The clinical artificial intelligence department: a prerequisite for success

• Published in: BMJ health & care informatics Vol. 27; no. 1; p. e100183
• Main Authors: Cosgriff, Christopher V., Stone, David J., Weissman, Gary, Pirracchio, Romain, Celi, Leo Anthony
• Format: Journal Article
• Language: English
• Published: London BMJ Publishing Group LTD 01-07-2020; BMJ Publishing Group
• Subjects: Artificial intelligence; Clinical outcomes; Clinical practice guidelines; Communication; Departments; Electronic health records; Health informatics; Information technology; Laboratories; Medicine; Patients
• ISSN: 2632-1009; 2632-1009
• DOI: 10.1136/bmjhci-2020-100183

[...]there is a stark contrast between the lack of concrete penetration of AI in medical practice, and the expectations set by the presence of AI in our daily life.3 But medical AI need not follow the path of the EHR as a clinical tool that to many led to more workflow woes than it was intended to fix.4–6 As Atul Gawande so eloquently put, “… we’ve reached a point where people in the medical profession actively, viscerally, volubly hate their computers”.7 If we are going to unavoidably add some disruption to workflow with AI, it should be as painless as possible to circumvent further, or perhaps even reduce, clinician burnout. [...]when models have been prospectively evaluated on clinical outcomes, the results have frequently been unimpressive.9–12 In contrast, the same multibillion-dollar technology companies that exploit patterns in our digital behaviour to sell advertising have now founded entire research programmes around health AI. [...]unless we change course, we should expect that AI deployment in healthcare will progress much the way the EHR revolution did before it, that is, mainly based on corporate and administrative benefits without requiring any demonstrable improvements in processes or outcomes for our patients or ourselves. [...]the clinical utilisation of AI will require standardisation such as the establishment of best practice guidelines regarding workflow integration design, performance assessment and model fairness.