A
Alan Karthikesalingam
Researcher at Google
Publications - 180
Citations - 12926
Alan Karthikesalingam is an academic researcher from Google. The author has contributed to research in topics: Endovascular aneurysm repair & Abdominal aortic aneurysm. The author has an hindex of 49, co-authored 170 publications receiving 8980 citations. Previous affiliations of Alan Karthikesalingam include St George's Hospital & University of London.
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Journal ArticleDOI
Clinically applicable deep learning for diagnosis and referral in retinal disease
Jeffrey De Fauw,Joseph R. Ledsam,Bernardino Romera-Paredes,Stanislav Nikolov,Nenad Tomasev,Sam Blackwell,Harry Askham,Xavier Glorot,Brendan O'Donoghue,Daniel Visentin,George van den Driessche,Balaji Lakshminarayanan,Clemens Meyer,Faith Mackinder,Simon Bouton,Kareem Ayoub,Reena Chopra,Dominic King,Alan Karthikesalingam,Cian Hughes,Rosalind Raine,Julian Hughes,Dawn A Sim,Catherine A Egan,Adnan Tufail,Hugh Montgomery,Demis Hassabis,Geraint Rees,Trevor Back,Peng T. Khaw,Mustafa Suleyman,Julien Cornebise,Pearse A. Keane,Olaf Ronneberger +33 more
TL;DR: A novel deep learning architecture performs device-independent tissue segmentation of clinical 3D retinal images followed by separate diagnostic classification that meets or exceeds human expert clinical diagnoses of retinal disease.
Journal ArticleDOI
International evaluation of an AI system for breast cancer screening.
Scott Mayer McKinney,Marcin Sieniek,Varun Godbole,Jonathan Godwin,Natasha Antropova,Hutan Ashrafian,Trevor Back,Mary Chesus,Greg C. Corrado,Ara Darzi,Mozziyar Etemadi,Florencia Garcia-Vicente,Fiona J. Gilbert,Mark D. Halling-Brown,Demis Hassabis,Sunny Jansen,Alan Karthikesalingam,Christopher Kelly,Dominic King,Joseph R. Ledsam,David S. Melnick,Hormuz Mostofi,Lily Peng,Joshua J. Reicher,Bernardino Romera-Paredes,Richard Sidebottom,Mustafa Suleyman,Daniel Tse,Kenneth C. Young,Jeffrey De Fauw,Shravya Shetty +30 more
TL;DR: A robust assessment of the AI system paves the way for clinical trials to improve the accuracy and efficiency of breast cancer screening and using a combination of AI and human inputs could help to improve screening efficiency.
Journal ArticleDOI
Key challenges for delivering clinical impact with artificial intelligence.
TL;DR: The safe and timely translation of AI research into clinically validated and appropriately regulated systems that can benefit everyone is challenging, and robust clinical evaluation, using metrics that are intuitive to clinicians and ideally go beyond measures of technical accuracy, is essential.
Journal ArticleDOI
A clinically applicable approach to continuous prediction of future acute kidney injury
Nenad Tomasev,Xavier Glorot,Jack W. Rae,Michal Zielinski,Harry Askham,Andre Saraiva,Anne Mottram,Clemens Meyer,Suman V. Ravuri,Ivan Protsyuk,Alistair Connell,Cian Hughes,Alan Karthikesalingam,Julien Cornebise,Hugh Montgomery,Geraint Rees,Chris Laing,Clifton R. Baker,Kelly S. Peterson,Ruth M. Reeves,Demis Hassabis,Dominic King,Mustafa Suleyman,Trevor Back,Christopher Nielson,Christopher Nielson,Joseph R. Ledsam,Shakir Mohamed +27 more
TL;DR: A deep learning approach that predicts the risk of acute kidney injury and provides confidence assessments and a list of the clinical features that are most salient to each prediction, alongside predicted future trajectories for clinically relevant blood tests are developed.
Posted Content
Underspecification Presents Challenges for Credibility in Modern Machine Learning
Alexander D'Amour,Katherine Heller,Dan Moldovan,Ben Adlam,Babak Alipanahi,Alex Beutel,Christina Chen,Jonathan Deaton,Jacob Eisenstein,Matthew D. Hoffman,Farhad Hormozdiari,Neil Houlsby,Shaobo Hou,Ghassen Jerfel,Alan Karthikesalingam,Mario Lucic,Yi-An Ma,Cory Y. McLean,Diana Mincu,Akinori Mitani,Andrea Montanari,Zachary Nado,Vivek T. Natarajan,Christopher Nielson,Thomas F. Osborne,Rajiv Raman,Kim Ramasamy,Rory Sayres,Jessica Schrouff,Martin G. Seneviratne,Shannon Sequeira,Harini Suresh,Victor Veitch,Max Vladymyrov,Xuezhi Wang,Kellie Webster,Steve Yadlowsky,Taedong Yun,Xiaohua Zhai,D. Sculley +39 more
TL;DR: This work shows the need to explicitly account for underspecification in modeling pipelines that are intended for real-world deployment in any domain, and shows that this problem appears in a wide variety of practical ML pipelines.