Improving Psychotherapy With AI: From the Couch to the Keyboardhttps://www.embs.org/pulse/wp-content/uploads/sites/13/2022/11/1-Improving-Psychotherapy-With-AI-From-the-Couch-to-the-Keyboard-by-Summer-Allen.jpg768432IEEE PulseIEEE Pulse//www.embs.org/pulse/wp-content/uploads/sites/13/2022/06/ieee-pulse-logo2x.png
Enhancing quality, expanding access, and measuring outcomes with the aid of artificial intelligence means better care for patients.
Finding New Ways to Analyze the Microbiomehttps://www.embs.org/pulse/wp-content/uploads/sites/13/2022/11/4-Finding-New-Ways-to-Analyze-the-Microbiome-by-Leslie-Mertz.jpg768432IEEE PulseIEEE Pulse//www.embs.org/pulse/wp-content/uploads/sites/13/2022/06/ieee-pulse-logo2x.png
The gut microbiome is a key contributor to human health, but untangling how this happens is still a challenge. Bioinformatics, software, and machine learning could help.
Comprehensive Database and Machine Learning Put Focus on Gut Microbiomehttps://www.embs.org/pulse/wp-content/uploads/sites/13/2022/11/6-Comprehensive-Database-and-Machine-Learning-Put-Focus-on-Gut-Microbiome-by-Leslie-Mertz.jpg768432IEEE PulseIEEE Pulse//www.embs.org/pulse/wp-content/uploads/sites/13/2022/06/ieee-pulse-logo2x.png
A technology company in Gaithersburg, MD, USA, has developed a three-pronged approach to help researchers analyze microbiome samples.
AI, Virtual Reality, and Robots Advancing Autism Diagnosis and Therapyhttps://www.embs.org/pulse/wp-content/uploads/sites/13/2021/11/Mertz-iStock-1312417650_small.jpg500315IEEE PulseIEEE Pulse//www.embs.org/pulse/wp-content/uploads/sites/13/2022/06/ieee-pulse-logo2x.png
Autism spectrum disorder (ASD) is a challenge in multiple ways. Just getting diagnosed can take months of visits to doctors and specialists. After the diagnosis, children are often put on long waiting lists to begin therapy, which itself consists of frequent sessions that while helpful, are usually quite taxing for both the children and their parents. And while child-directed therapies are available, adults who are on the spectrum often find little continuing support. Recent technologies are using artificial intelligence (AI), machine learning (ML), virtual reality (VR), and other advanced methods to address all of those issues with faster and easier diagnostics, and in-home therapeutic approaches designed for all ages.
Medical Care in the Digital Erahttps://www.embs.org/pulse/wp-content/uploads/sites/13/2021/06/Mertz-iStock-1205448274.jpg23091299IEEE PulseIEEE Pulse//www.embs.org/pulse/wp-content/uploads/sites/13/2022/06/ieee-pulse-logo2x.png
Electronic health records (EHRs), virtual office visits, and health-related apps are priming the path toward a future vision of medical care in the digital age. That future includes streamlining patient-provider interactions, making good use of the wealth of collected data, and ultimately improving all levels of care from prevention to diagnosis, treatment, and outcomes.
Predictive Models on the Rise, But Do They Work for Health Care?https://www.embs.org/pulse/wp-content/uploads/sites/13/2020/12/Mertz-predictive-models.jpg1000500IEEE PulseIEEE Pulse//www.embs.org/pulse/wp-content/uploads/sites/13/2022/06/ieee-pulse-logo2x.png
Predictive models are designed to remove some of the subjectivity inherent in medical decision-making and to automate certain health-related services with the idea of improving the accuracy of diagnosis, providing personalized treatment options, and streamlining the health care industry overall. More and more of these models using approaches including machine learning are showing up for use in doctor’s offices and hospitals, as well as in telemedicine applications, which have become prevalent with the growing demand for online alternatives to office visits.
AI-Driven COVID-19 Tools to Interpret, Quantify Lung Imageshttps://www.embs.org/pulse/wp-content/uploads/sites/13/2020/08/Mertz_AI-scaled-1.jpg25601440IEEE PulseIEEE Pulse//www.embs.org/pulse/wp-content/uploads/sites/13/2022/06/ieee-pulse-logo2x.png
Qualitative interpretation is a good thing when it comes to reading lung images in the fight against coronavirus 2019 disease (COVID-19), but quantitative analysis makes radiology reporting much more comprehensive. To that end, several research groups have begun looking to artificial intelligence (AI) as a tool for reading and analyzing X-rays and computed tomography (CT) scans, and helping to diagnose and monitor COVID-19.
Artificial Intelligence and the Future of Psychiatryhttps://www.embs.org/pulse/wp-content/uploads/sites/13/2020/06/F01A-01.png1364750IEEE PulseIEEE Pulse//www.embs.org/pulse/wp-content/uploads/sites/13/2022/06/ieee-pulse-logo2x.png
An estimated 792 million people live with mental health disorders worldwide—more than one in ten people—and this number is expected to grow in the shadow of the Coronavirus disease 2019 (COVID-19) pandemic. Unfortunately, there aren’t enough mental health professionals to treat all these people. Can artificial intelligence (AI) help? While many psychiatrists have different views on this question, recent developments suggest AI may change the practice of psychiatry for both clinicians and patients.
Detecting Faces, Saving Lives How facial recognition software is changing health careIEEE PulseIEEE Pulse//www.embs.org/pulse/wp-content/uploads/sites/13/2022/06/ieee-pulse-logo2x.png
Your phone scans your face to unlock its screen. A social media app offers suggestions of friends to tag in photos. Airline check-in systems verify who you are as you stare into a camera. These are just a few examples of how facial recognition technology (FRT) is now ubiquitous in everyday lives. The industries of law enforcement, Internet search engines, marketing, and security have long harnessed FRT, but the technology is becoming increasingly explored in the health care setting, where its potential benefit—and risks—are much greater.