The current and future state of AI interpretation of medical images

P Rajpurkar, MP Lungren - New England Journal of Medicine, 2023 - Mass Medical Soc
The Current and Future State of AI Interpretation of Medical Images | New England Journal of
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AI in health and medicine

P Rajpurkar, E Chen, O Banerjee, EJ Topol - Nature medicine, 2022 - nature.com
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 …

Evaluation and mitigation of the limitations of large language models in clinical decision-making

P Hager, F Jungmann, R Holland, K Bhagat… - Nature medicine, 2024 - nature.com
Clinical decision-making is one of the most impactful parts of a physician's responsibilities
and stands to benefit greatly from artificial intelligence solutions and large language models …

[HTML][HTML] Transparency of deep neural networks for medical image analysis: A review of interpretability methods

Z Salahuddin, HC Woodruff, A Chatterjee… - Computers in biology and …, 2022 - Elsevier
Artificial Intelligence (AI) has emerged as a useful aid in numerous clinical applications for
diagnosis and treatment decisions. Deep neural networks have shown the same or better …

Disparities in dermatology AI performance on a diverse, curated clinical image set

R Daneshjou, K Vodrahalli, RA Novoa, M Jenkins… - Science …, 2022 - science.org
An estimated 3 billion people lack access to dermatological care globally. Artificial
intelligence (AI) may aid in triaging skin diseases and identifying malignancies. However …

Human–AI collaboration enables more empathic conversations in text-based peer-to-peer mental health support

A Sharma, IW Lin, AS Miner, DC Atkins… - Nature Machine …, 2023 - nature.com
Advances in artificial intelligence (AI) are enabling systems that augment and collaborate
with humans to perform simple, mechanistic tasks such as scheduling meetings and …

Heterogeneity and predictors of the effects of AI assistance on radiologists

F Yu, A Moehring, O Banerjee, T Salz, N Agarwal… - Nature Medicine, 2024 - nature.com
The integration of artificial intelligence (AI) in medical image interpretation requires effective
collaboration between clinicians and AI algorithms. Although previous studies demonstrated …

Lack of transparency and potential bias in artificial intelligence data sets and algorithms: a sco** review

R Daneshjou, MP Smith, MD Sun… - JAMA …, 2021 - jamanetwork.com
Importance Clinical artificial intelligence (AI) algorithms have the potential to improve clinical
care, but fair, generalizable algorithms depend on the clinical data on which they are trained …

A reinforcement learning model for AI-based decision support in skin cancer

C Barata, V Rotemberg, NCF Codella, P Tschandl… - Nature Medicine, 2023 - nature.com
We investigated whether human preferences hold the potential to improve diagnostic
artificial intelligence (AI)-based decision support using skin cancer diagnosis as a use case …

Do as AI say: susceptibility in deployment of clinical decision-aids

S Gaube, H Suresh, M Raue, A Merritt… - NPJ digital …, 2021 - nature.com
Artificial intelligence (AI) models for decision support have been developed for clinical
settings such as radiology, but little work evaluates the potential impact of such systems. In …