AI vs Doctor: Who to Trust in Healthcare

AI-generated answers still struggle with accuracy and trust, especially in critical fields like healthcare. Retrieval-Augmented Generation (RAG) techniques help by grounding AI outputs in real-world data, improving reliability. But these systems often act like black boxes, making it hard to understand how they produce their results. Our new framework, KG-SMILE, brings clarity to RAG by pinpointing which parts of a knowledge graph influence AI-generated responses. This transparency helps balance accuracy with explainability, a vital step for sensitive applications. I believe trustworthy AI requires not only strong performance but also clear explanations that people can follow and trust. How important is transparency to you when using AI in decision-making?

For those holding their breath.. here is how we're tackling OCR differently at Alaffia Health: Instead of just converting scanned PDFs into text, we use advanced AI that “sees” and “reads” documents at the same time. Here’s how it works in simple terms: 1). Smart tagging ➡️ Our AI scans thousands of pages and tags only the ones with the information you actually need (e.g., lab results or discharge notes). 2). Focused extraction ➡️ A second AI zooms in on those pages to pull out the exact data points. 3). Fact agents ➡️ Specialized AI agents extract critical facts tailored to your workflows like diagnoses, lab values, medications, treatment plans, and even social determinants of health (!!!🫨). This layered approach makes large-scale medical record reviews faster, more accurate, and highly configurable for any claim or clinical decision-making process. When it comes to complex healthcare data, AI shouldn’t just “read” documents, it should understand them! 🔁

Gain actionable insights in our free on-demand webinar, and explore the potential of AI to elevate—not replace—clinical expertise. Key takeaways include: 🧠 What to ask about AI validation: testing protocols, expert input and data sources ✅ How to assess readiness and fit for AI-enabled decision support tools 🚀 Ways to deploy AI that elevate clinical expertise, not replace it ▶️ Register here 👉 https://hubs.la/Q03zLfMB0 #AIinHealthcare #AIinClinicalPractice #ClinicalDecisionSupport #Micromedex

AI in #healthcare works only as well as the data and context that shape it. Take predicting hospital readmissions: regression models can surface risk factors, but unless the data is complete and the model is well-matched to the task, insights won’t translate into better care. Choosing the right model is less about technology alone, more about aligning clinical goals, data realities and operational capacity. Explore how a structured framework helps healthcare teams make smarter #AI decisions: https://lnkd.in/d4AJ9mHD

AI in Health Information Systems 🩺 Health Information Systems (HIS) have long been the backbone of patient care, from storing records, supporting billing, and facilitating evidence-based decisions. But with Generative AI, we’re moving from record-keeping to intelligence. From Data ➝ Intelligence Instead of just data storage, AI presents interpretation of clinical notes, lab results, and even patient questions; turning information into real-time insights. This isn’t just an upgrade, it’s a paradigm shift. If done responsibly, Generative AI has the capability to empower patients, ease provider burnout, and strengthen health systems globally. 💡 The big question: How do we harness AI while keeping healthcare human? #digitalhealth #ehealth #AI

Artificial Intelligence (AI) is reshaping every aspect of healthcare — from patient interactions to back-office operations. But for community health centers, where resources are tight and priorities are clear, knowing where to start with AI is half the battle. That’s why Telcion created a new AI Resource Page built specifically for FQHCs and community-based providers — with insights on how to apply AI across medical, administrative, IT, and customer service workflows.   Explore sample vision statements, department-by-department checklists, and key questions to ask before you adopt AI. https://lnkd.in/eT9MJ-JZ

In the Healthcare Payer Industry speed without context leads to confusion. Claims may be processed in seconds, but the “why” behind each one often takes 30+ minutes to uncover. We helped transform this bottleneck with a Generative AI Retrieval Engine that makes context instant, transparent, and usable in the tools teams already know.  The result?   ● 25–30% faster claim resolution  ● Context-rich, source-cited answers  ● Fewer disputes and escalations  ● Tuned for real-world healthcare constraints Built with Persistent — clearer claims, smarter teams, stronger outcomes.   Read the full case study: https://lnkd.in/dBNTmymR Aniket Goel, Jaideep Vijay Dhok, Sameer Dixit, Shimona Chadha, Ravindran M. #ReAIlImpactStories #ReAImaginingTheWorld

We talk a lot about how AI will “save time” in healthcare. But maybe that’s not the right question anymore. Sure, automation helps with scheduling, notes, and paperwork. That’s valuable. But the AI systems now entering hospitals and clinics are doing something different — they’re interpreting, predicting, even prompting us to rethink decisions. And that takes time. What if the real value of AI isn’t speed, but pause? Not faster decisions, but smarter ones? If an AI alert makes you stop, reevaluate a diagnosis, or catch something early — is that a delay, or is that better medicine? This isn’t just about tools. It’s about how we define value in clinical care. So here’s the question we’re asking this week: Is time still the most important metric for judging AI in healthcare — or is it time we updated our thinking? What do you think?

🔍 AI is redefining how healthcare decisions are made —and we’re helping shape that future. Our perspective is featured in the latest MIT Sloan Management Review and TCS reports on Intelligent Choice Architectures, exploring how AI can unable trusted, faster, and more impactful decision-making in life sciences. By contributing to this global dialogue, we’re advancing our commitment to harness AI responsibility – with a potential to redefine patient care and innovation across healthcare. Explore the full reports here: http://spkl.io/6043Au0K1 #AI #HealthcareInnovation #Neversettle

“Every AI use case starts as a governance problem and succeeds through cultural transformation.” Emmanuel Frenehard, CDO Sanofi. Intersting read with the MIT Sloan Management review. 👇