CMS announces winner of AI challenge

The Centers for Medicare and Medicaid Services (CMS) announced the winner and runner-up in its Artificial Intelligence (AI) Health Outcomes Challenge. The Challenge was conducted by CMS in collaboration with the American Academy of Family Physicians, and aimed to demonstrate how AI tools such as deep learning and neural networks can accelerate solutions for predicting patient health outcomes. The winner was, located in Austin, Texas, and the runner-up was Geisinger in Danville, Pennsylvania.


Participants were tasked with developing innovative strategies to explain AI-derived predictions to front-line clinicians and patients, and to increase use of AI-enhanced data feedback for quality improvement. In Stage 1, participants were asked to use AI to predict unplanned hospital and skilled nursing facility (SNF) admissions and adverse events. In Stage 2, participants were to use AI to predict 12-month mortality for Medicare patients, as well as unplanned hospital and SNF admissions and adverse events.

FTC cautions that AI can reproduce biases

In a blog post on April 19, 2021, the Federal Trade Commission (FTC) cautioned that efforts to harness the benefits of artificial intelligence (AI) should be coupled with safeguards to avoid introducing bias. As an example, the FTC cited a recent article in the Journal of the American Medical Informatics Association which warned that AI used to guide resource allocation for COVID-19 patients were fraught with bias. For example, using healthcare spending as a proxy for disease burden exacerbated inequalities arising from barriers to care for Black patients.


The FTC advises companies, including healthcare organizations, to examine data sets used for AI models to determine if the data is missing information from some populations. It also suggests testing the algorithm before use and periodically thereafter to watch out for discrimination on the basis of race, gender or other protected class.

FDA authorizes marketing of device using AI to highlight potential lesions during colonoscopy

On April 9, 2021, the Food and Drug Administration (FDA) authorized marketing of the first device which uses artificial intelligence based on machine learning (AI/ML) to assist in detecting lesions during colonoscopy. The approval of the GI Genius device relied on results of a multicenter study in Italy that compared identification of lab-confirmed adenomas or carcinomas through colonoscopy using the device, versus standard colonoscopy. The GI Genius device identified suspicious lesions in 55.1% of patients compared to 42% with standard colonoscopy. The device uses AI algorithms to identify regions of interest as the patient is undergoing colonoscopy and generates markers that look like green squares superimposed on the video from the endoscope camera. The device is not intended to provide diagnostic assessments or replace lab sampling, but to alert the clinician so that suspect areas can be examined in real time.

FDA issues EUA for first machine learning COVID screening device

On March 19, 2021, the Food and Drug Administration (FDA) issued an emergency use authorization (EUA) for the first machine learning COVID-19 screening device. The Tiger Tech COVID Plus Monitor is to be used by trained personnel for screening persons without COVID symptoms or fever. It is a screening tool only, not a diagnostic device.  The device is an armband with embedded light sensors and a small computer processor. The sensors obtain pulsatile signals for three to five minutes. The processor extracts key features of the signals for analysis by a probabilistic machine learning model trained to make predictions on whether the individual is demonstrating certain biomarkers, such as hypercoagulation. The Tiger Tech monitor was evaluated in a hospital study and a school study, and identified COVID-19 positive individuals 98.6% of the time, and COVID-19 negative individuals 94.5% of the time.

HHS AI strategy signals new grantmaking focus on AI

The Department of Health and Human Services (HHS) has published its Artificial Intelligence (AI) Strategy, which will be supported by a new HHS AI Council. The AI Council will establish a Community of Practice of AI practitioners within HHS, to include data scientists, machine learning experts and other technologists, as well as health scientists who employ AI within their projects and organizations. An intriguing suggestion in the AI strategy is that HHS will encourage use of AI in grant-funded projects, including advancing biomedicine through AI-enabled insights and predictive analytics in public health surveillance.

FDA Considers Adapting Regulatory Approval Processes for Artificial Intelligence/Machine Learning

Over the last several years, the Food & Drug Administration (FDA) has been grappling with how to tailor its regulatory approval pathways for Software as a Medical Device (SaMD). SaMD is defined as software intended to be used for one or more medical purposes that performs these purposes without being part of a hardware medical device. The FDA utilizes different pathways for manufacturers prior to distribution of the SaMD, depending on risk. The introduction of artificial intelligence (AI) and machine learning (ML) technologies further complicates this evaluation. The FDA published a discussion paper in April 2019 describing a proposed regulatory framework for modifications to AI/ML software.

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FDA announces action plan for AI/ML software as a medical device

In April 2019, the Food and Drug Administration (FDA) published a proposed regulatory framework for evaluating modifications for artificial intelligence/machine learning (AI/ML) software as a medical device (SaMD). AI/ML technologies in SaMD present unique issues, because the technology is intended to learn from real-world feedback and incorporate improvements into the SaMD algorithms. Since publishing the proposed framework, the FDA utilized workshops, publications and other means to obtain stakeholder feedback from device manufacturers and the public. The recently published action plan describes the agency’s intended actions incorporating this feedback.

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