Like any technology, AI has just as much potential for harm as for good. Some experts predict that once the excitement and novelty of AI-assisted clinical procedures wear off, problems will begin to pop up. For example, few of the 130 AI devices the U.S. Food and Drug Administration (FDA) has approved over the past couple of years have been tested in clinical trials. As a result, AI could miss a tumor during a CT scan, recommend the wrong medication, give a hospital bed to a patient who needs it less than another and produce many other errors. And if there is a fundamental flaw in the programming, it could misdiagnose thousands of patients instead of just one.

Artificial intelligence has made its way into the lives of average consumers. Even smartphones have some form of artificial intelligence baked into them. We are slowly, but surely becoming dependent on artificial intelligence. We've long relied on technology to help us become more efficient in our craft. Sculptors can now use 3D-printing technology, architects use augmented reality to get a real-time preview of their project, and real estate agents use virtual reality to enable prospective buyers to experience a virtual tour of the home they intend to buy.

AI is already on its way to transforming healthcare delivery and improving patient outcomes. However, while AI, Machine Learning, and Robotics are all designed to reduce human error and increase the predictability of patient care, they also create new risks across the healthcare liability landscape. In a situation where a healthcare provider uses AI to treat a patient who has a less than a desired outcome (or even simply an unanticipated one), we anticipate liability suits against those healthcare providers, healthcare systems, AI software companies, and robotic device manufacturers. In this post, we will consider what happens when lawsuits get ahead of science, insurance considerations in this new liability landscape, and possible modifications to legal doctrine to address this new science. What makes AI so compelling is its use of predictive, learning algorithms (Machine Learning) to improve the precision of the practice of medicine.

Cognitive and ethical biases: Humans exhibit a variety of biases which interfere with reasoning, including cognitive biases and ethical biases such as in-group bias. In general, we expect direct answers to questions to reflect primarily Type 1 thinking (fast heuristic judgment), while we would like to target a combination of Type 1 and Type 2 thinking (slow, deliberative judgment). Lack of domain knowledge: We may be interested in questions that require domain knowledge unavailable to people answering the questions. For example, a correct answer to whether a particular injury constitutes medical malpractice may require detailed knowledge of medicine and law. In some cases, a question might require so many areas of specialized expertise that no one person is sufficient, or (if AI is sufficiently advanced) deeper expertise than any human possesses.