Medical Image Foundation Models
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%20are%20large-scale%20deep-learning%20models%20trained%20on%20extensive%20datasets%20using%20self-supervised%20techniques.%20These%20models%20serve%20as%20a%20base%20for%20various%20downstream%20tasks%2C%20including%20healthcare.%20FMs%20have%20been%20adopted%20with%20great%20success%20across%20various%20domains%20within%20healthcare%2C%20including%20natural%20language%20processing%20(NLP)%2C%20computer%20vision%2C%20graph%20learning%2C%20biology%2C%20and%20omics.%20Existing%20healthcare-based%20surveys%20have%20not%20yet%20included%20all%20of%20these%20domains.%20Therefore%2C%20this%20survey%20provides%20a%20comprehensive%20overview%20of%20FMs%20in%20healthcare.%20We%20focus%20on%20the%20history%2C%20learning%20strategies%2C%20flagship%20models%2C%20applications%2C%20and%20challenges%20of%20FMs.%20We%20explore%20how%20FMs%20such%20as%20the%20BERT%20and%20GPT%20families%20are%20reshaping%20various%20healthcare%20domains%2C%20including%20clinical%20large%20language%20models%2C%20medical%20image%20analysis%2C%20and%20omics%20data.%20Furthermore%2C%20we%20provide%20a%20detailed%20taxonomy%20of%20healthcare%20applications%20facilitated%20by%20FMs%2C%20such%20as%20clinical%20NLP%2C%20medical%20computer%20vision%2C%20graph%20learning%2C%20and%20other%20biology-related%20tasks.%20Despite%20the%20promising%20opportunities%20FMs%20provide%2C%20they%20also%20have%20several%20associated%20challenges%2C%20which%20are%20explained%20in%20detail.%20We%20also%20outline%20potential%20future%20directions%20to%20provide%20researchers%20and%20practitioners%20with%20insights%20into%20the%20potential%20and%20limitations%20of%20FMs%20in%20healthcare%20to%20advance%20their%20deployment%20and%20mitigate%20associated%20risks.)
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