Will more data solve AI hallucinations? Maybe. But what about the industries that have the most to gain (and lose) from AI transformation like healthcare? Join us as we dive deep into the role of data and transformation and what obstacles the healthcare industry still has to clear to turn their digital delusions into data dreams. Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Thoughts on this? Join the convo and connect with other AI leaders on LinkedIn.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: [email protected] with Jordan on LinkedInTopics Covered in This Episode:Trillion-Dollar Data Center Investments for AIGenerative AI Transforming Health Tech Use CasesAI and Physician Burnout SolutionsOrganizational Challenges: AI Literacy and EducationData Quality, Cleanliness, and Health Tech OutcomesTransparency and Accountability in Health Data PipelinesImportance of RAG (Retrieval Augmented Generation)AI Hallucinations and Patient Safety RisksOpen Source AI Models and Health Data PrivacyFuture Impacts of Large Language Model InvestmentsTimestamps:00:00 "Tech and Healthcare Evolution Insights"06:50 AI Investment and Data Challenges in Ghana09:48 Ensuring Accurate, Real-Time Data Retrieval13:18 "Building Transparent GenAI Models"15:54 Prior Authorization Ensures Claims Accuracy18:34 Responsible Data Management Challenges22:45 AI Data Accuracy and Consequences25:04 Data Transparency and Accuracy ImperativeKeywords:Generative AI in health tech, artificial intelligence in healthcare, large language models, data center investment, AI hallucinations, data quality, retrieval augmented generation (RAG), physician burnout, AI-powered clinical notes, medical imaging AI, HR workforce optimization, health tech digital transformation, healthcare data privacy, healthcare data security, AI model training, AI education, data literacy, operational efficiency in healthcare, sustainable data centers, environmental impact of data centers, transparency in AI, data trustworthiness, real-time data extraction, cross-checking AI outputs, patient outcomes, healthcare regulations, high-stakes AI industries, open source AI models, AI supply chain management, healthcare claims denial, prior authorization automation, grounding AI models, human-in-the-loop in AI, data accuracy, precision in healthcare AI, medical decision making AISend Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Head to AI.studio/build to create your first app. Head to AI.studio/build to create your first app.

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“What did the council decide on item 42?” — answered with the speaker and the moment.