Machine learning in oral squamous cell carcinoma: Current status, clinical concerns and prospects for future—A systematic review
Background Oral cancer can show heterogenous patterns of behavior. For proper and
effective management of oral cancer, early diagnosis and accurate prediction of prognosis …
effective management of oral cancer, early diagnosis and accurate prediction of prognosis …
[HTML][HTML] Developing, implementing and governing artificial intelligence in medicine: a step-by-step approach to prevent an artificial intelligence winter
D Van de Sande, ME Van Genderen… - BMJ health & care …, 2022 - ncbi.nlm.nih.gov
Objective Although the role of artificial intelligence (AI) in medicine is increasingly studied,
most patients do not benefit because the majority of AI models remain in the testing and …
most patients do not benefit because the majority of AI models remain in the testing and …
Key challenges for delivering clinical impact with artificial intelligence
Background Artificial intelligence (AI) research in healthcare is accelerating rapidly, with
potential applications being demonstrated across various domains of medicine. However …
potential applications being demonstrated across various domains of medicine. However …
Developing a delivery science for artificial intelligence in healthcare
Artificial Intelligence (AI) has generated a large amount of excitement in healthcare, mostly
driven by the emergence of increasingly accurate machine learning models. However, the …
driven by the emergence of increasingly accurate machine learning models. However, the …
[HTML][HTML] Trustworthy AI: closing the gap between development and integration of AI systems in ophthalmic practice
C González-Gonzalo, EF Thee, CCW Klaver… - Progress in retinal and …, 2022 - Elsevier
An increasing number of artificial intelligence (AI) systems are being proposed in
ophthalmology, motivated by the variety and amount of clinical and imaging data, as well as …
ophthalmology, motivated by the variety and amount of clinical and imaging data, as well as …
Nuclear medicine and artificial intelligence: best practices for evaluation (the RELAINCE guidelines)
An important need exists for strategies to perform rigorous objective clinical-task-based
evaluation of artificial intelligence (AI) algorithms for nuclear medicine. To address this need …
evaluation of artificial intelligence (AI) algorithms for nuclear medicine. To address this need …
Continual updating and monitoring of clinical prediction models: time for dynamic prediction systems?
Clinical prediction models (CPMs) have become fundamental for risk stratification across
healthcare. The CPM pipeline (development, validation, deployment, and impact …
healthcare. The CPM pipeline (development, validation, deployment, and impact …
[PDF][PDF] A path for translation of machine learning products into healthcare delivery
Despite enormous enthusiasm, machine learning models are rarely translated into clinical
care and there is minimal evidence of clinical or economic impact. New conference venues …
care and there is minimal evidence of clinical or economic impact. New conference venues …
[HTML][HTML] Detection of calibration drift in clinical prediction models to inform model updating
Abstract Model calibration, critical to the success and safety of clinical prediction models,
deteriorates over time in response to the dynamic nature of clinical environments. To support …
deteriorates over time in response to the dynamic nature of clinical environments. To support …
Holding AI to account: challenges for the delivery of trustworthy AI in healthcare
The need for AI systems to provide explanations for their behaviour is now widely
recognised as key to their adoption. In this article, we examine the problem of trustworthy AI …
recognised as key to their adoption. In this article, we examine the problem of trustworthy AI …