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.
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Topics Covered in This Episode:
- Trillion-Dollar Data Center Investments for AI
- Generative AI Transforming Health Tech Use Cases
- AI and Physician Burnout Solutions
- Organizational Challenges: AI Literacy and Education
- Data Quality, Cleanliness, and Health Tech Outcomes
- Transparency and Accountability in Health Data Pipelines
- Importance of RAG (Retrieval Augmented Generation)
- AI Hallucinations and Patient Safety Risks
- Open Source AI Models and Health Data Privacy
- Future Impacts of Large Language Model Investments
Timestamps:
00:00 "Tech and Healthcare Evolution Insights"
06:50 AI Investment and Data Challenges in Ghana
09:48 Ensuring Accurate, Real-Time Data Retrieval
13:18 "Building Transparent GenAI Models"
15:54 Prior Authorization Ensures Claims Accuracy
18:34 Responsible Data Management Challenges
22:45 AI Data Accuracy and Consequences
25:04 Data Transparency and Accuracy Imperative
Keywords:
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 AI
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