N
Nenad Tomasev
Researcher at Jožef Stefan Institute
Publications - 63
Citations - 3725
Nenad Tomasev is an academic researcher from Jožef Stefan Institute. The author has contributed to research in topics: Computer science & Curse of dimensionality. The author has an hindex of 17, co-authored 50 publications receiving 2314 citations. Previous affiliations of Nenad Tomasev include University of Novi Sad.
Papers
More filters
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
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.
Journal ArticleDOI
AI for social good: unlocking the opportunity for positive impact.
Nenad Tomasev,Julien Cornebise,Frank Hutter,Frank Hutter,Shakir Mohamed,Angela Picciariello,Bec Connelly,Danielle Belgrave,Daphne Ezer,Daphne Ezer,Fanny Cachat van der Haert,Frank Mugisha,Gerald Abila,Hiromi Arai,Hisham Almiraat,Julia Proskurnia,Kyle Snyder,Mihoko Otake-Matsuura,Mustafa Othman,Tobias Glasmachers,Wilfried de Wever,Yee Whye Teh,Mohammad Emtiyaz Khan,Ruben De Winne,Tom Schaul,Claudia Clopath +25 more
TL;DR: This work provides a set of guidelines for establishing successful long-term collaborations between AI researchers and application-domain experts, relate them to existing AI4SG projects and identify key opportunities for future AI applications targeted towards social good.
Journal ArticleDOI
The Role of Hubness in Clustering High-Dimensional Data
TL;DR: This paper shows that hubness, i.e., the tendency of high-dimensional data to contain points (hubs) that frequently occur in k-nearest-neighbor lists of other points, can be successfully exploited in clustering, and proposes several hubness-based clustering algorithms.
Journal ArticleDOI
Large Language Models Encode Clinical Knowledge
Karan Singhal,Shekoofeh Azizi,Tao Tu,S Mahdavi,Jason Loh Seong Wei,Hyung Won Chung,Nathan Scales,Ajay Kumar Tanwani,Heather Cole-Lewis,Stephen Pfohl,P. A. Payne,Martin G. Seneviratne,P. Gamble,Chris Kelly,Nathaneal Scharli,Aakanksha Chowdhery,Philip Andrew Mansfield,Blaise Aguera y Arcas,Dale R. Webster,Greg S. Corrado,Yossi Matias,K. Chou,Juraj Gottweis,Nenad Tomasev,Yun Liu,Alvin Rajkomar,Joëlle K. Barral,Christopher Semturs,Alan Karthikesalingam,Vivek T. Natarajan +29 more
TL;DR: The authors proposed a human evaluation framework for model answers along multiple axes including factuality, comprehension, reasoning, possible harm and bias, and showed that comprehension, knowledge recall and reasoning improve with model scale and instruction prompt tuning, suggesting the potential utility of LLMs in medicine.