Giving Compass' Take:
- Brookings' researchers explore how generative AI can help provide health functions and offer benefits for healthcare systems but also explain the limitations and challenges.
- Experts say that there is still bias in generative AI, especially when working with limited data. How should healthcare professionals account for these biases when thinking about tech innovation for healthcare? How can donors support ethical AI?
- Learn more about AI and healthcare.
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The increasing consumption of and self-reliance on informal information sources, particularly the internet, by patients has long been a well noted trend in the health care system. However, with the emergence of generative artificial intelligence (AI), this dependence has not only been heightened but also rapidly extended to physicians and other health care providers.
While earlier AI models were largely limited to analyzing and interpreting existing data, generative AI systems are capable of creating new content. This content creation capability, coupled with the ease of use and accessibility provided through user-friendly interfaces, has led to a surge in its adoption and use by many professionals, including health care providers. The overreliance on digital information sources traditionally stemmed from patients seeking to better understand their conditions. Now, with generative AI, health care providers might also lean heavily on AI-assisted decision-making.
While the application of generative AI in health care has yielded promising results, it is crucial to recognize that this technology is not a panacea. It cannot be universally applied to solve all problems in every health care setting. Physicians and health care providers must deploy generative AI discerningly to mitigate unintended consequences; responsible use is key to harnessing its benefits while avoiding adverse outcomes.
Generative AI performs optimally in environments characterized by high repetition and low risk. This effectiveness stems from the technology’s reliance on historical data to identify patterns and make predictions, under the premise that future conditions will mirror those of the past. Utilizing such technology in low-risk situations, particularly where errors carry minor consequences, is prudent. This cautious approach offers several advantages: It enables health care providers and, more importantly, patients to gradually comprehend the AI’s capabilities and establish trust in its utility. Additionally, it affords AI developers valuable opportunities to rigorously test and refine their systems in a controlled environment before deployment in higher-stakes scenarios.
Read the full article about generative AI in healthcare by Niam Yaraghi at Brookings.