A deep learning system for differential diagnosis of skin diseases 1 1 1 1 1 1,2 † Yuan Liu , Ayush Jain , Clara Eng , David H. Way , Kang Lee , Peggy Bui , Kimberly Kanada , ‡ 1 1 1 Guilherme de Oliveira Marinho , Jessica Gallegos , Sara Gabriele , Vishakha Gupta , Nalini 1,3,§ 1 4 1 1 Singh , Vivek Natarajan , Rainer Hofmann-Wellenhof , Greg S. Corrado , Lily H. Peng , Dale 1 1 † 1, 1, 1, R. Webster , Dennis Ai , Susan Huang , Yun Liu * , R. Carter Dunn * *, David Coz * * Affiliations: 1 G oogle Health, Palo Alto, CA, USA 2 U niversity of California, San Francisco, CA, USA 3 M assachusetts Institute of Technology, Cambridge, MA, USA 4 M edical University of Graz, Graz, Austria † W ork done at Google Health via Advanced Clinical. ‡ W ork done at Google Health via Adecco Staffing. § W ork done at Google Health. *Corresponding author:
[email protected] **These authors contributed equally to this work. Abstract Skin and subcutaneous conditions affect an estimated 1.9 billion people at any given time and remain the fourth leading cause of non-fatal disease burden worldwide. Access to dermatology care is limited due to a shortage of dermatologists, causing long wait times and leading patients to seek dermatologic care from general practitioners. However, the diagnostic accuracy of general practitioners has been reported to be only 0.24-0.70 (compared to 0.77-0.96 for dermatologists), resulting in over- and under-referrals, delays in care, and errors in diagnosis and treatment. In this paper, we developed a deep learning system (DLS) to provide a differential diagnosis of skin conditions for clinical cases (skin photographs and associated medical histories).