Artificial intelligence (AI) is gaining high visibility in the realm of health care innovation. Broadly defined, AI is a field of computer science that aims to mimic human intelligence with computer systems. This mimicry is accomplished through iterative, complex pattern matching, generally at a speed and scale that exceed human capability. Proponents suggest, often enthusiastically, that AI will revolutionize health care for patients and populations. However, key questions must be answered to translate its promise into action.Maddox, TM, Rumsfeld, JS, Payne, PR. (2018). Questions for Artificial Intelligence in Health Care. JAMA. Published online December 10, 2018. doi:10.1001/jama.2018.1893.
The questions and follow-up responses presented in the article are useful, highlighting the nuance that is often ignored in mainstream pieces that tend to focus on the extreme potential of the technology (i.e. what this might one day be like) rather than the more subtle implications that we need to consider today. The following text is verbatim from the article:
- What are the right tasks for AI in healthcare? AI is best used when the primary task is identifying clinically useful patterns in large, high-dimensional data sets. AI is most likely to succeed when used with high-quality data sources on which to “learn” and classify data in relation to outcomes. However, most clinical data, whether from electronic health records (EHRs) or medical billing claims, remain ill-defined and largely insufficient for effective exploitation by AI techniques.
arethe right data for AI? AI is most likely to succeed when used with high-quality data sources on which to “learn” and classify data in relation to outcomes. However, most clinical data, whether from electronic health records (EHRs) or medical billing claims, remain ill-defined and largely insufficient for effective exploitation by AI techniques.
- What is the right evidence standard for AI? Innovations in medications and medical devices are required to undergo
extensiveevaluation, often including randomized clinical trials and postmarketing surveillance, to validate clinical effectiveness and safety. If AI is to directly influence and improve clinical care delivery, then an analogous evidence standard is needed to demonstrate improved outcomes and a lack of unintended consequences.
- What are the right approaches for integrating AI into clinical care? Even after the correct tasks, data, and evidence for AI are addressed, realization of its potential will not occur without effective integration into clinical care. To do so requires that clinicians develop a facility with interpreting and integrating AI-supported insights in their clinical care.