Healthcare companies are incorporating generative AI tools into their product pipelines and IT systems since the technology showed its ability to perform many tasks faster, cheaper, and in some cases better than humans. However, some challenges remain in understanding how generative AI works and what ethical challenges may arise when exposed to new types of data. AI models create ideas of what is statistically likely from vast amounts of data, including Wikipedia, books, and elsewhere on the Internet, and what is good from human feedback on the answers.
These models are not grounded in understanding the world or causality and can fail spatial reasoning and math tasks. It is crucial to ask questions about who built the model and what data was used to train it to ensure that the model is not biased or misinformed. Builders of generative AI systems can use reinforcement learning to improve accuracy.
While there are many examples of inappropriate use of algorithms in medicine, experts hope that generative AI can be used responsibly with the right guardrails. There are no regulations specific to generative AI yet, but there is a growing movement for rules. In broadest sense, an apocalyptic scenario as a result of AI technology is extremely unlikely, and the potential for AI to democratize access to quality healthcare, develop better medicines, and reduce pressure on scarce physicians is vast.