{"id":44394,"topic":"ai","source":"Google DeepMind","title":"AI co-clinician: researching the path toward AI-augmented care - Google DeepMind","url":"https://deepmind.google/blog/ai-co-clinician/","url_hash":"ef460ce904dc1c49e4eea32756b7318b24f0efd0","author":"","summary":"AI co-clinician: researching the path toward AI-augmented care Google DeepMind","content":"Engineering trust with safeguards for clinical-grade AI\nThe transition and deployment of AI into clinical environments requires uncompromising architectural and operational safeguards. In our research on simulations of patient-facing telemedical conversations, AI co-clinician uses a dual-agent architecture: a \"Planner\" module continuously monitors the conversation, verifying that the \"Talker\" agent stays within safe clinical boundaries.\nSimilarly, to meet doctorsâ needs AI co-clinician prioritizes clinical-grade evidence, performing verification and citation checking for retrieval. 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This project involved collaborations with many teams at Google DeepMind, Google Research, Google Cloud and Google for Health and we thank our team mates for insightful discussions and contributions.\nIn particular, AI co-clinician would not have been possible without the core research and engineering efforts of Aniruddh Raghu, Arthur Chen, Charlie Taylor, CJ Park, David Stutz, Devora Berlowitz, Doug Fritz, Dylan Slack, Eliseo Papa, Jack Chen, JD Velasquez, Jing Rong Lim, Katya Tregubova, Kelvin Guu, Meet Shah, Richard Green, Ryutaro Tanno, Sukhdeep Singh, Victoria Johnston, Adam Rodman.\nWe thank our many collaborators for their invaluable contributions, including Ali Eslami, Aliya Rysbeck, Andy Song, Anil Palepu, Anna Cupani, Bakul Patel, Bibo Xu, Brett Hatfield, David Wu, Ed Chi, Emma Cooney, Erica Oppenheimer, Erwan Rolland, Euan A. 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In our research on simulations of patient-facing telemedical conversations, AI co-clinician uses a dual-agent architecture: a \"Planner\" module continuously monitors the conversation, verifying that the \"Talker\" agent stays within safe clinical boundaries.\nSimilarly, to meet doctorsâ needs AI co-clinician prioritizes clinical-grade evidence, performing verification and citation checking for retrieval. 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Our goal is to ensure that medical AI is developed and deployed responsibly in line with applicable standards, supporting better health worldwide.\nNote: Our research collaborations are not, at this stage, intended for use in the diagnosis, cure, mitigation, treatment, or prevention of disease, or to provide medical advice.\nAcknowledgements\nWe are grateful to our research partners at Harvard Medical School and Stanford Medicine and the many medical centers and care organizations engaging in further trusted tester evaluations with our team. This project involved collaborations with many teams at Google DeepMind, Google Research, Google Cloud and Google for Health and we thank our team mates for insightful discussions and contributions.\nIn particular, AI co-clinician would not have been possible without the core research and engineering efforts of Aniruddh Raghu, Arthur Chen, Charlie Taylor, CJ Park, David Stutz, Devora Berlowitz, Doug Fritz, Dylan Slack, Eliseo Papa, Jack Chen, JD Velasquez, Jing Rong Lim, Katya Tregubova, Kelvin Guu, Meet Shah, Richard Green, Ryutaro Tanno, Sukhdeep Singh, Victoria Johnston, Adam Rodman.\nWe thank our many collaborators for their invaluable contributions, including Ali Eslami, Aliya Rysbeck, Andy Song, Anil Palepu, Anna Cupani, Bakul Patel, Bibo Xu, Brett Hatfield, David Wu, Ed Chi, Emma Cooney, Erica Oppenheimer, Erwan Rolland, Euan A. 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