PDF(Pubmed)
Abstract：
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
摘要：
■当前的精神卫生保健模式侧重于临床恢复和症状缓解。该模型的疗效受治疗师对患者恢复潜力和治疗关系深度的信任影响。精神分裂症是一种具有严重症状的慢性疾病，康复的可能性是一个有争议的问题。随着人工智能(AI)融入医疗保健领域，重要的是检查其评估精神分裂症等主要精神疾病恢复潜力的能力。
■本研究旨在评估大型语言模型（LLM）与心理健康专业人员相比的能力，以评估有或没有专业治疗的精神分裂症的预后以及长期的积极和消极结果。
■Vignettes被输入到LLM界面中，并由4个AI平台进行了10次评估：ChatGPT-3.5，ChatGPT-4，GoogleBard，还有克劳德.共收集了80项评估，并对照现有规范进行了基准评估，以分析哪些精神卫生专业人员(全科医生、精神病医生,临床心理学家,和心理健康护士）和公众思考有或没有专业治疗的精神分裂症预后以及精神分裂症干预措施的积极和消极长期结果。
■对于专业治疗精神分裂症的预后，ChatGPT-3.5非常悲观，而ChatGPT-4，克劳德，和巴德与专业观点一致，但与普通公众不同。所有LLM都认为，未经专业治疗的精神分裂症将保持静止或恶化。对于长期结果，ChatGPT-4和Claude预测的负面结果比Bard和ChatGPT-3.5更多。为了取得积极成果，ChatGPT-3.5和Claude比Bard和ChatGPT-4更悲观。
■在考虑“治疗”条件时，发现4个LLM中有3个与心理健康专业人员的预测密切相关，这证明了该技术在提供专业临床预后方面的潜力。ChatGPT-3.5的悲观评估是一个令人不安的发现，因为它可能会降低患者开始或坚持精神分裂症治疗的动机。总的来说,尽管法学硕士在加强医疗保健方面有希望，它们的应用需要严格的验证和与人类专业知识的和谐融合。
■本研究旨在评估大型语言模型（LLM）与心理健康专业人员相比的能力，以评估有或没有专业治疗的精神分裂症的预后以及长期的积极和消极结果。
■Vignettes被输入到LLM界面中，并由4个AI平台进行了10次评估：ChatGPT-3.5，ChatGPT-4，GoogleBard，还有克劳德.共收集了80项评估，并对照现有规范进行了基准评估，以分析哪些精神卫生专业人员(全科医生、精神病医生,临床心理学家,和心理健康护士）和公众思考有或没有专业治疗的精神分裂症预后以及精神分裂症干预措施的积极和消极长期结果。
■对于专业治疗精神分裂症的预后，ChatGPT-3.5非常悲观，而ChatGPT-4，克劳德，和巴德与专业观点一致，但与普通公众不同。所有LLM都认为，未经专业治疗的精神分裂症将保持静止或恶化。对于长期结果，ChatGPT-4和Claude预测的负面结果比Bard和ChatGPT-3.5更多。为了取得积极成果，ChatGPT-3.5和Claude比Bard和ChatGPT-4更悲观。
■在考虑“治疗”条件时，发现4个LLM中有3个与心理健康专业人员的预测密切相关，这证明了该技术在提供专业临床预后方面的潜力。ChatGPT-3.5的悲观评估是一个令人不安的发现，因为它可能会降低患者开始或坚持精神分裂症治疗的动机。总的来说,尽管法学硕士在加强医疗保健方面有希望，它们的应用需要严格的验证和与人类专业知识的和谐融合。
