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Data standards for the health information ecosystem have played a critical role in enabling software integration across health care enterprises for data sharing, analysis, clinical research, and public health.    However, the ability to use large language models (LLMs) to dynamically extract unstructured data into a standardized form for downstream use poses a question about the future of health data. Namely, what role do data standards play in the era of LLMs, and will we need data standards at all?    Gabriel Brat, MD, MPH, FACS, Josh Mandel, MD, and Matthew B.A. McDermott, PhD, address this question in a new editorial.    Read “Do We Need Data Standards in the Era of Large Language Models?”: https://nejm.ai/4dcpyDb    #HealthIT #AIinMedicine 

  • “Can we rely on [large language models] to perform at the necessary levels in critical settings, such as medicine? Even if we can, would they be too costly or inefficient for real-world use?”

EDITORIAL
“Do We Need Data Standards in the Era of Large Language Models?” by G.A. Brat et al.
Alexander Stevens, PhD

Strategic Informatics Analyst @ Memorial Sloan Kettering Cancer Center | Responsible AI

2mo

Humility, compassion, and patience are human qualities no [model] can provide. But me thinks given the exorbitant cost in compute power and overall cost, applied thoughtfully and deliberately, large language models can certainly help our skilled human clinicians and organizations to focus on those qualities while improving health outcomes. Thoughts?

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