Do no harm: a roadmap for responsible machine learning for health care
19-08-2019 – Jenna Wiens,Suchi Saria,Mark Sendak,Marzyeh Ghassemi,Vincent X. Liu,Finale Doshi-Velez,Kenneth Jung,Katherine Heller,David Kale,Mohammed Saeed,Pilar N. Ossorio,Sonoo Thadaney-Israni,Anna Goldenberg
Author Correction: Microbiota therapy acts via a regulatory T cell MyD88/RORγt pathway to suppress food allergy
16-08-2019 – Azza Abdel-Gadir,Emmanuel Stephen-Victor,Georg K. Gerber,Magali Noval Rivas,Sen Wang,Hani Harb,Leighanne Wang,Ning Li,Elena Crestani,Sara Spielman,William Secor,Heather Biehl,Nicholas DiBenedetto,Xiaoxi Dong,Dale T. Umetsu,Lynn Bry,Rima Rachid,Talal A. Chatila
Resistance to TRK inhibition mediated by convergent MAPK pathway activation
12-08-2019 – Emiliano Cocco,Alison M. Schram,Amanda Kulick,Sandra Misale,Helen H. Won,Rona Yaeger,Pedram Razavi,Ryan Ptashkin,Jaclyn F. Hechtman,Eneda Toska,James Cownie,Romel Somwar,Sophie Shifman,Marissa Mattar,S. Duygu Selçuklu,Aliaksandra Samoila,Sean Guzman,Brian B. Tuch,Kevin Ebata,Elisa de Stanchina,Rebecca J. Nagy,Richard B. Lanman,Brian Houck-Loomis,Juber A. Patel,Michael F. Berger,Marc Ladanyi,David M. Hyman,Alexander Drilon,Maurizio Scaltriti
TRK fusions are found in a variety of cancer types, lead to oncogenic addiction, and strongly predict tumor-agnostic efficacy of TRK inhibition
Gut microbiome alteration in MORDOR I: a community-randomized trial of mass azithromycin distribution
12-08-2019 – T. Doan,A. Hinterwirth,L. Worden,A. M. Arzika,R. Maliki,A. Abdou,S. Kane,L. Zhong,S. L. Cummings,S. Sakar,C. Chen,C. Cook,E. Lebas,E. D. Chow,I. Nachamkin,T. C. Porco,J. D. Keenan,T. M. Lietman
The MORDOR I trial
An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosis
12-08-2019 – Po-Hsuan Cameron Chen,Krishna Gadepalli,Robert MacDonald,Yun Liu,Shiro Kadowaki,Kunal Nagpal,Timo Kohlberger,Jeffrey Dean,Greg S. Corrado,Jason D. Hipp,Craig H. Mermel,Martin C. Stumpe
The microscopic assessment of tissue samples is instrumental for the diagnosis and staging of cancer, and thus guides therapy. However, these assessments demonstrate considerable variability and many regions of the world lack access to trained pathologists. Though artificial intelligence (AI) promises to improve the access and quality of healthcare, the costs of image digitization in pathology and difficulties in deploying AI solutions remain as barriers to real-world use. Here we propose a cost-effective solution: the augmented reality microscope (ARM). The ARM overlays AI-based information onto the current view of the sample in real time, enabling seamless integration of AI into routine workflows. We demonstrate the utility of ARM in the detection of metastatic breast cancer and the identification of prostate cancer, with latency compatible with real-time use. We anticipate that the ARM will remove barriers towards the use of AI designed to improve the accuracy and efficiency of cancer diagnosis.
Publisher Correction: Homeward bound
09-08-2019 – Shraddha Chakradhar
An amendment to this paper has been published and can be accessed via a link at the top of the paper.