Muhammad Mustafa Habib, Zahra Hoodbhoy, M A Rehman Siddiqui
Knowledge, attitudes, and perceptions of healthcare students and professionals on the use of artificial intelligence in healthcare in Pakistan
PLOS digital health
deep learningbased clinical imaging analysis underlies diagnostic artificial intelligence ai models match even exceed performance clinical experts potential revolutionize clinical practice wide variety automated machine learning automl platforms lower technical barrier entry deep learning extending ai capabilities clinicians limited technical expertise even autonomous foundation models multimodal large language models provide technical overview automl descriptions automl may applied education research clinical practice stage process conducting automl project outlined emphasis ethical technical best practices specifically data acquisition data partitioning model training model validation analysis model deployment considered strengths limitations available codefree codeminimal codeintensive automl platforms considered automl great potential democratize ai medicine improving ai literacy enabling handson education automl may serve useful adjunct research facilitating rapid testing benchmarking significant computational resources committed automl may also applied clinical contexts provided regulatory requirements met abstraction automl arduous aspects ai engineering promotes prioritization data set curation supporting transition conventional modeldriven approaches datacentric development fulfill potential clinicians must educated apply technologies ethically rigorously effectively tutorial represents comprehensive summary relevant considerations