Article Excerpt: Mental healthcare is on the verge of a significant transformation with algorithms emerging as potential allies in the treatment process. However, the inherent bias in generative AI poses a critical question: what are the implications for patient outcomes?
Full Article: https://tinyurl.com/ykx8j6u5
Article Source: Omnia Health
Article Excerpt: Technology offers new avenues for mental health delivery. Digital record keeping, virtual consultations, wearables that monitor activity and well-being, mindfulness apps, and AI-based chatbots are just a few examples. But these advances have not been leveraged effectively enough, Cornell Tech Professor and HealthRhythms Co-Founder Tanzeem Choudhury said in a keynote talk Tuesday at the Digital Mental Health and AI Symposium organized by the Center for Technology and Behavioral Health. Choudhury explored the challenges that have forestalled digital mental health from delivering on some of its early promises and how to move the needle forward.
Full Article: https://tinyurl.com/ysvssb4s
Article Source: Dartmouth News
Article Excerpt: Clinicians, therapists, and researchers are increasingly finding that artificial intelligence (AI) can be a powerful tool in the provision of mental healthcare. As I will cover in this article, a growing body of evidence suggests that AI can help with diagnosing conditions, developing therapies, and enabling more personalized approaches and treatments.
Full Article: https://tinyurl.com/35at64cr
Article Source: Forbes
Article Excerpt: Hargrove’s story illustrates the challenges often faced by those struggling with opioid addiction — especially people of color — in receiving buprenorphine, a medication that public health experts believe should play a critical role in curbing an addiction-and-overdose crisis fueled by fentanyl. His overdose happened this month as a newly published national study from the Harvard T.H. Chan School of Public Health showed that White patients are up to 80 percent more likely to receive buprenorphine than Black patients, and that Black patients receive a more limited supply.
Full Article: https://tinyurl.com/2p854uta
Article Source: The Washington Post
Article Excerpt: Researchers from MIT acknowledged that many machine-learning models were created using skewed data, causing them to produce uneven results… Using the deep metric learning technique, researchers trained the neural network to recognize photos that are similar and different with regard to facial recognition and skin tone. During this process, researchers uncovered more information about why the previous models produced unfair results. They found that two people with a lighter skin tone were more likely to be differentiated than two people with a darker skin tone. Also, if models are trained taking into consideration the majority group instead of the minority group, it would cause bias.
Full Article: https://tinyurl.com/2p8eddan
Article Source: Health IT Analytics