Health information for all: do large language models bridge or widen the digital divide? | The BMJ <https://www.bmj.com/content/387/bmj-2024-080208>
Key messages
- Large language models (LLMs) like ChatGPT could have a role in narrowing the health information digital divide, democratising access to healthcare
- But evidence indicates that LLMs might exacerbate the digital disparity in health information access in low and middle income countries
- Most LLMs perform badly in low resources languages like Vietnamese, resulting in the dissemination of inaccurate health information and posing potential public health risks
- Coordinated effort from policy makers, research funding agencies, big technology corporations, the research community, healthcare practitioners, and linguistically underrepresented communities is crucial to bridge the gap in AI language inclusivity
'Imagine asking a health information chatbot for advice on atrial fibrillation symptoms and receiving information on Parkinson’s disease — a completely unrelated condition. This is not a fictional scenario; it is what currently happens when you inquire about medical information in the Vietnamese language using OpenAI’s GPT-3.5 (through ChatGPT). This mix-up, far from a simple error, illustrates a critical problem with artificial intelligence (AI) driven healthcare communication in languages like Vietnamese. We explore the complex interplay between AI advancements and equitable access to accurate health information in low resources languages—a term used in computational linguistics to describe languages with limited digital resources available for computational model development. <https://www.bmj.com/content/387/bmj-2024-080208#ref-1>'
HIFA profile: Richard Fitton is a retired family doctor - GP. Professional interests: Health literacy, patient partnership of trust and implementation of healthcare with professionals, family and public involvement in the prevention of modern lifestyle diseases, patients using access to professional records to overcome confidentiality barriers to care, patients as part of the policing of the use of their patient data. Email address: richardpeterfitton7 AT gmail.com