Like many sectors, health care has benefited from the rising use of artificial intelligence, but it has sometimes happened at the expense of minority patients. In fact, health care AI might amplify and worsen disparities (racial/ethnic and others) because the data sources that "teach" AI are not representative and/or are based on data from current unequal care, says University of Michigan law professor Nicholson Price, who also is a member of U-M's Institute for Healthcare Policy & Innovation. In a recent Science article, Price and colleagues Ana Bracic of Michigan State University and Shawneequa Callier of George Washington University say these disparities are happening despite efforts in medicine by physicians and health systems trying strategies focused on diverse workforce recruitment or implicit bias training. What is an example of anti-minority culture? There are depressingly many examples of cultures that include deeply embedded biases against minoritized populations (that is, populations constructed as minorities by a dominant group).

Artificial intelligence and medical algorithms are deeply intertwined with our modern health care system. These technologies mimic the thought processes of doctors to make medical decisions and are designed to help providers determine who needs care. But one big problem with artificial intelligence is that it very often replicate the biases and blind spots of the humans who create them. Researchers and physicians have warned that algorithms used to determine who gets kidney transplants, heart surgeries and breast cancer diagnoses display racial bias. Those problems can lead to detrimental care that, in some cases, can jeopardize the health of millions of patients.

As medicine continues to test automated machine learning tools, many hope that low-cost support tools will help narrow care gaps in countries with constrained resources. But new research suggests it's those countries that are least represented in the data being used to design and test most clinical AI -- potentially making those gaps even wider. Researchers have shown that AI tools often fail to perform when used in real-world hospitals. It's the problem of transferability: An algorithm trained on one patient population with a particular set of characteristics won't necessarily work well on another. Those failures have motivated a growing call for clinical AI to be both trained and validated on diverse patient data, with representation across spectrums of sex, age, race, ethnicity, and more.

As the medical community's understanding of the application of augmented intelligence (AI) in health care grows, there remains the question of how AI--often called artificial intelligence--should be incorporated into physician training. The term augmented intelligence is preferred because it recognizes the enhancement, rather than replacement, of human capabilities. A webinar produced by the AMA Accelerating Change in Medical Education Consortium featured a presentation by Cornelius A. James, MD, assistant professor of internal medicine and pediatrics at University of Michigan Medical School. He is also the principal investigator of "Data Augmented, Technology Assisted Medical Decision Making and Diagnosis (DATA-MD): A Novel Curriculum." James outlined the initial steps medical educators should be taking to work AI into their curricula. "Each organization or system will have their own unique barriers, but there are some barriers that are a bit more generalizable," Dr. James said.

Computers can sometimes show a surprising lack of common sense. That's why asking the right questions, using the right data and guarding against the introduction of bias are keys to making augmented intelligence (AI) a valuable decision-support tool that is often called artificial intelligence. "Your clinicians can program the protocol you want for the alerts and predictions you want," said Ben Maisano, chief digital and innovation officer for New Jersey's Atlantic Health System, an AMA Health System Program member. "If we're trying to reduce hospital stays, or we're trying to understand if our accountable care organization is profitable, or if social determinants of health data helps us better take care of someone, or predict a risk for readmission, you've got to understand what problems are you trying to solve and mapping that to the outcome you want--and then go fill in the blanks," said Maisano, who is a co-founder of CareDox, a platform that connected schools, pediatric practices and families in 38 states. Maisano spoke during a virtual meeting of the AMA Insight Network that covered how to get a health care AI program up and running, and how to use it properly.

When Microsoft CEO Satya Nadella spoke to investors Monday about his company's plan to acquire speech-recognition specialist Nuance for $16 billion, he emphasized the importance of artificial intelligence in health care. Nuance's software listens to doctor-patient conversations and transcribes speech into organized digitized medical notes. This helps explain the hefty price tag, even as voice recognition has become commoditized and now comes packaged with every smartphone and laptop. But Microsoft may also see much broader potential for Nuance's technology. Gregg Pessin, an analyst with Gartner, says the deal gives Microsoft "an entry point into the health care industry, and a huge customer base already running this stuff."

The technological capacity exists to use augmented intelligence (AI) algorithms and tools to transform health care, but real challenges remain in ensuring that tools are developed, implemented and maintained responsibly, according to a JAMA Viewpoint column, "Artificial Intelligence in Health Care: A Report From the National Academy of Medicine." "The challenges [to use of AI] are unrealistic expectations, biased and nonrepresentative data, inadequate prioritization of equity and inclusion, the risk of exacerbating health care disparities, low levels of trust, uncertain regulatory and tort environments and inadequate evaluation before scaling narrow AI," the opinion piece concludes. AI is often called artificial intelligence. The Viewpoint column was co-written by two co-authors of the National Academy of Medicine (NAM) report, AI in Healthcare: The Hope, The Hype, The Promise, The Peril. The 2019 NAM publication--a mix of caution and guarded optimism--presents what's known about AI in the clinical setting and serves as a guide on how the field can move forward responsibly and in a way that benefits all patients.

Physicians who hear the term artificial intelligence or augmented intelligence (AI) may conjure images of robots or computer algorithms replacing some of the care they provide to patients. Explore insights from the AMA's updated study on physicians' motivations and requirement for adopting digital clinical tools. But--just as the AMA is advocating in this emerging field--technologists and clinicians designing AI in the health care setting who gathered for a panel discussion said they are aiming to enhance physician intelligence, not replace it. They also stressed that physicians' input is crucial during the design process to create AI that clinicians will adopt to build trust and a robust evidence base for these tools. At an AMA moderated discussion at the Institute of Electrical and Electronics Engineers Engineering in Medicine and Biology Society (IEEE EMBS) conference on health care innovation and point-of-care technologies, four academic and industry leaders in health care AI shared some of the successes they've had, as well as what they hope to accomplish in the future. Watch the 80-minute discussion, "Healthcare AI for the Clinical Environment: Design, validation and implementation of technologies that can augment rather than replace clinicians," which was held prior to the onset of the COVID-19 pandemic.

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Augmented intelligence (AI) promises to be a transformational force in health care, especially within primary care. Experts outline ways that innovations driven by AI--often called artificial intelligence--can aid rather than subvert the patient-physician relationship. "AI implemented poorly risks pushing humanity to the margins; done wisely, AI can free up physicians' cognitive and emotional space for patients, and shift the focus away from transactional tasks to personalized care," wrote the authors of an article published in the Journal of General Internal Medicine. The AMA is committed to helping physicians harness AI in ways that safely and effectively improve patient care. The authors--Steven Y. Lin, MD, and Megan R. Mahoney, MD, associate clinical professor of medicine and clinical professor of medicine, respectively, in the Division of Primary Care and Population Health at Stanford University School of Medicine, and AMA vice president of professional satisfaction Christine A. Sinsky, MD--reviewed promising AI inventions in 10 distinct problem areas.