Most artificial intelligence-powered medical devices approved by the FDA have not been comprehensively studied to determine the fairness and …

To read the full story, subscribe or sign in. The rapidly expanding field artificial intelligence (AI)-aided image analysis received a boost with the FDA 510(k) clearance for Optellum Ltd.'s Virtual Nodule Clinic, which helps clinicians evaluate small, potentially malignant lung lesions or nodules. The action makes Optellum's system the first cleared radiomic application for early lung cancer, an area of active research for the last five years.

Small-molecule therapeutics treat a wide variety of diseases, but their effectiveness is often diminished because of their pharmacokinetics -- what the body does to a drug. After administration, the body dictates how much of the drug is absorbed, which organs the drug enters, and how quickly the body metabolizes and excretes the drug again. Nanoparticles, usually made out of lipids, polymers, or both, can improve the pharmacokinetics, but they can be complex to produce and often carry very little of the drug. Some combinations of small-molecule cancer drugs and two small-molecule dyes have been shown to self-assemble into nanoparticles with extremely high payloads of drugs, but it is difficult to predict which small-molecule partners will form nanoparticles among the millions of possible pairings. MIT researchers have developed a screening platform that combines machine learning with high-throughput experimentation to identify self-assembling nanoparticles quickly.

Artificial intelligence (AI) can transform health care practices with its increasing ability to translate the uncertainty and complexity in data into actionable—though imperfect—clinical decisions or suggestions. In the evolving relationship between humans and AI, trust is the one mechanism that shapes clinicians’ use and adoption of AI. Trust is a psychological mechanism to deal with the uncertainty between what is known and unknown. Several research studies have highlighted the need for improving AI-based systems and enhancing their capabilities to help clinicians. However, assessing the magnitude and impact of human trust on AI technology demands substantial attention. Will a clinician trust an AI-based system? What are the factors that influence human trust in AI? Can trust in AI be optimized to improve decision-making processes? In this paper, we focus on clinicians as the primary users of AI systems in health care and present factors shaping trust between clinicians and AI. We highlight critical challenges related to trust that should be considered during the development of any AI system for clinical use.

A coalition of AI researchers and health care professionals in fields like infectious disease, radiology, and ontology have found several common but serious shortcomings with machine learning made for COVID-19 diagnosis or prognosis. After the start of the global pandemic, startups like DarwinAI, major companies like Nvidia, and groups like the American College of Radiology launched initiatives to detect COVID-19 from CT scans, X-rays, or other forms of medical imaging. The promise of such technology is that it could help health care professionals distinguish between pneumonia and COVID-19 or provide more options for patient diagnosis. Some models have even been developed to predict if a person will die or need a ventilator based on a CT scan. However, researchers say major changes are needed before this form of machine learning can be used in a clinical setting.

BOSTON - New treatments for Alzheimer's disease are desperately needed, but numerous clinical trials of investigational drugs have failed to generate promising options. Now a team at Massachusetts General Hospital (MGH) and Harvard Medical School (HMS) has developed an artificial intelligence-based method to screen currently available medications as possible treatments for Alzheimer's disease. The method could represent a rapid and inexpensive way to repurpose existing therapies into new treatments for this progressive, debilitating neurodegenerative condition. Importantly, it could also help reveal new, unexplored targets for therapy by pointing to mechanisms of drug action. "Repurposing FDA-approved drugs for Alzheimer's disease is an attractive idea that can help accelerate the arrival of effective treatment--but unfortunately, even for previously approved drugs, clinical trials require substantial resources, making it impossible to evaluate every drug in patients with Alzheimer's disease," explains Artem Sokolov, PhD, director of Informatics and Modeling at the Laboratory of Systems Pharmacology at HMS. "We therefore built a framework for prioritizing drugs, helping clinical studies to focus on the most promising ones."

Alongside the rapid development of vaccines, the FDA has cleared a number of COVID-19 breakthroughs for emergency use as part of the ongoing fight against the devastating virus. So far, we've seen the agency approve medical advances including lab-made monoclonal antibodies for moderate infections that risk turning more severe, a rapid test that uses CRISPR gene-editing tech and Fitbit's Flow ventilator. The latest tool to gain clearance is the first AI-based screening device designed to pinpoint lurking signs of COVID-19 in asymptomatic people. Dubbed the Tiger Tech COVID Plus Monitor, the apparatus is an armband that uses light sensors and a small computer processor to check for biomarkers of the virus, such as hypercoagulation -- a common COVID-19 abnormality that causes the blood to clot more easily. Once strapped to a person's arm, the monitor's onboard sensors start collecting pulse signals from blood flow over a period of three to five minutes.

The Food and Drug Administration this week gave emergency-use authorization to a "machine learning-based" device that will reportedly work to detect COVID even in cases in which no immediate symptoms are evident. The device, manufactured by Tiger Tech Solutions, "identifies certain biomarkers that may be indicative of SARS-CoV-2 infection … in asymptomatic individuals over the age of 5," the FDA said in a press release. The device works by reading signals of a patient's blood flow using an armband. "The sensors first obtain pulsatile signals from blood flow over a period of three to five minutes," the FDA said. "Once the measurement is completed," the statement continued.

Imbio has gained FDA 510(k) clearance for its RV/LV AnalysisTM algorithm, a leading supplier of artificial intelligence (AI) solutions for medical imaging evaluation. The RV/LV Analysis algorithm is a quick and easy way to check for right ventricular dilation. The tool efficiently and precisely evaluates the heart's ventricles to calculate the proportion of the right to left ventricle's maximum diameter. The RV/LV Analysis results are readily accessible for clinicians without any extra work, including a detailed report of quantitative findings directly attached to the patient imaging study in minutes. David Hannes, Imbio Chief Executive Officer, stated that their automated RV/LV Assessment has the control to supply factual information and notify risk stratification in many acute cases. Imbio is proud to offer this AI-driven algorithm to physicians and partners to support acute cases and facilitate critical treatment decisions for patients.

Riva Health, founded by scientist Tuhin Sinha and Siri co-founder Dag Kittlaus, wants to help people measure their blood pressure in a clinically approved way. Blood pressure can help indicate at-risk patients before they are actually at risk, showing early signs of heart disease. While other hardware solutions on the market promise the same end goal, Riva wants to be a purely software solution that integrates with hardware that it thinks its end user has anyway: their smartphone. The company, launching out of stealth today, has raised $15.5 million in seed funding in a round led by Menlo Ventures, with participation from True Ventures. Greg Yap of Menlo, who talked to Sinha for three years before investing, will be joining the board.