Navigating the minefield of AI in healthcare: Balancing innovation with accuracy

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Article explores the challenges of ensuring generative AI, like large language models (LLMs), provides accurate health information, focusing on AI errors, disinformation, and privacy concerns, while suggesting methods for improvement.

By Hugo Francisco de SouzaMar 25 2024Reviewed by Susha Cheriyedath, M.Sc. In a recent 'Fast Facts' article published in the journal BMJ, researchers discuss recent advances in generative artificial intelligence , the importance of the technology in the world today, and the potential dangers that need to be addressed before large language models such as ChatGPT can become the trustworthy sources of factual information we believe them to be.

Unfortunately, advancements in generative AI, especially large language models like ChatGPT, have far outpaced ethical and safety checks, introducing the potential for severe consequences, both accidental and deliberate . Research estimates that more than 70% of people use the internet as their primary source of health and medical information, with more individuals tapping into LLMs such as Gemini, ChatGPT, and Copilot with their queries each day.

For lay members of society incapable of discerning between factual and inaccurate information, these errors can become very costly very fast, especially in cases of erroneous medical information. Even trained medical professionals may suffer from these errors, given the growing amount of research conducted using LLMs and generative AI for data analyses.

"One option for preventing AI-generated health disinformation involves fine-tuning models to align with human values and preferences, including avoiding known harmful or disinformation responses from being generated. An alternative is to build a specialized model to detect inappropriate or harmful requests and responses."

 

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