Avoiding missed opportunities in AI for radiology

In the last decade, the development of Deep Learning and its variants, based on the application of artificial neural networks, has reinvigorated Artificial Intelligence (AI). As a result, many new applications of AI in medicine, especially Radiology, have been introduced. This resurgence in AI, and its diverse clinical and nonclinical applications throughout healthcare, requires a thorough understanding to reap the potential benefits and avoid the potential pitfalls.

To realize the full potential of AI in medicine, a highly coordinated approach should be undertaken to select, support and finance more highly focused AI projects. By studying and understanding the successes and failures, and strengths and limitations, of AI in Radiology, it is possible to seek and develop the most clinically relevant AI algorithms. The authors have reviewed their clinical practice regarding the use of AI to determine applications in which AI can add both clinical and remunerative benefits.

Review of our policies and applications regarding AI in the Department of Radiology emphasized that, at the time of this writing, AI has been useful in the detection of specific clinical entities for which the AI algorithms have been designed. In addition to helping to reduce diagnostic errors, AI offers an important opportunity to prioritize positive cases, such as pulmonary embolism or intracranial hemorrhage. It has become apparent that the detection of certain conditions, such as incidental and unsuspected cerebral aneurysms can be used to initiate a variety of patient-oriented activities. Finding an unsuspected brain aneurysm is not only of clinical importance to the patient, but the required clinical workup and management of the patient can help generate reimbursement that helps defray the cost of AI implementations. A program for screening, clinical management, and follow-up, facilitated by the AI detection of incidental brain aneurysms, has been implemented at our multi-hospital healthcare system.

We feel that it is possible to avoid missed opportunities for AI in Radiology and create AI tools to enhance medical wisdom and improve patient care, within a fiscally responsive environment.