Large language models in medicine
Large language models (LLMs) can respond to free-text queries without being specifically
trained in the task in question, causing excitement and concern about their use in healthcare …
trained in the task in question, causing excitement and concern about their use in healthcare …
[HTML][HTML] Opening the black box: the promise and limitations of explainable machine learning in cardiology
Many clinicians remain wary of machine learning because of longstanding concerns about
“black box” models.“Black box” is shorthand for models that are sufficiently complex that they …
“black box” models.“Black box” is shorthand for models that are sufficiently complex that they …
[HTML][HTML] Federated learning and differential privacy for medical image analysis
The artificial intelligence revolution has been spurred forward by the availability of large-
scale datasets. In contrast, the paucity of large-scale medical datasets hinders the …
scale datasets. In contrast, the paucity of large-scale medical datasets hinders the …
[HTML][HTML] Combining the strengths of radiologists and AI for breast cancer screening: a retrospective analysis
Background We propose a decision-referral approach for integrating artificial intelligence
(AI) into the breast-cancer screening pathway, whereby the algorithm makes predictions on …
(AI) into the breast-cancer screening pathway, whereby the algorithm makes predictions on …
[HTML][HTML] Characteristics of publicly available skin cancer image datasets: a systematic review
D Wen, SM Khan, AJ Xu, H Ibrahim, L Smith… - The Lancet Digital …, 2022 - thelancet.com
Publicly available skin image datasets are increasingly used to develop machine learning
algorithms for skin cancer diagnosis. However, the total number of datasets and their …
algorithms for skin cancer diagnosis. However, the total number of datasets and their …
The need to separate the wheat from the chaff in medical informatics: Introducing a comprehensive checklist for the (self)-assessment of medical AI studies
F Cabitza, A Campagner - International Journal of Medical Informatics, 2021 - Elsevier
This editorial aims to contribute to the current debate about the quality of studies that apply
machine learning (ML) methodologies to medical data to extract value from them and …
machine learning (ML) methodologies to medical data to extract value from them and …
Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation
The ever-increasing demand for novel materials with superior properties inspires retrofitting
traditional research paradigms in the era of artificial intelligence and automation. An …
traditional research paradigms in the era of artificial intelligence and automation. An …
[HTML][HTML] Deep learning based methods for breast cancer diagnosis: a systematic review and future direction
Breast cancer is one of the precarious conditions that affect women, and a substantive cure
has not yet been discovered for it. With the advent of Artificial intelligence (AI), recently, deep …
has not yet been discovered for it. With the advent of Artificial intelligence (AI), recently, deep …
Federated learning for the healthcare metaverse: Concepts, applications, challenges, and future directions
Recent technological advancements have considerably improved healthcare systems to
provide various intelligent services, improving life quality. The Metaverse, often described as …
provide various intelligent services, improving life quality. The Metaverse, often described as …
[HTML][HTML] The contribution of data-driven technologies in achieving the sustainable development goals
The United Nations' Sustainable Development Goals (SDGs) set out to improve the quality of
life of people in developed, emerging, and developing countries by covering social and …
life of people in developed, emerging, and developing countries by covering social and …