@article{351,
  keywords = {artificial intelligence, clinical reasoning, diagnostic error, machine learning, management reasoning, overdiagnosis, patient safety},
  author = {Paul A. Bergl and Thilan P. Wijesekera and Najlla Nassery and Karen Cosby},
  title = {Controversies in diagnosis: contemporary debates in the diagnostic safety literature.},
  abstract = {<p>Since the 2015 publication of the National Academy of Medicine's (NAM) Improving Diagnosis in Health Care (Improving Diagnosis in Health Care. In: Balogh EP, Miller BT, Ball JR, editors. Improving Diagnosis in Health Care. Washington (DC): National Academies Press, 2015.), literature in diagnostic safety has grown rapidly. This update was presented at the annual international meeting of the Society to Improve Diagnosis in Medicine (SIDM). We focused our literature search on articles published between 2016 and 2018 using keywords in Pubmed and the Agency for Healthcare Research and Quality (AHRQ)'s Patient Safety Network's running bibliography of diagnostic error literature (Diagnostic Errors Patient Safety Network: Agency for Healthcare Research and Quality; Available from: https://psnet.ahrq.gov/search?topic=Diagnostic-Errors&f_topicIDs=407). Three key topics emerged from our review of recent abstracts in diagnostic safety. First, definitions of diagnostic error and related concepts are evolving since the NAM's report. Second, medical educators are grappling with new approaches to teaching clinical reasoning and diagnosis. Finally, the potential of artificial intelligence (AI) to advance diagnostic excellence is coming to fruition. Here we present contemporary debates around these three topics in a pro/con format.</p>},
  year = {2020},
  journal = {Diagnosis (Berl)},
  volume = {7},
  pages = {3-9},
  month = {01/2020},
  issn = {2194-802X},
  doi = {10.1515/dx-2019-0016},
  language = {eng},
}