id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-243982-qhrlvmap	Unberath, Mathias	Artificial Intelligence-based Clinical Decision Support for COVID-19 -- Where Art Thou?	2020-06-05		.txt	text/plain	2845	112	43	In this perspective piece, we identify opportunities and requirements for AI-based clinical decision support systems and highlight challenges that impact"AI readiness"for rapidly emergent healthcare challenges. Learning-based algorithms had been shown to accurately forecast the onset of septic shock [1] , ML-based pattern recognition methods classified skin lesions with dermatologist level accuracy [2] , diagnostic AI systems successfully identified diabetic retinopathy during routine primary care visits [3] , AI-based breast cancer screening outperformed radiologists by a fairly large margin [4] , ML-driven triaging tools improved outcome differentiation beyond the emergency severity index [5] , AI-enabled assistance systems simplified interventional workflows [6] , and algorithm-driven organizational studies enabled redesign of infusion centers [7] . In addition to capturing data, it is equally important to understand the clinical use case -effective development and deployment of any CDS, including that driven by AI, requires a deep understanding of both the problems of focus and the environment in which it is encountered.	./cache/cord-243982-qhrlvmap.txt	./txt/cord-243982-qhrlvmap.txt