Artificial Intelligence Systems Regularly Employ Biased, Offensive Language
In the realm of healthcare, the integration of Artificial Intelligence (AI) is a growing trend. However, it's crucial to ensure that AI systems are used responsibly, particularly in sensitive areas such as addiction and substance use disorders.
Ongoing initiatives are focusing on enhancing the use of patient-centered language in Large Language Models (LLMs) used in healthcare communication. These efforts aim to create more inclusive, equitable, and culturally competent communication tools tailored to patient needs.
One key strategy is the development of Patient-Centered Value Frameworks and Guidelines. The Center for Innovation & Value Research recently released a Blueprint for Patient-Centered Value Research, encouraging the embedding of patient perspectives throughout healthcare research and communication tools. This initiative emphasizes inclusive, community-informed language that reflects patient priorities and experiences, which can be applied to improve LLM outputs in addiction and substance use disorder contexts.
Another approach involves leveraging AI-driven multilingual and culturally competent tools. These tools, employed in multilingual patient support initiatives, can help reduce healthcare disparities caused by language barriers. They provide culturally competent communication, which is vital for effective addiction care.
Federal and policy support for Patient-Centered Digital Health is also essential. Initiatives like those from the CMS and White House health technology ecosystem promote AI conversational assistants and digital health tools that improve patient engagement and care navigation. These tools incorporate user-friendly, patient-centered language to empower patients coping with chronic diseases and behavioral health challenges, including substance use disorders.
Expanding Telebehavioral Health Access is another area of focus. The Health Resources and Services Administration (HRSA) plans to increase telebehavioral health services access, which often leverage patient-centered digital communication tools. This expansion necessitates language models that use respectful, non-stigmatizing, and inclusive language for addiction patients.
Lastly, healthcare organizations are working to build patient-centered cultures supported by technology that promotes meaningful patient-provider communication. This includes embedding patient preferences and understandable language in digital interventions, training providers, and securing funding for such projects, which can translate into improved LLM design and deployment in addiction care.
A recent study by Mass General Brigham revealed that over one-third of the answers from models without special adjustments included stigmatizing language. To address this, the researchers suggest offering alternative wording that is more patient-friendly and free of stigma. They also recommend that clinicians review any AI-generated content carefully before sharing it with patients. By carefully crafting the instructions for the models (prompt engineering), the researchers were able to reduce the use of stigmatizing language by nearly 90%.
Involving patients and families in the development and refining of AI tools can provide valuable insights into respectful and helpful language. Future efforts should include people with personal experience of addiction in developing and refining the language used by AI tools. The study underscores the importance of building a healthcare environment that supports all patients, particularly those facing addiction and related challenges.
In conclusion, the convergence of patient-centered value research, AI-powered multilingual and conversational tools, supportive policy frameworks, and expanded telehealth services represents ongoing initiatives to make large language models more attuned to the needs of patients with addiction and substance use disorders, using respectful and patient-focused language. Balancing the benefits of AI with careful consideration of its impact on language and stigma is crucial in healthcare.
- The integration of responsible AI systems, particularly in sensitive areas like mental health and addiction, can be strengthened by leveraging AI-driven health communication tools that employ respectful, non-stigmatizing, and inclusive language.
- The development of patient-centered language in Large Language Models (LLMs) used in health communication is crucial to enhance mental health care, especially for individuals dealing with addiction and substance use disorders, as it promotes a more supportive and inclusive healthcare environment.
