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
Medicine Skip to main content The New England Journal of Medicine homepage Advanced …
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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 …
[HTML][HTML] Transparency of deep neural networks for medical image analysis: A review of interpretability methods
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 …
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 …
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
Advances in artificial intelligence (AI) are enabling systems that augment and collaborate
with humans to perform simple, mechanistic tasks such as scheduling meetings and …
with humans to perform simple, mechanistic tasks such as scheduling meetings and …
Lack of transparency and potential bias in artificial intelligence data sets and algorithms: a scoping 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 …
care, but fair, generalizable algorithms depend on the clinical data on which they are trained …
Do as AI say: susceptibility in deployment of clinical decision-aids
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 …
settings such as radiology, but little work evaluates the potential impact of such systems. In …
A patient-centric dataset of images and metadata for identifying melanomas using clinical context
Prior skin image datasets have not addressed patient-level information obtained from
multiple skin lesions from the same patient. Though artificial intelligence classification …
multiple skin lesions from the same patient. Though artificial intelligence classification …
[HTML][HTML] Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts
Background Multiple studies have compared the performance of artificial intelligence (AI)–
based models for automated skin cancer classification to human experts, thus setting the …
based models for automated skin cancer classification to human experts, thus setting the …
" Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction
Despite the proliferation of explainable AI (XAI) methods, little is understood about end-
users' explainability needs and behaviors around XAI explanations. To address this gap and …
users' explainability needs and behaviors around XAI explanations. To address this gap and …