It still remains to be seen whether the sci-fi genre is correct and artificial intelligence will one day rise up against the human race, but in the meantime, AI just might save your life. An algorithm developed by the Mayo Clinic can significantly increase the number of cases of low ejection fraction caught in its earliest stages, when it's still most treatable, according to a study published this month in Nature Medicine. The condition, in which the heart is unable to pump enough blood from its chamber with each contraction, is associated with cardiomyopathy and heart failure and is often symptomless in its early stages. Traditionally, the only way to diagnose low ejection fraction is with the use of an echocardiogram, a time-consuming and expensive cardiac ultrasound. The Mayo Clinic's AI algorithm, however, can screen for low ejection fraction in a standard 12-lead electrocardiogram (EKG) reading, which is a much faster and more readily available tool. In the study, more than 22,600 patients received an EKG as part of their usual primary care checkups, then were randomly assigned to have their results analyzed by the AI or by a physician as usual.
A novel device designed to help stroke patients recover wrist and hand function has been approved by the US Food and Drug Administration (FDA). Called IpsiHand, the system is the first brain-computer interface (BCI) device to ever receive FDA market approval. The IpsiHand device consists of two separate parts – a wireless exoskeleton that is positioned over the wrist, and a small headpiece that records brain activity using non-invasive electroencephalography (EEG) electrodes. The system is based on a discovery made by Eric Leuthardt and colleagues at the Washington University School of Medicine over a decade ago. It is well known that each side of the brain controls movement on the opposite side of the body, so if a stroke damages motor function on the right side of the brain movement on a person's left side will be affected.
To give clinicians a quick, cross-sectional look into potential blockages of the heart's major arteries, Abbott has combined digital imaging technology with artificial intelligence to build an automated system for cardiac procedures. The company's Ultreon software relies on catheters equipped with optical coherence tomography, which uses laser light to scan the interior of a blood vessel and the immediately surrounding tissues to detect calcium and plaque deposits, while also instantly measuring the diameter of an artery. The system--which has now received a CE Mark in Europe--is designed to provide surgeons with prompt information during the placement of coronary stents, faster and more precisely compared to conventional angiography imaging. A previous study by Abbott found having the information from OCT scans readily available led most physicians to change their treatment approach, by selecting the proper stent size and placement location. RELATED: FDA clears PhotoniCare's handheld OCT scanner for checking ear infections After planning a procedure using angiography alone, 88% of operations altered course when surgeons saw high-resolution OCT images and automatic measurements from inside the patient's arteries.
Artificial intelligence companies are developing audio transcription tools that can create searchable archives of calls and meetings, WIRED reported April 15. Artificial intelligence companies have greatly improved their automated audio transcription in recent years, and the technology is now able to produce transcripts with impressive accuracy, according to WIRED. One example is Stedi, a company that makes business-to-business software. It developed a tool called Rewatch that records meetings and uses voice-dictation AI to transcribe it, providing employees with a searchable record of everything said during the meeting. AI companies Otter.ai and Trint also offer voice-dictation to produce meeting transcripts, and Zoom has built-in wares that offer meeting notes.
In just the last two years, artificial intelligence has become embedded in scores of medical devices that offer advice to ER doctors, cardiologists, oncologists, and countless other health care providers. The Food and Drug Administration has approved at least 130 AI-powered medical devices, half of them in the last year alone, and the numbers are certain to surge far higher in the next few years. Several AI devices aim at spotting and alerting doctors to suspected blood clots in the lungs. Some analyze mammograms and ultrasound images for signs of breast cancer, while others examine brain scans for signs of hemorrhage. Cardiac AI devices can now flag a wide range of hidden heart problems.
Today, the U.S. Food and Drug Administration authorized marketing of the GI Genius, the first device that uses artificial intelligence (AI) based on machine learning to assist clinicians in detecting lesions (such as polyps or suspected tumors) in the colon in real time during a colonoscopy. "Artificial intelligence has the potential to transform health care to better assist health care providers and improve patient care. When AI is combined with traditional screenings or surveillance methods, it could help find problems early on, when they may be easier to treat," said Courtney H. Lias, Ph.D. acting director of the GastroRenal, ObGyn, General Hospital and Urology Devices Office in the FDA's Center for Devices and Radiological Health. "Studies show that during colorectal cancer screenings, missed lesions can be a problem even for well-trained clinicians. With the FDA's authorization of this device today, clinicians now have a tool that could help improve their ability to detect gastrointestinal lesions they may have missed otherwise."
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.