Beirut, Lebanon – By the time Zainab Dhaher and her family fled their southern Lebanese village last September, Israeli shelling had become relentless. They packed what they could and drove 13 hours to Beirut, only to find themselves once again within range of Israeli bombardment. The cycle of displacement repeated. I didn't have time to pack clothes for my children," the 34-year-old mother of two recalls, her voice cracking during a phone interview. "We moved from place to place, and no one helped us. Months after a United States-brokered ceasefire took effect in November, the fear still lingers.

Background: Rapid advancements in natural language processing have led to the development of large language models with the potential to revolutionize mental health care. These models have shown promise in assisting clinicians and providing support to individuals experiencing various psychological challenges. Objective: This study aims to compare the performance of two large language models, GPT-4 and Chat-GPT, in responding to a set of 18 psychological prompts, to assess their potential applicability in mental health care settings. Methods: A blind methodology was employed, with a clinical psychologist evaluating the models' responses without knowledge of their origins. The prompts encompassed a diverse range of mental health topics, including depression, anxiety, and trauma, to ensure a comprehensive assessment. Results: The results demonstrated a significant difference in performance between the two models (p > 0.05). GPT-4 achieved an average rating of 8.29 out of 10, while Chat-GPT received an average rating of 6.52. The clinical psychologist's evaluation suggested that GPT-4 was more effective at generating clinically relevant and empathetic responses, thereby providing better support and guidance to potential users. Conclusions: This study contributes to the growing body of literature on the applicability of large language models in mental health care settings. The findings underscore the importance of continued research and development in the field to optimize these models for clinical use. Further investigation is necessary to understand the specific factors underlying the performance differences between the two models and to explore their generalizability across various populations and mental health conditions.

Seniors and first responders try to snag one of 800 doses available at a vaccination site in Fort Myers, Fla. Octavio Jones/Getty Images hide caption Seniors and first responders try to snag one of 800 doses available at a vaccination site in Fort Myers, Fla. With millions of older Americans eligible for coronavirus vaccines and limited supplies, many continue to describe a frantic and frustrating search to secure a shot, beset by uncertainty and difficulty. The efforts to vaccinate people who are 65 and older have strained under the enormous demand that has overwhelmed cumbersome, inconsistent scheduling systems. The struggle represents a shift from the first wave of vaccinations -- health care workers in health care settings -- which went comparatively smoothly. Now, in most places, elderly people are pitted against each other competing on an unstable technological playing field for limited shots.

BOSTON – A few years ago, toward the end of his life, my father battled severe depression. As a physician and professor, he did not lack access to mental-health care. But he had grown up in a society that stigmatized mental illness, and he was unwilling to seek professional help. As a son, it was devastating to watch my father suffer. As a public-health researcher, I gained a new awareness of the myriad systemic failures in the provision of care.

NEW DELHI: A senior executive at a leading IT services company recently approached Sairee Chahal, founder of women career services portal SHEROES, to explore job options. The executive was worried there could be bad news for her at work. A patient of Samir Parikh at the department of mental health and behavioural sciences at Fortis Healthcare in New Delhi lost his job about two months ago. Along with the job, the patient lost complete self-confidence to face interviews. He used to get panic attacks and palpitations hours before an interview.

Artificial intelligence is ascendant in medicine--from AI eye doctors to chatbot therapists. As medical databases balloon in size and complexity, researchers are teaching computers to sift through and identify patterns, hinting at a future in which machine learning algorithms diagnose disease all on their own. Sometimes, algorithms pick up on early signs of disease that humans wouldn't even know to look for. Last week, researchers at the University of North Carolina and Washington University reported an AI that can identify autistic infants long before they present behavioral symptoms. It's a thrilling opportunity: Early detection gives autism neuroscience a big leg up, as researchers try to understand what goes wrong during development.

A 29-year-old Nepalese student in Tokyo has found herself stuck in limbo with her dreams derailed, and the state of Japan's language schools is to blame. A survivor of human trafficking in the past, the woman, who wished to be identified only by her last name, Puri, came to Japan in 2014 as an exchange student. Brimming with high expectations at the time, she said she was determined to acquire a master's degree in sociology, with an emphasis on a subject dear to her, women's rights. Imagine her disappointment, then, when her dream was cut short by the Japanese-language school in Tokyo where she was studying. The school taught her only the very basics of the language, lumped her in with unmotivated students who frequently fell asleep in class and -- to her shock -- informed her that a vocational school was the only educational path it could prepare her for.