Why is AI adoption in health care lagging?

Artificial intelligence (AI) technologies have improved rapidly over the past decade,1 largely driven by advances in machine learning, which is closely related to data science and statistical prediction.2 Several aspects of the health care system involve prediction, including diagnosis, treatment, administration, and operations. This connection between machine learning's capabilities and needs of the health care system has led to widespread speculation that AI will have a large impact on health care. For instance, Eric Topol's "Deep Medicine: How Artificial Intelligence can make Health Care Human Again," highlights AI's potential to improve the lives of doctors and patients. The progress and promise of clinical AI algorithms range from image-based diagnosis in radiology and dermatology to surgery, and from patient monitoring to genome interpretation and drug discovery.