By Mark Watts
As I gazed down on the blueprints for the new medical center the chief medical officer asked me if I wanted the signage to read Radiology or Medical Imaging? I was being given an opportunity to design a new medical center. As I looked over the design, I thought about the mistakes I had made in the past with new construction. Was I building the hospital of 15 years ago or the hospital of the future? Where would I put the Clinical Artificial Intelligence Center?
Historical precedents in radiology and laboratory medicine offer lessons for how to shepherd a new tool into the realm of safe and effective clinical use. Such accomplishments were due, in large part, to the gathering of relevant stakeholders under a single department. This approach ensured that the necessary clinical participants took the controls rather than ceding them to third-party developers.
Thus, to secure AI’s place in the annals of successful medical technologies, I would propose the establishment of the first departments of clinical AI.
This proposal is deeply rooted in the history of American medicine. On February 22, 1890, the first X-ray photograph was “accidently” generated at the University of Pennsylvania, although unbeknownst to its creators, Dr. Arthur Goodspeed and William Jennings.
When the significance of this emerging technology was finally appreciated after the discovery of Roentgen rays, Goodspeed began informally collaborating with surgeons to deploy the technology clinically. This quickly led to the first division, and subsequently, department, of radiology. Under the auspices of this department, clinicians, researchers, engineers, managers and ethicists worked together on a shared mission to pioneer various technologies and methods that are intrinsic to the way medicine is practiced today.
Within academic medicine, algorithms are currently developed in silos by researchers interested in the intersection of health care and machine learning. This has led to a panoply of published models trained on health data, yet only a handful have been prospectively evaluated on patients. In fact, when models have been prospectively evaluated on clinical outcomes, the results have frequently been unimpressive. In contrast, the same multibillion-dollar technology companies that exploit patterns in our digital behavior to sell advertising have now founded entire research programs around health AI. I would argue that the lack of clinical results is the byproduct of a lack of coherence, leadership and vision.
The failure to plan for success will lead to suboptimal deployment. If we in health care do not change course, we should expect that AI deployment to progress much the way the EHR revolution did, that is, mainly based on corporate and administrative benefits without requiring any demonstrable improvements in processes or outcomes for our patients or ourselves.
As in the development of other areas that required full departmental support, the decision to establish a department of clinical AI has several logistical and policy implications.
First, leveraging the premises of AI to improve health care represents challenges in several ways such as implementation issues and applied policies.
A chief mandate of department of clinical AI would be to make health centers “AI Ready.” These initiatives should lead to the development of models that will directly benefit the health of our patients, pioneer research that advances the field of clinical AI, focus on its integration into clinical workflows and foster educational programs and fellowships to ensure we are training current practitioners as well as the next generation of leaders in this field. In addition to these traditional tripartite roles, AI departments should also play an essential role in the implementation, utilization and enhancement of the infrastructures that underlie AI solutions. Central to this mission will be removing barriers to data access, and the proposed department would therefore assume partnered stewardship of the institution’s data as part of its mandate. While the role of information technology specialists in maintaining a health system’s computational infrastructure should not be subsumed, the department would be responsible for integration, research and production databases that can support its broader mission. By centralizing this role, we would finally overcome the chasms among ideas, development and effective deployment.
Second, these new departments will be instrumental as the United States financial and regulatory environments shift to acknowledge and incorporate AI’s potential to improve care. The tasks and benefits involved may require a modified model of reimbursement such as that in place for laboratory tests. But as has been the case for corporate AI (eg, Amazon), demonstrated improvements in clinical and financial outcomes could provide financial incentives to support the clinical use of AI and drive the increased deployment of predictive models. Market incentives will no doubt promote the proliferation of companies seeking to sell models to health systems. However, the need for model re-calibration precludes simply buying and deploying third-party models. Clinical AI departments will work to ensure that health systems are poised for safe implementations that are tailored to their specific patient populations, and that the necessary data analytics will be readily available for negotiating with payers.
Third, the clinical utilization of AI will require standardization such as the establishment of best practice guidelines regarding workflow integration design, performance assessment and model fairness. Appropriate models should be tested on held-out current data to assess performance and safety, and only then prospectively evaluated first without, and then with, deployment in terms of accuracy and impact on clinical end points. From there, regular re-assessments of model calibration must occur to ensure the relationship between the inputs and the outputs has not changed, and to re-fit the model where it has. This requirement for re-assessment and recalibration in a specific clinical context has become evident when researchers have attempted to apply one site’s data sets across institutional, system and geographic boundaries: AI applications can be sensitive to small input changes, and this potential fragility must be carefully and expertly monitored. While AI intrinsically manifests some degree of “black box” characteristics, the functionality and reasons for its results should be as transparent and explicable as possible so that clinicians can incorporate these modalities into their workflows.
Twenty years now into the 21st century, there is little question that AI will be a defining technology for the foreseeable future. We need visionary clinicians working with expert technical collaborators to establish the organizational structures requisite to translate technological progress into meaningful clinical outcomes. With the innumerable ways in which medicine could be improved, the hype around AI in health care will only be realized when the scattered champions of this movement emerge from their silos and begin formally working as a team under the same roof. Our patients are waiting for us to make use of these advances to improve their care, and every day wasted is a missed opportunity.
As I looked at the blueprints for Fountain Hills Medical Center, I wondered who will establish the first department of clinical AI?
Mark Watts is the enterprise imaging director at Fountain Hills Medical Center.