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 ClosedLoop.ai, 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.

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Published: 18 May 2021 18 May 2021

• artificial intelligence

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.

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Published: 26 April 2021 26 April 2021

• COVID-19
• AI/ML

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.

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Published: 13 April 2021 13 April 2021

• medical devices
• artificial intelligence

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.

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Published: 23 March 2021 23 March 2021

• COVID-19
• machine learning