No Priors Ep. 17 | With Karan Singhal

What if AI could revolutionize healthcare with advanced language learning models? Sarah and Elad welcome Karan Singhal, Staff Software Engineer at Google Research, who specializes in medical AI and the development of MedPaLM2. On this episode, Karan emphasizes the importance of safety in medical AI applications and how language models like MedPaLM2 have the potential to augment scientific workflows and transform the standard of care. Other topics include the best workflows for AI integration, the potential impact of AI on drug discoveries, how AI can serve as a physician's assistant, and how privacy-preserving machine learning and federated learning can protect patient data, while pushing the boundaries of medical innovation. 00:00 - Introduction 00:22 - Google's Medical AI Development 08:57 - Medical Language Model and MedPaLM 2 Improvements 18:18 - Safety, cost/benefit decisions, drug discovery, health information, AI applications, and AI as a physician's assistant. 24:51 - Privacy Concerns - HIPAA's implications, privacy-preserving machine learning, and advances in GPT-4 and MedPOM2. 37:43 - Large Language Models in Healthcare and short/long term use.

Sarah GuohostKaran SinghalguestElad Gilhost

May 17, 202342mWatch on YouTube ↗

WHAT IT’S REALLY ABOUT

Google’s Med-PaLM 2 Aims to Safely Transform Healthcare With AI

Karan Singhal, a lead researcher on Google’s Med-PaLM 2, explains how large language models are being adapted for high‑stakes medical use, with a focus on safety, accuracy, and responsible deployment. He traces the evolution from PaLM to PaLM 2 and Med-PaLM, detailing methods like instruction prompt tuning and improved pretraining objectives. The discussion digs into evaluation challenges, regulatory and privacy barriers, and why medical workflows can be a powerful testbed for AI alignment and safety research. Looking ahead, Singhal expects LLMs to raise the global standard of care, augment clinicians and scientists, and help solve key scientific and medical problems—if society can set realistic, benefit-aware safety bars.

IDEAS WORTH REMEMBERING

5 ideas

Start from strong general models, then cheaply align to the medical domain.

Med-PaLM builds on PaLM/PaLM 2’s general reasoning and knowledge, then uses data-efficient techniques like instruction prompt tuning with doctor-written examples to adapt behavior for long-form medical QA without costly full retraining.

Choose tuning method based on how much labeled domain data you actually have.

If you have only a handful of examples, use prompting; with tens of examples, consider prompt tuning; and once you have 100+ high-quality labeled examples, full fine-tuning tends to deliver the best performance relative to cost.

Robust evaluation in medicine must go beyond benchmarks to human review in real workflows.

Existing medical AI work often relied on limited multiple‑choice benchmarks; Med-PaLM emphasized systematic automated evaluation plus detailed human evaluation by clinicians and laypeople, but Singhal stresses the need for workflow‑embedded, outcomes‑aware studies.

The bar for safety should be “better than the realistic status quo,” not perfection.

While stakes are high in medicine, patients and physicians already rely on imperfect web searches and time‑pressed clinicians; indecision or inaction is also a decision, so policy must weigh both risks and missed benefits of withholding capable systems.

Near-term impact will come from assistant and back-office roles, then higher-stakes support.

LLMs are already being piloted for documentation and billing; Singhal expects next waves in radiology assistance, report QA, telemedicine augmentation, and clinical decision support—initially as physician co-pilots, not replacements.

WORDS WORTH SAVING

5 quotes

Indecision is a decision as well.

— Karan Singhal

The ideal thing would be to have access to all of the data, but in a privacy-preserving way.

— Karan Singhal

It’s hard to tell the difference between models and physicians.

— Karan Singhal

There is a chance to go for the jugular here in terms of health information.

— Karan Singhal

In the long term when things go really well with AI, it’s going to be because we’ve solved a lot of the most pressing scientific problems of today.

— Karan Singhal

Karan Singhal’s path into socially responsible AI and medical AIFrom PaLM to PaLM 2 and how Med-PaLM is specializedTechniques for domain alignment: instruction tuning, prompt tuning, and fine-tuningEvaluation of medical LLMs: benchmarks, human evaluation, and safetyRegulation, HIPAA, privacy, and federated learning in healthcare AIAI as physician assistant, documentation tool, and scientific research aidMedical AI as a laboratory for alignment, hallucination reduction, and scalable oversight

High quality AI-generated summary created from speaker-labeled transcript.

Get more out of YouTube videos.

High quality summaries for YouTube videos. Accurate transcripts to search & find moments. Powered by ChatGPT & Claude AI.