About this Research Topic
Suicide constitutes a grave global health concern, being a major contributor to mortality and morbidity with around 800,000 deaths each year worldwide.
Factors contributing to this challenging issue include:
- Issues in effectively assessing suicide risk, namely amongst specific populations like veterans, the LGBT community, and middle-aged men.
- Difficulties in implementing suicide risk evaluation processes.
- The impact of self-stigma, public, and anticipated stigma on seeking treatment.
- The influence of media on suicide including coverage accuracy and content generated by social media users.
The world of Artificial Intelligence (AI), especially with the development of Generative AI models like Large Language Models (LLMs), presents ground-breaking potential to transform mental health practices. They can streamline research methodologies, provide valuable assistance to healthcare professionals, and widen the accessibility of mental health resources to patients.
However, the use of Generative AI in enhancing mental health outcomes, particularly for suicide prevention and intervention, warrants rigorous review. The development of AI presents a fresh perspective on suicide research, focusing on the possibilities unveiled by AI technologies. This topic seeks to explore the possible interactions of AI and mental health, advocating for thorough research on how Generative AI technologies can be utilized to advance suicide understanding and prevention.
Key subjects of study include:
- Leveraging Generative AI to refine suicide risk assessment processes.
- Utilizing Generative AI to boost training protocols for mental health professionals concerning suicide prevention.
- Using Generative AI to change social perceptions and combat stigma surrounding suicide.
- Harnessing AI's potential to alter the representation of suicide in the media.
- Addressing ethical, safety, and privacy issues in employing AI systems in this critical field.
Factors contributing to this challenging issue include:
- Issues in effectively assessing suicide risk, namely amongst specific populations like veterans, the LGBT community, and middle-aged men.
- Difficulties in implementing suicide risk evaluation processes.
- The impact of self-stigma, public, and anticipated stigma on seeking treatment.
- The influence of media on suicide including coverage accuracy and content generated by social media users.
The world of Artificial Intelligence (AI), especially with the development of Generative AI models like Large Language Models (LLMs), presents ground-breaking potential to transform mental health practices. They can streamline research methodologies, provide valuable assistance to healthcare professionals, and widen the accessibility of mental health resources to patients.
However, the use of Generative AI in enhancing mental health outcomes, particularly for suicide prevention and intervention, warrants rigorous review. The development of AI presents a fresh perspective on suicide research, focusing on the possibilities unveiled by AI technologies. This topic seeks to explore the possible interactions of AI and mental health, advocating for thorough research on how Generative AI technologies can be utilized to advance suicide understanding and prevention.
Key subjects of study include:
- Leveraging Generative AI to refine suicide risk assessment processes.
- Utilizing Generative AI to boost training protocols for mental health professionals concerning suicide prevention.
- Using Generative AI to change social perceptions and combat stigma surrounding suicide.
- Harnessing AI's potential to alter the representation of suicide in the media.
- Addressing ethical, safety, and privacy issues in employing AI systems in this critical field.
Keywords: suicide, mental health, artificial intelligence, generative AI, large language models (LLMs), stigma, suicidal
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