HAI Policy Briefs
July 2021
Improving AI Software for Healthcare Diagnostics
One of the most promising uses of artificial intelligence is in radiology, the medical specialization that uses imaging technology to diagnose and treat disease. AI holds great promise to improve traditional medical imaging methods like CT, MRI, and X-ray by offering computational capabilities that process images with greater speed and accuracy, automatically recognizing complex patterns to assess a patient’s health. This sophisticated software needs more robust evaluation methods to reduce risk to the patient, to establish trust, and to ensure wider adoption.
Key Takeaways
➜ AI-based diagnostics show great promise to improve traditional medical imaging methods, such as CT scans, MRIs, and X-rays. These algorithms offer computational capabilities that process images with greater speed and accuracy than traditional methods and can improve patient outcomes for millions.
➜ Current proposals for regulatory frameworks do not fully address the necessity to build trust in these systems due to the confusion between the algorithm in question and the task it is designed to perform, inadequate establishment of standard-setting bodies, and insufficient rigor in the evaluation and development process.
➜ Policymakers should turn to medical societies for the clinical definitions of diagnostic tasks. These groups should extend performance assessments beyond simply testing for accuracy.
Authors
David B. Larson - Stanford University
Daniel L. Rubin - Stanford University
Curtis P. Langlotz - Stanford University