Towards Conversational Diagnostic AI
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929206532767744 |
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| author | Tu, Tao Palepu, Anil Schaekermann, Mike Saab, Khaled Freyberg, Jan Tanno, Ryutaro Wang, Amy Li, Brenna Amin, Mohamed Tomasev, Nenad Azizi, Shekoofeh Singhal, Karan Cheng, Yong Hou, Le Webson, Albert Kulkarni, Kavita Mahdavi, S Sara Semturs, Christopher Gottweis, Juraj Barral, Joelle Chou, Katherine Corrado, Greg S Matias, Yossi Karthikesalingam, Alan Natarajan, Vivek |
| author_facet | Tu, Tao Palepu, Anil Schaekermann, Mike Saab, Khaled Freyberg, Jan Tanno, Ryutaro Wang, Amy Li, Brenna Amin, Mohamed Tomasev, Nenad Azizi, Shekoofeh Singhal, Karan Cheng, Yong Hou, Le Webson, Albert Kulkarni, Kavita Mahdavi, S Sara Semturs, Christopher Gottweis, Juraj Barral, Joelle Chou, Katherine Corrado, Greg S Matias, Yossi Karthikesalingam, Alan Natarajan, Vivek |
| contents | At the heart of medicine lies the physician-patient dialogue, where skillful history-taking paves the way for accurate diagnosis, effective management, and enduring trust. Artificial Intelligence (AI) systems capable of diagnostic dialogue could increase accessibility, consistency, and quality of care. However, approximating clinicians' expertise is an outstanding grand challenge. Here, we introduce AMIE (Articulate Medical Intelligence Explorer), a Large Language Model (LLM) based AI system optimized for diagnostic dialogue.
AMIE uses a novel self-play based simulated environment with automated feedback mechanisms for scaling learning across diverse disease conditions, specialties, and contexts. We designed a framework for evaluating clinically-meaningful axes of performance including history-taking, diagnostic accuracy, management reasoning, communication skills, and empathy. We compared AMIE's performance to that of primary care physicians (PCPs) in a randomized, double-blind crossover study of text-based consultations with validated patient actors in the style of an Objective Structured Clinical Examination (OSCE). The study included 149 case scenarios from clinical providers in Canada, the UK, and India, 20 PCPs for comparison with AMIE, and evaluations by specialist physicians and patient actors. AMIE demonstrated greater diagnostic accuracy and superior performance on 28 of 32 axes according to specialist physicians and 24 of 26 axes according to patient actors. Our research has several limitations and should be interpreted with appropriate caution. Clinicians were limited to unfamiliar synchronous text-chat which permits large-scale LLM-patient interactions but is not representative of usual clinical practice. While further research is required before AMIE could be translated to real-world settings, the results represent a milestone towards conversational diagnostic AI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_05654 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Conversational Diagnostic AI Tu, Tao Palepu, Anil Schaekermann, Mike Saab, Khaled Freyberg, Jan Tanno, Ryutaro Wang, Amy Li, Brenna Amin, Mohamed Tomasev, Nenad Azizi, Shekoofeh Singhal, Karan Cheng, Yong Hou, Le Webson, Albert Kulkarni, Kavita Mahdavi, S Sara Semturs, Christopher Gottweis, Juraj Barral, Joelle Chou, Katherine Corrado, Greg S Matias, Yossi Karthikesalingam, Alan Natarajan, Vivek Artificial Intelligence Computation and Language Machine Learning At the heart of medicine lies the physician-patient dialogue, where skillful history-taking paves the way for accurate diagnosis, effective management, and enduring trust. Artificial Intelligence (AI) systems capable of diagnostic dialogue could increase accessibility, consistency, and quality of care. However, approximating clinicians' expertise is an outstanding grand challenge. Here, we introduce AMIE (Articulate Medical Intelligence Explorer), a Large Language Model (LLM) based AI system optimized for diagnostic dialogue. AMIE uses a novel self-play based simulated environment with automated feedback mechanisms for scaling learning across diverse disease conditions, specialties, and contexts. We designed a framework for evaluating clinically-meaningful axes of performance including history-taking, diagnostic accuracy, management reasoning, communication skills, and empathy. We compared AMIE's performance to that of primary care physicians (PCPs) in a randomized, double-blind crossover study of text-based consultations with validated patient actors in the style of an Objective Structured Clinical Examination (OSCE). The study included 149 case scenarios from clinical providers in Canada, the UK, and India, 20 PCPs for comparison with AMIE, and evaluations by specialist physicians and patient actors. AMIE demonstrated greater diagnostic accuracy and superior performance on 28 of 32 axes according to specialist physicians and 24 of 26 axes according to patient actors. Our research has several limitations and should be interpreted with appropriate caution. Clinicians were limited to unfamiliar synchronous text-chat which permits large-scale LLM-patient interactions but is not representative of usual clinical practice. While further research is required before AMIE could be translated to real-world settings, the results represent a milestone towards conversational diagnostic AI. |
| title | Towards Conversational Diagnostic AI |
| topic | Artificial Intelligence Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2401.05654 |