Consumer Financial Protection Bureau (CFPB) issues policy statement on credit decisions based on complex algorithms. On May 26, the CFPB issued Circular 2022-03, which addresses an important question about algorithmic decision-making: "When creditors make credit decisions based on complex algorithms that prevent creditors from accurately identifying the specific reasons for denying credit or taking other adverse actions, do these creditors need to comply with the Equal Credit Opportunity Act's requirement to provide a statement of specific reasons to applicants against whom adverse action is taken?" The Circular says yes, compliance with ECOA and Regulation B is required even if complex algorithms (including AI and machine learning) make it difficult to accurately identify the specific reasons for taking the adverse action. Further, the Circular makes clear that those laws "do not permit creditors to use complex algorithms when doing so means they cannot provide the specific and accurate reasons for adverse actions." White House executive order calls for study of predictive algorithms used by law enforcement agencies.

AI-enabled imaging company Aidoc scooped up $110 million in a Series D funding round. The round was led by TCV and Alpha Intelligence Capital with participation from AIC's co-investor CDIB Capital. The investment, which comes nearly a year after the startup announced its $66 million Series C, brings Aidoc's funding pot to $250 million. Aidoc offers tools that help radiologists find and triage injuries and health conditions based on imaging results. It also provides coordination software for stroke and cardiovascular care, alerting relevant members of the care team and sharing data and images.

This week Aidoc announced that they have raised $110 million in their Series D expansion round. This round of funding was co-led by TCV and Alpha Intelligence Capital with participation from CDIB Capital. Funding raised in this round will go toward expansion of Aidoc's first of its kind AI Care Platform. The platform offers health systems a singular platform solution designed to help doctors manage the entire patient lifecycle--from diagnostic aid, to consultation, to suggested treatment paths, to patient follow-up tools. In clinical studies, this platform has proven to reduce turnaround time, shorten patient length of stay and improve patient outcomes.

First Korean healthcare company to obtain "AA-AA" ratings in technology assessment for its FDA-cleared and CE-marked solutions Lunit intends to list its common stock on the KOSDAQ market under the ticker code "A32813". NH Investment & Securities will act as book-running manager, backing Lunit's debut. A total of 1,124,300 shares will be offered in the price range of KRW 44,000 to 49,000 ($34-38). The exact price will be determined after recording the demand of institutional investors on July 7-8, while retail buyers can take part in the public subscription during July 12-13. Based on the low end of the targeted range, Lunit expects to raise about KRW 54 billion ($42 million).

Aidoc recently earned FDA clearance for its ninth artificial intelligence-powered radiology tool, this one to spot potential cases of brain …

As the development and adoption of AI-enabled healthcare continue to accelerate, regulators and researchers are beginning to confront oversight concerns in the clinical evaluation process that could yield negative consequences on patient health if left unchecked. Since 2015, the United States Food and Drug Administration (FDA) has evaluated and granted clearance for over 100 AI-based medical devices using a fairly rudimentary evaluation process that is in dire need of improvement as these evaluations have not been adapted to address the unique concerns surrounding AI. This brief examined this evaluation process and analyzed how devices were evaluated before approval. We analyzed public records for all 130 FDA-approved medical AI devices between January 2015 and December 2020 and observed significant variety and limitations in test-data rigor and what developers considered appropriate clinical evaluation. When we performed an analysis of a well-established diagnostic task (pneumothorax, or collapsed lung) using three sets of training data, the level of error exhibited between white and Black patients increased dramatically.

Walter Bradley Center director Robert J. Marks continued his podcast discussion with anesthesiologist Richard Hurley in "Good and bad algorithms in the practice of medicine" (May 19, 2022). An algorithm is "a procedure for solving a mathematical problem (as of finding the greatest common divisor) in a finite number of steps that frequently involves repetition of an operation." Algorithms, Dr. Marks points out, can either sharpen or derail services, depending on their content. Before we get started: Note: Robert J. Marks, a Distinguished Professor of Computer and Electrical Engineering, Engineering at Baylor University, has a new book, coming out Non-Computable You (June, 2022), on the need for realism in another area as well -- the capabilities of artificial intelligence. This portion begins at 01:59 min.

Artificial intelligence (AI) is here to stay, and it's really very important for the psychiatric field to take a leading role in developing it.

"Artificial intelligence is around us, and it will change medicine, including ophthalmology. Come and learn about recent developments in different subfields of ophthalmology, based on AI technology!" Andrzej Grzybowski, Professor of Ophthalmology and Chair of the Department of Ophthalmology, University of Warmia and Mazury, Olsztyn, Poland, and Head of the Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznań, Poland, talks about the inspiration behind the virtual event, the impressive speaker list, and his own work in the field. When did you first decide to organize this online event; what was the inspiration behind it? I have thought about it for some time. However, the final argument for going ahead with the event was to receive the support from the Polish Ministry of Science and Education.

Mental health issues have come into a clearer focus amid the pandemic. Depression became endemic, but it still too often goes undetected. Even when it does, healthcare providers struggle to meet demand. Two women engineers -- both of whom experienced depression and had trouble finding therapy -- thought the answer might be helping medical pros detect depression. Kintsugi is a startup that wants to put technology to work on the problem.