Statement on Augmented Intelligence in Medical Care

Artificial Intelligence (AI) is the ability of a machine to simulate intelligent behavior, a quality that enables an entity to function appropriately and with foresight in its environment. The term AI covers a range of methods, techniques and systems. Common examples of AI systems include, but are not limited to, natural language processing (NLP), computer vision and machine learning. In health care, as in other sectors, AI solutions may include a combination of these systems and methods.

In health care, a more appropriate term is “augmented intelligence”, an alternative conceptualization that more accurately reflects the purpose of such systems because they are intended to coexist with human decision-making. Therefore, in the remainder of this statement, AI refers to augmented intelligence.

An AI system utilizing machine learning employs an algorithm programmed to learn (“learner algorithm”) from data referred to as “training data.” The learner algorithm will then automatically adjust the machine learning model based on the training data. A “continuous learning system” updates the model without human oversight as new data is presented, whereas “locked learners” will not automatically update the model with new data. In health care, it is important to know whether the learner algorithm is eventually locked or whether the learner algorithm continues to learn once deployed into clinical practice in order to assess the systems for quality, safety, and bias. Being able to trace the source of training data is critical to understanding the risk associated with applying a health care AI system to individuals whose personal characteristics are significantly different than those in the training data set.

Health care AI generally describes methods, tools and solutions whose applications are focused on health care settings and patient care. In addition to clinical applications, there are many other applications of AI systems in health care including business operations, research, health care administration, and population health.

The concepts of AI and machine learning have quickly become attractive to health care organizations, but there is often no clear definition of terminology used. Many see AI as a technological panacea; however, realizing the promise of AI may have its challenges, since it might be hampered by evolving regulatory oversight to ensure safety and clinical efficacy, lack of widely accepted standards, liability issues, need for clear laws and regulations governing data uses, and a lack of shared understanding of terminology and definitions.

Some of the most promising uses for health care AI systems include predictive analytics, precision medicine, diagnostic imaging of diseases, and clinical decision support. Development in these areas is underway, and investments in AI have grown over the past several years [1]. Currently, health care AI systems have started to provide value in the realm of pattern recognition, NLP, and deep learning. Machine learning systems are designed to identify data errors without perpetuating them. However, health care AI systems do not replace the need for the patient-physician relationship. Such systems augment physician-provided medical care and do not replace it.

Health care AI systems must be, transparent, reproducible, and be trusted by both health care providers and patients. Systems must focus on users’ needs. Usability should be tested by participants who reflect similar needs and practice patterns of the end user, and systems must work effectively with people. Physicians will be more likely to accept AI systems that can be integrated into or improve their existing practice patterns, and also improve patient care.