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AI tools can personalize telehealth care but require accurate data, industry leaders say
AI tools in telehealth can improve personalized care, but only if the underlying data is accurate, industry leaders say. Poor data quality directly limits what AI systems can deliver for patients.
AI in Telehealth Needs Reliable Data to Work, Industry Leaders Say
AI tools can improve personalized care delivery in telehealth, but only if they work with accurate and trustworthy data, according to industry executives.
Juli Hysenbelli of the HIMSS Virtual Care Community and Kyle Zebley, CEO of the American Telemedicine Association, outlined the requirement in recent remarks. Both emphasized that data quality directly affects whether AI systems can meet patients' expectations for tailored treatment.
The challenge reflects a broader tension in healthcare: demand for personalized care has grown, but the systems meant to deliver it depend on clean, reliable information. Without it, AI applications fall short.
For healthcare professionals implementing or evaluating AI for Healthcare, this means data governance cannot be an afterthought. Organizations need processes to validate patient records, clinical notes, and other inputs before feeding them into AI systems.
The emphasis on data trustworthiness also connects to broader questions about how Generative AI and LLM systems function in clinical settings. These tools can only produce reliable outputs when trained on or working with high-quality inputs.
Healthcare organizations already managing large patient databases face practical decisions: which datasets meet the standard for AI use, and what validation processes should be in place before deployment.