Health products powered by artificial intelligence, or AI, are streaming into our lives, from virtual doctor apps to wearable sensors and drugstore chatbots. IBM boasted that its AI could "outthink cancer." Others say computer systems that read X-rays will make radiologists obsolete. "There's nothing that I've seen in my 30-plus years studying medicine that could be as impactful and transformative" as AI, said Dr. Eric Topol, a cardiologist and executive vice president of Scripps Research in La Jolla, Calif. AI can help doctors interpret MRIs of the heart, CT scans of the head and photographs of the back of the eye, and could potentially take over many mundane medical chores, freeing doctors to spend more time talking to patients, Topol said.

In a move that could help win over some skeptics about the value and efficacy of AI in clinical care, The American Diabetes Association, in its new set of clinical standards, recognizes the use of autonomous artificial intelligence for the screening of some medical conditions. WHY IT MATTERS The ADA's new 2020 Standards of Medical Care in Diabetes includes language noting that "AI systems that detect more than mild diabetic retinopathy and diabetic macular edema authorized for use by the FDA represent an alternative to traditional screening approaches." The clinical standards – published earlier this month in the peer-reviewed journal Diabetes Care – represent a new source for evidence-based best practices, consulted by hospitals and health systems, physicians, insurers and quality organizations. While acknowledging that autonomous AI can be an alternative to traditional screening, however, the ADA specifies that it feels the "benefits and optimal utilization of this type of screening have yet to be fully determined." In addition, it cautions that "artificial intelligence systems should not be used for patients with known retinopathy, prior retinopathy treatment, or symptoms of vision impairment."

AI has enormous potential when it comes to the healthcare field, capable of improving diagnoses and finding new, more effective drugs. However, as a piece in Scientific American recently discussed, the speed with which AI is penetrating the healthcare field also opens up many new challenges and risks. Over the course of the past five years, the US Food and Drug Administration has approved over 40 different AI products. However, as reported by Scientific American, none of the products cleared for sale in the US have had their performance evaluated in randomized controlled clinical trials. Many AI medical tools don't even require approval by the FDA.

Health products powered by artificial intelligence, or AI, are streaming into our lives, from virtual doctor apps to wearable sensors and drugstore chatbots. IBM boasted that its AI could "outthink cancer." Others say computer systems that read X-rays will make radiologists obsolete. "There's nothing that I've seen in my 30-plus years studying medicine that could be as impactful and transformative" as AI, said Dr. Eric Topol, a cardiologist and executive vice president of Scripps Research in La Jolla, Calif. AI can help doctors interpret MRIs of the heart, CT scans of the head and photographs of the back of the eye, and could potentially take over many mundane medical chores, freeing doctors to spend more time talking to patients, Topol said. Even the Food and Drug Administration ― which has approved more than 40 AI products in the past five years ― says "the potential of digital health is nothing short of revolutionary."

Successful deployment of machine learning algorithms in healthcare requires careful assessments of their performance and safety. To date, the FDA approves locked algorithms prior to marketing and requires future updates to undergo separate premarket reviews. However, this negates a key feature of machine learning--the ability to learn from a growing dataset and improve over time. This paper frames the design of an approval policy, which we refer to as an automatic algorithmic change protocol (aACP), as an online hypothesis testing problem. As this process has obvious analogy with noninferiority testing of new drugs, we investigate how repeated testing and adoption of modifications might lead to gradual deterioration in prediction accuracy, also known as ``biocreep'' in the drug development literature. We consider simple policies that one might consider but do not necessarily offer any error-rate guarantees, as well as policies that do provide error-rate control. For the latter, we define two online error-rates appropriate for this context: Bad Approval Count (BAC) and Bad Approval and Benchmark Ratios (BABR). We control these rates in the simple setting of a constant population and data source using policies aACP-BAC and aACP-BABR, which combine alpha-investing, group-sequential, and gate-keeping methods. In simulation studies, bio-creep regularly occurred when using policies with no error-rate guarantees, whereas aACP-BAC and -BABR controlled the rate of bio-creep without substantially impacting our ability to approve beneficial modifications.

Health products powered by artificial intelligence, or AI, are streaming into our lives, from virtual doctor apps to wearable sensors and drugstore chatbots. IBM boasted that its AI could "outthink cancer." Others say computer systems that read X-rays will make radiologists obsolete. "There's nothing that I've seen in my 30-plus years studying medicine that could be as impactful and transformative" as AI, said Eric Topol, a cardiologist and executive vice president of Scripps Research in La Jolla, Calif. AI can help doctors interpret MRIs of the heart, CT scans of the head and photographs of the back of the eye, and could potentially take over many mundane medical chores, freeing doctors to spend more time talking to patients, Topol said.

Personalizing drug prescriptions in cancer care based on genomic information requires associating genomic markers with treatment effects. This is an unsolved challenge requiring genomic patient data in yet unavailable volumes as well as appropriate quantitative methods. We attempt to solve this challenge for an experimental proxy for which sufficient data is available: 42 drugs tested on 1018 cancer cell lines. Our goal is to develop a method to identify the drug that is most promising based on a cell line's genomic information. For this, we need to identify for each drug the machine learning method, choice of hyperparameters and genomic features for optimal predictive performance. We extensively compare combinations of gene sets (both curated and random), genetic features, and machine learning algorithms for all 42 drugs. For each drug, the best performing combination (considering only the curated gene sets) is selected. We use these top model parameters for each drug to build and demonstrate a Drug Recommendation System (Dr.S). Insights resulting from this analysis are formulated as best practices for developing drug recommendation systems. The complete software system, called the Cell Line Analyzer, is written in Python and available on github.

The nation's leading association that fights against diabetes released a new set of clinical standards that for the first time include the use of autonomous artificial intelligence (AI). The American Diabetes Association (ADA)'s 2020 Standards of Medical Care in Diabetes states that, "AI systems that detect more than mild diabetic retinopathy and diabetic macular edema authorized for use by the FDA represent an alternative to traditional screening approaches." To date, IDx-DR is the first and only FDA-authorized autonomous AI diagnostic system for the detection of diabetic retinopathy and macular edema. It is currently in use at a number of large health systems that each serve tens of thousands of people with diabetes and have struggled to implement diabetic retinopathy eye exams at scale for their large diabetes population. "The ADA's inclusion of our technology in its Standards of Care marks a significant move toward mainstream adoption of autonomous AI in clinical care," said Michael Abramoff, MD, PhD, Founder and Executive Chairman at IDx. "Our early customers are visionary leaders who foresaw that autonomous AI would one day become a standard of care for diabetic retinopathy screening, and taking that leap is paying off for them. Already, health systems that are using IDx-DR have experienced significant improvements in accessibility, efficiency and compliance rates, unleashing massive potential for cost savings and improved patient outcomes."

An artificial intelligence algorithm developed by Rochester, Minn.-based Mayo Clinic and cardiac monitoring startup Eko to analyze ECG data for evidence of reduced left ventricular ejection fraction has been designated a "breakthrough device" by the FDA. The algorithm reads ECG data collected by Eko's digital stethoscope to measure LVEF, which refers to the amount of blood pumped out of the heart's left ventricle and can indicate heart failure. The breakthrough device label, presented to technology with potential to address unmet clinical needs, will speed up regulatory review of the algorithm. Eko and Mayo Clinic's partnership to develop the AI algorithm began in late 2018. Since then, studies have shown that the algorithm-equipped stethoscope achieves significant accuracy in detecting low ejection fraction.

Approvals for AI-based healthcare products are streaming in from regulators around the globe, with medical imaging leading the way. It's just the start of what's expected to become a steady flow as submissions rise and the technology becomes better understood. More than 90 medical imaging products using AI are now cleared for clinical use, thanks to approvals from at least one global regulator, according to Signify Research Ltd., a U.K. consulting firm in healthcare technology. Regulators in Europe and the U.S. are leading the pace. Each has issued about 60 approvals to date.