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The Clinical Artificial Intelligence Department: a Prerequisite for Success Open access Communication BMJ Health Care Inform: first published as 10.1136/bmjhci-2020-100183 on 15 July 2020. Downloaded from The clinical artificial intelligence department: a prerequisite for success Christopher V. Cosgriff,1 David J. Stone,2,3 Gary Weissman,4,5 Romain Pirracchio,6 Leo Anthony Celi 7,8 To cite: Cosgriff CV, Stone DJ, Is artificial intelligence (AI) on track to usurp its creators, Goodspeed and Jennings.8 When Weissman G, et al. The clinical the electronic health record (EHR) as the the significance of this emerging technology artificial intelligence department: a prerequisite for success. most disappointing application of technology was finally appreciated after the discovery of BMJ Health Care Inform within medicine? The medical literature Roentgen rays, Goodspeed began informally 2020;27:e100183. doi:10.1136/ is increasingly populated with perspective collaborating with surgeons to deploy the bmjhci-2020-100183 pieces lauding the transformative nature of technology clinically. This quickly led to the AI and forecasting an unforeseen disruption first division, and subsequently, department, Received 21 May 2020 1 2 Accepted 17 June 2020 in the way we are practising. However, the of radiology. Under the auspices of this depart- reality of the available evidence increasingly ment, clinicians, researchers, engineers, leaves little room for optimism. As a result, managers and ethicists worked together on a there is a stark contrast between the lack of shared mission to pioneer various technolo- concrete penetration of AI in medical prac- gies and methods that are intrinsic to the way tice, and the expectations set by the presence medicine is practised today. of AI in our daily life.3 But medical AI need Within academic medicine, algorithms are not follow the path of the EHR as a clinical currently developed in silos by researchers copyright. tool that to many led to more workflow woes interested in the intersection of health- than it was intended to fix.4–6 care and machine learning. This has led to As Atul Gawande so eloquently put, “… a panoply of published models trained on we’ve reached a point where people in the health data, yet only a handful have been medical profession actively, viscerally, volubly prospectively evaluated on patients. In fact, hate their computers”.7 If we are going to when models have been prospectively eval- unavoidably add some disruption to workflow uated on clinical outcomes, the results with AI, it should be as painless as possible to have frequently been unimpressive.9–12 In http://informatics.bmj.com/ circumvent further, or perhaps even reduce, contrast, the same multibillion-dollar tech- clinician burnout. We believe that this will nology companies that exploit patterns in our require the combined and cross-disciplinar y digital behaviour to sell advertising have now expertise of an organised and dedicated clin- founded entire research programmes around ical AI department. health AI. We would argue that the lack of Historical precedents in radiology and clinical results is the byproduct of a lack of laboratory medicine offer lessons for how to coherence, leadership and vision. Hence, steward a new tool into the realm of safe and unless we change course, we should expect on September 24, 2021 by guest. Protected effective clinical use. Such accomplishments that AI deployment in healthcare will prog- were due, in large part, to the gathering of ress much the way the EHR revolution did relevant stakeholders under a single depart- before it, that is, mainly based on corporate ment. This approach ensured that the neces- and administrative benefits without requiring © Author(s) (or their sary clinical participants took the reins rather any demonstrable improvements in processes employer(s)) 2020. Re- use than ceding them to third-party developers. or outcomes for our patients or ourselves. permitted under CC BY-NC. No commercial re- use. See rights Thus, to secure AI’s place in the annals of As in the development of other areas that and permissions. Published by successful medical technologies, we propose required full departmental support, the deci- BMJ. the establishment of the first departments of sion to establish a department of clinical AI For numbered affiliations see clinical AI. has several logistical and policy implications. end of article. This proposal is deeply rooted in the history First, leveraging the premises of AI to Correspondence to of American medicine. In 1890, the first improve healthcare represents challenges Dr Leo Anthony Celi; X- ray image was generated at the University in a number of ways such as implementa- LCeli@ mit. edu of Pennsylvania, although unbeknownst to tion issues and applied policies. Therefore, Cosgriff CV, et al. BMJ Health Care Inform 2020;27:e100183. doi:10.1136/bmjhci-2020-100183 1 Open access BMJ Health Care Inform: first published as 10.1136/bmjhci-2020-100183 on 15 July 2020. Downloaded from copyright. http://informatics.bmj.com/ Figure 1 Medical artificial intelligence (AI) departments will provide the structure by which institutions can become AI Ready. a chief mandate of department of clinical AI would be integration, research and production databases that can to make health centres AI Ready, a concept we illustrate support its broader mission. By centralising this role, we in figure 1. These initiatives should lead to the develop- would finally overcome the chasms among ideas, develop- ment of models that will directly benefit the health of our ment and effective deployment. patients, pioneer research that advances the field of clin- Second, these new departments will be instrumental as on September 24, 2021 by guest. Protected ical AI, focus on its integration into clinical workflows and our country’s financial and regulatory environments shift foster educational programmes and fellowships to ensure to acknowledge and incorporate AI’s potential to improve we are training current practitioners as well as the next care. The tasks and benefits involved may require a modi- generation of leaders in this field. In addition to these fied model of reimbursement such as that in place for traditional tripartite roles, AI departments should also laboratory tests. But as has been the case for corporate play an essential role in the implementation, utilisation (eg, Amazon) AI, demonstrated improvements in clinical and enhancement of the infrastructures that underlie and financial outcomes could provide financial incentives AI solutions. Central to this mission will be removing to support the clinical use of AI and drive the increased barriers to data access, and the proposed department deployment of predictive models. Market incentives would therefore assume partnered stewardship of the will no doubt promote the proliferation of companies institution’s data as part of its mandate. While the role seeking to sell models to health systems. However, the of information technology specialists in maintaining a need for model re-calibration precludes simply buying health system’s computational infrastructure should not and deploying third- party models.13 Clinical AI depart- be subsumed, the department would be responsible for ments will work to ensure that health systems are poised 2 Cosgriff CV, et al. BMJ Health Care Inform 2020;27:e100183. doi:10.1136/bmjhci-2020-100183 Open access BMJ Health Care Inform: first published as 10.1136/bmjhci-2020-100183 on 15 July 2020. Downloaded from for safe implementations that are tailored to their specific 7Laboratory for Computational Physiology, Harvard- MIT Division of Health Sciences patient populations, and that the necessary data analytics and Technology, Cambridge, Massachusetts, USA 8Division of Pulmonary Critical Care and Sleep Medicine, Beth Israel Deaconess will be readily available for negotiating with payers. Medical Center, Boston, Massachusetts, USA Third, the clinical utilisation of AI will require stan- dardisation such as the establishment of best practice Twitter Christopher V. Cosgriff @cosgriffc and Leo Anthony Celi @MITCriticalData guidelines regarding workflow integration design, perfor- Contributors CVC produced the original draft under guidance from LAC. DS, GW mance assessment and model fairness. Appropriate and RP then gave input and provided edits. LAC oversaw the incorporation of these models should be tested on held-out current data to edits, led the discussions around the principle concepts and approved the final assess performance and safety, and only then prospec- draft. tively evaluated first without, and then with, deployment Funding LAC is funded by the National Institute of Health through NIBIB R01 in terms of accuracy and impact on clinical end points. EB017205. From there, regular re- assessments of model calibra- Competing interests None declared. tion must occur to ensure the relationship between the Patient consent for publication Not required. inputs and the outputs has not changed, and to re- fit Provenance and peer review Not commissioned; externally peer reviewed. the model where it has. This requirement for re- assess- Open access This is an open access article distributed in accordance with the ment and recalibration in a specific clinical context has Creative Commons Attribution Non Commercial (CC BY- NC 4.0) license, which become evident when researchers have attempted to permits others to distribute, remix, adapt, build upon this work non-commercially , apply one site’s data sets across institutional, system or and license their derivative works on
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