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Tag: diagnosis
07/17/2023

This AI Chatbot Has Helped Doctors Treat 3 Million People–And May Be Coming To A Hospital Near You

Article Excerpt: The problem with turning to the internet for medical advice is that you can come away thinking that you either have a headache or a brain tumor – but the reality is you probably just have a headache. With K Health, Allon Bloch is creating an antidote to “Dr. Google” that ingests your symptoms and medical history via an AI-powered chatbot, sifts through the data of millions of patients and suggests a medical condition based on how you compare to other people like you. “We’re trying to mimic the best doctor in the world,” says Bloch, 53, cofounder and CEO of the seven-year-old New York-based startup.

Full Article: https://tinyurl.com/4awtf4ua

Article Source: Forbes

06/21/2023

Health Watch: New Center Focuses on AI’s Impact on Future of Health Care

Article Excerpt: A new center based at the Dartmouth-Hitchcock Medical Center in Lebanon will focus on artificial intelligence and the transformative role it’s expected to play in health care over the next decade. We may not know it but artificial intelligence, or AI, is all around us, whether it’s the cars we drive or the cellphones we use on a regular basis. And that includes how health care is delivered. “Computers are getting very good at solving problems,” Saeed Hassanpour said. Hassanpour is the director of the Center for Precision Health and Artificial Intelligence, which he calls a hub for education and research. It’s based in the Williamson building at the Dartmouth-Hitchcock Medical Center and is a collaboration between the Dartmouth Cancer Center, Dartmouth College and the Geisel School of Medicine.

Full Article: https://tinyurl.com/2b4sswn7

Article Source: WCAX

06/09/2023

DHMC Campus to Host AI Initiative

Article Excerpt: With $2 million, the Geisel School of Medicine and the Dartmouth Cancer Center are launching a new Center for Precision Health and Artificial Intelligence on the Dartmouth Hitchcock Medical Center campus in Lebanon. The new center, which will be based in the Williamson Translational Research Building on DHMC’s campus, aims to bring together related research and clinical efforts across Dartmouth to use information about patients’ biology, such as genetics, medical history, lifestyle and environment to create personalized treatment plans and disease-prevention strategies in order to improve people’s health. “It is a very active domain of research,” Saeed Hassanpour, a Dartmouth associate professor of biomedical data science, epidemiology and computer science and the center’s inaugural director, said in a phone interview. “There’s a lot of promise.”

Full Article: https://tinyurl.com/4zvp4rkb

Article Source: Valley News

06/08/2023

The Tech Solutions Helping Battle Depression and Anxiety

Article Excerpt: Though COVID-19 is no longer classified as a global health emergency, the spike in mental health disorders that accompanied the rapid spread of the virus hasn’t abated… the rise in mental health conditions has also meant that more people are comfortable seeking support. As a result, there’s never been more demand for health and wellbeing services with the behavioral health market expected to grow to $105 billion by 2029. And tech innovators continue to develop solutions that address specific gaps in the treatment pipeline, democratize access to treatment such as therapy and provide tools to manage our wellbeing holistically.

Full Article: https://tinyurl.com/yckvvf35

Article Source: 150sec

06/07/2023

Dartmouth Launches Center for Artificial Intelligence, Precision Medicine

Article Excerpt: Dartmouth launched its Center for Precision Health and Artificial Intelligence (CPHAI) this week, which is set to advance interdisciplinary research into how artificial intelligence (AI) and biomedical data can be used to improve precision medicine and health outcomes. CPHAI’s launch is supported by $2 million in initial funding from Dartmouth’s Geisel School of Medicine and the Dartmouth Cancer Center. The center’s research aims to improve public health and healthcare delivery while maintaining rigorous ethical standards for health AI, according to CPHAI’s website.

Full Article: https://tinyurl.com/2p983hnm

Article Source: Precision Medicine News

06/05/2023

New Dartmouth Center Applies AI to Improve Health Outcomes

Article Excerpt: Dartmouth has created a Center for Precision Health and Artificial Intelligence (CPHAI ) to spur interdisciplinary research that can better leverage—as well as more safely and ethically deploy—biomedical data in assessing and treating patients and improving their health care outcomes…. “What makes CPHAI unique is its interdisciplinary and comprehensive approach to precision health and artificial intelligence, focusing not only on technological advancements but also on ethical and societal implications,” says Saeed Hassanpour, associate professor of biomedical data science, epidemiology, and computer science, who serves as the center’s inaugural director.

Full Article: https://tinyurl.com/2nxus3p7

Article Source: Dartmouth News

10/27/2022

Personalising Mental Health Care

Article Excerpt: Although researchers have made unprecedented progress in identifying ‘averaged’ or ‘population-level’ mechanisms of mental health disorders, these approaches have led to a drowning effect at an individual level where person-specific information is often lost if it doesn’t align with an averaged expectation. To bridge this gap between research and clinical practice, we have developed a novel individualised machine learning framework called Affinity Scores. By identifying personalised signatures that can be integrated into a clinician’s decision-making for each of their patients, Affinity Scores represent a fundamental shift in our approach to personalised psychiatry.

Full Article: https://tinyurl.com/mrcn2ayh

Article Source: Pursuit

10/17/2022

Designing an electronic medical record alert to identify hospitalised patients with HIV: successes and challenges

El-Nahal W, Grader-Beck T, Gebo K, Holmes E, Herne K, Moore R, Thompson D, Berry S. Designing an electronic medical record alert to identify hospitalised patients with HIV: successes and challenges. BMJ Health Care Inform 2022;29:e100521. doi:10.1136/bmjhci-2021-100521

An electronic medical record (EMR) alert system was developed to use readily available data elements to accurately identify hospitalized people with HIV. Authors described the design and implementation of the EMR alert and methods to evaluate its accuracy for identifying people with HIV. Over 24 months, the EMR alert was used to notify an intervention team and data abstraction team in real time about admissions of people with HIV. Sensitivity was assessed by comparing the machine-learning alert system to manual chart reviews. Positive predictive value (probability that a patient with a positive test result actually has the disease), was assessed by false positives identified in chart review (not having HIV despite alert triggering). Results demonstrated high sensitivity (sensitivity=100%, 95% CI 82-100%) and good predictive value (84%, 95% CI 82-86%). A combination of data (diagnosis, prescriptions, and lab orders) in the EMR alert system achieved high sensitivity and positive predictive value in identifying people with HIV. ICD Code diagnoses were the strongest contributors to predictive value, compared to the other criteria. Use of data-driven alerts in electronic health record systems can facilitate the deployment of multidisciplinary teams for medication review, education, case management, and outpatient linkage to follow-up.

07/25/2022

Evaluation of an artificial intelligence-based medical device for diagnosis of autism spectrum disorder

Megerian JT, Dey S, Melmed RD, Coury DL, Lerner M, Nicholls CJ, Sohl K, Rouhbakhsh R, Narasimhan A, Romain J, Golla S, Shareef S, Ostrovsky A, Shannon J, Kraft C, Liu-Mayo S, Abbas H, Gal-Szabo DE, Wall DP, & Taraman S (2022). Evaluation of an artificial intelligence-based medical device for diagnosis of autism spectrum disorder. NPJ Digital Medicine, 5(1), 57–57. https://doi.org/10.1038/s41746-022-00598-6

Researchers conducted a double-blinded, multi-site, active comparator cohort study to test the accuracy of artificial intelligence software for diagnosing autism spectrum disorder (ASD). The software device collects data about child behavioral features from 3 sources (caregiver questionnaire, analysis of two short 1 minute home videos recorded and uploaded by the child’s caregiver, provider questionnaire). Data are processed using a machine learning algorithm to indicate whether a person is ASD positive, ASD negative, or inconclusive (i.e., inputted data are not sufficient for a predictive output). Researchers evaluated the software in a study with 425 children aged 18-72 months for whom a caregiver or provider had a concern about developmental delay. Researchers compared the software outputs to the clinical standard (diagnosis made by a provider based on DSM-5 criteria). Results demonstrated that data collection with the software device took less time to administer and require less specialty training relative to clinical standard process. For about 33% of the sample, the algorithm output supported accurate diagnoses compared with clinical evaluation. Of the children for whom the software algorithm made a definite evaluation, 98.4% with clinically diagnosed ASD received an ASD positive result and 78.9% without a clinical diagnosis of ASD received an ASD negative result. All children who received a false-positive result (n=15) had a non-ASD developmental condition. Only one child received a false negative result in this study. Overall, this machine learning tool demonstrated high sensitivity and good specificity for diagnosing ASD. The tool can potentially expand the ability to effectively diagnose children with ASD in primary care to facilitate early intervention and more efficient use of specialist resources.