AI in health and medicine
Artificial intelligence (AI) is poised to broadly reshape medicine, potentially improving the
experiences of both clinicians and patients. We discuss key findings from a 2-year weekly …
experiences of both clinicians and patients. We discuss key findings from a 2-year weekly …
Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI
A growing number of artificial intelligence (AI)-based clinical decision support systems are
showing promising performance in preclinical, in silico, evaluation, but few have yet …
showing promising performance in preclinical, in silico, evaluation, but few have yet …
Capabilities of gpt-4 on medical challenge problems
H Nori, N King, SM McKinney, D Carignan… - arXiv preprint arXiv …, 2023 - arxiv.org
Large language models (LLMs) have demonstrated remarkable capabilities in natural
language understanding and generation across various domains, including medicine. We …
language understanding and generation across various domains, including medicine. We …
Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations
Artificial intelligence (AI) systems have increasingly achieved expert-level performance in
medical imaging applications. However, there is growing concern that such AI systems may …
medical imaging applications. However, there is growing concern that such AI systems may …
[HTML][HTML] Significance of machine learning in healthcare: Features, pillars and applications
Abstract Machine Learning (ML) applications are making a considerable impact on
healthcare. ML is a subtype of Artificial Intelligence (AI) technology that aims to improve the …
healthcare. ML is a subtype of Artificial Intelligence (AI) technology that aims to improve the …
On the opportunities and risks of foundation models
AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …
Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review
AAH de Hond, AM Leeuwenberg, L Hooft… - NPJ digital …, 2022 - nature.com
While the opportunities of ML and AI in healthcare are promising, the growth of complex data-
driven prediction models requires careful quality and applicability assessment before they …
driven prediction models requires careful quality and applicability assessment before they …
Swarm learning for decentralized and confidential clinical machine learning
S Warnat-Herresthal, H Schultze, KL Shastry… - Nature, 2021 - nature.com
Fast and reliable detection of patients with severe and heterogeneous illnesses is a major
goal of precision medicine,. Patients with leukaemia can be identified using machine …
goal of precision medicine,. Patients with leukaemia can be identified using machine …
The value of standards for health datasets in artificial intelligence-based applications
A Arora, JE Alderman, J Palmer, S Ganapathi… - Nature Medicine, 2023 - nature.com
Artificial intelligence as a medical device is increasingly being applied to healthcare for
diagnosis, risk stratification and resource allocation. However, a growing body of evidence …
diagnosis, risk stratification and resource allocation. However, a growing body of evidence …
Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on …
Introduction The Transparent Reporting of a multivariable prediction model of Individual
Prognosis Or Diagnosis (TRIPOD) statement and the Prediction model Risk Of Bias …
Prognosis Or Diagnosis (TRIPOD) statement and the Prediction model Risk Of Bias …