Microsoft has built an artificial intelligence-powered medical tool it claims is four times more successful than human doctors at diagnosing complex ailments, as the tech giant unveils research it believes could speed up treatment https://t.co/ODgdcMb5C3

Excited to share two advances that bring us closer to real-world impact in healthcare AI: SDBench introduces a new benchmark that transforms 304 NEJM cases into interactive diagnostic simulations. AI must ask questions, order tests, and weigh costs, mirroring the complexity of

As some of you may know, I recently moved to London to help lead a new Health AI team! Excited for our first research paper, which demonstrates that AI can tackle medicine’s toughest diagnostic challenges -- at 4x higher accuracy and 20% lower costs than a group of physicians🧵

This is incredible. Microsoft's new AI diagnosis tool is proving FOUR times more successful than human physicians at diagnosing complex diseases. Another leap towards medical superintelligence.

Agentic A.I. vs experienced physicians for diagnosis of > 300 complex diagnostic cases: 4-fold higher accuracy and 20% lower cost https://t.co/DT8jYrKhZK @HarshaNori @chrisck @Dominic1King @mustafasuleyman @erichorvitz

MAI-DxO in action, tackling one of those complex cases:

We're taking a big step towards medical superintelligence. AI models have aced multiple choice medical exams – but real patients don’t come with ABC answer options. Now MAI-DxO can solve some of the world’s toughest open-ended cases with higher accuracy and lower costs.

NEW Health Policy: "Generating evidence to support the role of #AI in diabetic eye screening: considerations from the UK National Screening Committee". @DrXiaoLiu @Denniston_Ophth @ok1zjf @siantphillips2 @adnan_tufail1 Read it here: https://t.co/3OPHJINq3x

We have supported the UK National Screening Committee in the evidence approach for #AI in Diabetic Eye Screening over last few years. In this paper we outline the NSC's considerations for evaluating ARIAs in England... more to come from @trystanbmacd https://t.co/coohFoUtNp

A new review examines the design and performance of AI monitoring systems in clinical settings and introduces a structured road map to guide the development of effective and practical monitoring systems. Read the full article: https://t.co/h0Q0N6KTdO

Perspective by J.E. Alderman et al.: Tackling Algorithmic Bias and Promoting Transparency in Health Datasets: The STANDING Together Consensus Recommendations https://t.co/AkmMJ5kOnG #AIinMedicine

The authors of a new Perspective set out recommendations enabling those creating and/or using health care datasets to identify and transparently acknowledge underlying biases. Read the full Perspective: https://t.co/AkmMJ5kOnG

CANAIRI - Collaboration for AI Translational Trials - takes flight!🪽 @NatureMedicine We will define: how to take a sociotechnical approach to responsible #AI integration. Spearheaded by my awesome co-lead @MMccradden🐦 https://t.co/j0zVuTL3yL

Exploring patient and public participation in the STANDING Together initiative for AI in healthcare. Comment from @DrXiaoLiu @unibirmingham and colleagues. https://t.co/vAFI4RwiE7

Nice🧵by @jaldmn here: https://t.co/Ekf82MljdX

Published in @LancetDigitalH and @NEJM_AI, and article published from the patient's perspective in @NatureMedicine https://t.co/ostbGdWUHz https://t.co/RVwQuCrDOJ

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One of my biggest joys is seeing an entire community take a stand for something important. STANDING Together, recommendations for health data diversity and algorithmic bias is now published: an international effort involving >350 experts & 58 countries. https://t.co/kLknVynDgo

Excited that our paper *Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations* is published in The Lancet Digital Health. Honored to be part of this amazing team of authors led by @DrXiaoLiu and @jaldmn. This

NEW Editorial: A long STANDING commitment to improving health care. Read it here: https://t.co/M80w8zSD4a