Cross-Cultural Differences in the Influences of Spiritual and Religious Tendencies on Beliefs in Genetic Determinism and Family Health History Communication: A Teleological Approach

Comparing the Perspectives of Generative AI, Mental Health Experts, and the General Public on Schizophrenia Recovery: Case Vignette Study

Background

The current paradigm in mental health care focuses on clinical recovery and symptom remission. This model's efficacy is influenced by therapist trust in patient recovery potential and the depth of the therapeutic relationship. Schizophrenia is a chronic illness with severe symptoms where the possibility of recovery is a matter of debate. As artificial intelligence (AI) becomes integrated into the health care field, it is important to examine its ability to assess recovery potential in major psychiatric disorders such as schizophrenia.

Objective

This study aimed to evaluate the ability of large language models (LLMs) in comparison to mental health professionals to assess the prognosis of schizophrenia with and without professional treatment and the long-term positive and negative outcomes.

Methods

Vignettes were inputted into LLMs interfaces and assessed 10 times by 4 AI platforms: ChatGPT-3.5, ChatGPT-4, Google Bard, and Claude. A total of 80 evaluations were collected and benchmarked against existing norms to analyze what mental health professionals (general practitioners, psychiatrists, clinical psychologists, and mental health nurses) and the general public think about schizophrenia prognosis with and without professional treatment and the positive and negative long-term outcomes of schizophrenia interventions.

Results

For the prognosis of schizophrenia with professional treatment, ChatGPT-3.5 was notably pessimistic, whereas ChatGPT-4, Claude, and Bard aligned with professional views but differed from the general public. All LLMs believed untreated schizophrenia would remain static or worsen without professional treatment. For long-term outcomes, ChatGPT-4 and Claude predicted more negative outcomes than Bard and ChatGPT-3.5. For positive outcomes, ChatGPT-3.5 and Claude were more pessimistic than Bard and ChatGPT-4.

Conclusions

The finding that 3 out of the 4 LLMs aligned closely with the predictions of mental health professionals when considering the "with treatment" condition is a demonstration of the potential of this technology in providing professional clinical prognosis. The pessimistic assessment of ChatGPT-3.5 is a disturbing finding since it may reduce the motivation of patients to start or persist with treatment for schizophrenia. Overall, although LLMs hold promise in augmenting health care, their application necessitates rigorous validation and a harmonious blend with human expertise.

Relationships of Family History-related Factors and Causal Beliefs to Cancer Risk Perception and Mammography Screening Adherence Among Medically Underserved Women

This study examines how family history-related factors and causal beliefs affect underserved women's cancer risk perceptions and adherence to mammography. 1,010 patients at a primary care safety net clinic at a large urban hospital completed a survey in 2015. Of the 1,010 patients, 467 women 45 years of age or older were included in this analysis. The majority of participants were African American (68%). We built multivariable linear and logistic regression models to examine the dependent variables of cancer risk perception and mammography screening adherence. According to the results, those with a family history of cancer were significantly more likely to be adherent to mammography. Perceived importance of family health history also significantly predicted their mammography screening adherence. However, cancer risk perceptions did not predict underserved women's mammography adherence. Significant interaction effects on the associations 1) between family cancer history, cancer risk perceptions, and mammography screening adherence and 2) between race, behavioral causal beliefs, and risk perceptions were found. Findings suggest that implementing different strategies across racial groups and by cancer history may be necessary to promote regular mammography screening.