.
- Training in Internal medicine or family medicine or emergency medicine.
- Able to provide informed consent and complete assessments in English.
Exclusion Criteria:
- Not currently practicing clinically.
- Resident directly involved in the design of the study, development of the workshop, or creation/piloting of the assessment vignettes.
- Resident who declines or withdraws consent.
研究計画
研究はどのように設計されていますか？
デザインの詳細
- 主な目的：診断
- 割り当て：ランダム化
- 介入モデル：並列代入
- マスキング：独身
武器と介入
参加者グループ / アーム |
介入・治療 |
|---|---|
|
介入なし：Assessment-First
Participants assigned to this group will be tasked with completing the assessment using a password protected LLM interface first in advance of the workshop.
The workshop will be delivered during a scheduled mandatory didactic time slot.
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|
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実験的：Workshop-first
Participants assigned to this group will be tasked with completing the assessment using a password protected LLM interface after they participate in a scheduled mandatory didactic time slot.
|
Participants will attend a workshop during their didactic time that will review various aspects of large language models including fundamentals and best practices of interacting and interpreting their outputs.
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この研究は何を測定していますか？
主要な結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
|
Total Score on Expert-Development Rubrics
時間枠：Within 24 hours of assessment completion.
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The primary outcome will be the number of correct responses for all cases using select questions from expert-developed scoring rubrics.
The rubrics were developed using a Delphi consensus process by expert physicians as established by Goh et al (Nature Medicine, 2025; DOI: 10.1038/s41591-024-03456-y). The primary outcome will be analyzed at the case level, comparing performance between the randomized study groups, with a higher number of correct responses indicating a better outcome.
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Within 24 hours of assessment completion.
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二次結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
|
Time Spent on Management
時間枠：Within 24 hours of assessment completion.
|
Comparison of time spent per case between the two study groups.
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Within 24 hours of assessment completion.
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Prompt Count
時間枠：Within 24 hours of assessment completion.
|
Comparison of the number of resident-initiated prompts to the LLM per case between the two study groups.
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Within 24 hours of assessment completion.
|
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Management Reasoning Using Expert-Derived Rubrics
時間枠：Within 24 hours of assessment completion.
|
Comparison of management reasoning accuracy based on the number of correct responses per case between the two groups on expert-developed scoring rubrics.
The rubrics were developed using a Delphi consensus process by expert physicians as established by Goh et al (Nature Medicine, 2025; DOI: 10.1038/s41591-024-03456-y). The primary outcome will be analyzed at the case level, comparing performance between the randomized study groups with more correct responses indicating a better outcome.
|
Within 24 hours of assessment completion.
|
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Diagnostic Reasoning
時間枠：Within 24 hours of assessment completion.
|
Comparison of diagnostic reasoning accuracy based on the number of correct responses per case between the two groups.
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Within 24 hours of assessment completion.
|
協力者と研究者
研究記録日
主要日程の研究
研究開始 (推定)
一次修了 (推定)
研究の完了 (推定)
試験登録日
最初に提出
QC基準を満たした最初の提出物
最初の投稿 (実際)
学習記録の更新
投稿された最後の更新 (実際)
QC基準を満たした最後の更新が送信されました
最終確認日
詳しくは
本研究に関する用語
キーワード
その他の研究ID番号
- 2026P000369
- 63901784-350738 (その他の助成金/資金番号：Macy Foundation)
個々の参加者データ (IPD) の計画
個々の参加者データ (IPD) を共有する予定はありますか?
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