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Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations

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  Print publication without navigation Published in Science and Technology on by The Lancet
          🞛 This publication is a summary or evaluation of another publication 🞛 This publication contains editorial commentary or bias from the source
Without careful dissection of the ways in which biases can be encoded into artificial intelligence (AI) health technologies, there is a risk of perpetuating existing health inequalities at scale. One major source of bias is the data that underpins such technologies.
The article from The Lancet Digital Health discusses the potential of large language models (LLMs) in transforming healthcare by enhancing clinical decision support, patient communication, and administrative tasks. It highlights the capabilities of LLMs in understanding and generating human-like text, which could lead to personalized medicine, improved diagnostic accuracy, and better patient engagement. However, the article also addresses significant challenges and ethical considerations, including data privacy, bias in AI algorithms, the need for transparency, and the risk of over-reliance on AI systems. It emphasizes the importance of rigorous validation, ethical guidelines, and regulatory frameworks to ensure that LLMs are integrated into healthcare in a safe, equitable, and effective manner. The piece calls for a collaborative approach involving clinicians, AI developers, ethicists, and policymakers to navigate these complexities and harness the full potential of LLMs in healthcare.

Read the Full The Lancet Article at:
[ https://www.thelancet.com/journals/landig/article/PIIS2589-7500(24)00224-3/fulltext ]