researching the path toward AI-augmented care — Google DeepMind
Engineering trust with safeguards for clinical-grade AI
The 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.
Similarly, to meet doctors’ needs AI co-clinician prioritizes clinical-grade evidence, performing verification and citation checking for retrieval. The evaluations we report above were constructed by physicians to mirror a range of their real-world evidence needs, formulating questions from hypothetical scenarios for rigorously evaluating AI’s capabilities.
Research collaborations for rigorous real-world evaluation of AI co-clinician
To further develop and assess AI co-clinician, we are currently advancing a phased approach with academic and research collaborators across globally diverse healthcare settings including in the US, India, Australia, New Zealand, Singapore and UAE.
As we progress through these evaluation phases, we will further our research in more geos including mission-aligned healthcare organizations and academic medical centers. Our goal is to ensure that medical AI is developed and deployed responsibly in line with applicable standards, supporting better health worldwide.
Note: 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.
Acknowledgements
We 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.
In 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.
We 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. Ashley, Francesca Pietra, Rebeca Santamaria-Fernadez, Gordon Turner, Gregory Wayne, Hannah Gladman, Irene Teinemaa, Jack O’Sullivan, Jacob Koshy, Jan Freyberg, Jason Gusdorf, Joelle Wilson, Katherine Tong, Juraj Gottweis, Michael Howell, Mili Sanwalka, Pavel Dubov, Pete Clardy, Peter Brodeur, Rachelle Sico, SiWai Man, Sumanth Dahathri, Taylan Cemgil, Tim Strother, Uchechi Okereke, Valentin Lievin, Vishnu Ravi, Yana Lunts, Yun Liu, Simon Staffell, Rachel Teo, Adriana Fernandez Lara, Armin Senoner, Danielle Breen, Paula Tesch, Leen Verburgh, Dimple Vijaykumar, Juanita Bawagan, Muinat Abdul, Mariana Montes and Rob Ashley. Feature videos were produced by Christopher Godfree, Matt Mager, Emma Moxhay and Simon Waldron.
Thanks to James Manyika and Demis Hassabis for their insightful guidance and support throughout the research process.
