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Background: Stigma is a pervasive and distressing problem experienced frequently by lung cancer patients, and there is a lack of psychosocial interventions that target the reduction of lung cancer stigma. Mindful self-compassion (MSC) is an empirically supported intervention demonstrated to increase self-compassion and reduce feelings of shame and distress in non-cancer populations. However, there are several anticipated challenges for delivering MSC to lung cancer patients, and modifications may be needed to improve acceptability, appropriateness, and feasibility.

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Integrated care demands a workforce that is confident, capable and compassionate. This is dependent on a willingness to work inter-professionally and understand the roles, standards and values of other professional groups. However, there are few examples of integrated care initiatives within higher education that aim to build the knowledge and skills required to support effective integrated, people-centred care.

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Responsible Design, Integration, and Use of Generative AI in Mental Health.

JMIR Ment Health

January 2025

The Samueli Initiative for Responsible AI in Medicine, Tel Aviv University, Tel Aviv, Israel.

Generative artificial intelligence (GenAI) shows potential for personalized care, psychoeducation, and even crisis prediction in mental health, yet responsible use requires ethical consideration and deliberation and perhaps even governance. This is the first published theme issue focused on responsible GenAI in mental health. It brings together evidence and insights on GenAI's capabilities, such as emotion recognition, therapy-session summarization, and risk assessment, while highlighting the sensitive nature of mental health data and the need for rigorous validation.

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Introduction: The integration of artificial intelligence (AI) into healthcare introduces innovative possibilities but raises ethical, legal and professional concerns. Assessing the performance of AI in core components of the United States Medical Licensing Examination (USMLE), such as communication skills, ethics, empathy and professionalism, is crucial. This study evaluates how well ChatGPT versions 3.

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This study investigates how interpersonal (speaker-partner) synchrony contributes to empathetic response generation in communication scenarios. To perform this investigation, we propose a model that incorporates multimodal directional (positive and negative) interpersonal synchrony, operationalized using the cosine similarity measure, into empathetic response generation. We evaluate how incorporating specific synchrony affects the generated responses at the language and empathy levels.

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