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Utilizing Generative AI to Enable Precision Healthcare

• 0:00:13 Introduction: Hoyin (Hoy) Pan, General Manager of Microsoft Health Futures, is introduced as an expert in healthcare AI.

• 0:00:57 Hoyin's Role: Hoyin leads biomedical AI research and incubation for precision health, focusing on multimodal generative AI to unlock population-scale real-world evidence.

• 0:01:10 The Challenge of Treatment Response: The ideal scenario is every patient responding to prescribed treatment. The reality is many patients don't, particularly in cancer treatment.

• 0:01:33 Immunotherapy Example: Immunotherapy (e.g., Keytruda) can be highly effective, but overall response rates are around 20-30%.

• 0:01:52 Clinical Trials as Last Hope: When standard care fails, clinical trials become crucial, but access is limited.

• 0:02:05 Marty's Story: A successful AI researcher, Marty, was diagnosed with cancer but found a matching trial that cured him.

• 0:02:16 Limited Trial Access: Most patients aren't as fortunate as Marty; only a small portion find matching trials, and many trials fail due to lack of patients.

• 0:02:29 Drug Development Challenges: Developing new drugs is expensive and time-consuming, becoming unsustainable in the era of precision health.

• 0:02:50 Drug Development Costs: Most costs are in later-stage clinical trials and post-market surveillance, offering a significant opportunity for disruption.

• 0:03:15 Phase Three Trial Cost: A single phase three cancer trial can cost hundreds of millions of dollars.

• 0:03:31 Harnessing AI: AI can unlock population-scale real-world evidence, leveraging the rapid digitization of patient records.

• 0:03:51 Patient Journey as a Mini Trial: Every patient's journey provides valuable data, and unlocking its insights is like a "population-scale free lunch."

• 0:04:31 Multimodal Data: Patient journeys involve rich, multimodal information (medical imaging, multiomics, etc.) that, when combined, can create a holistic patient representation.

• 0:04:51 Machine Learning for Precision Health: Precision health involves learning a function that inputs multimodal patient journeys and outputs key medical events.

• 0:05:11 Challenges: Patient journeys are longitudinal, with missing information, noise, and biases.

• 0:05:29 Generative AI's Promise: Generative AI can compress observable information, predict missing data, and forecast medical events.

• 0:05:56 Overarching Agenda: Harness population-scale data to create high-fidelity patient embeddings that act as digital twins.

• 0:06:08 Patient-Level Reasoning: Using patient embeddings for population-scale reasoning (e.g., getting millions of second opinions from similar patients).

• 0:06:56 Exciting but Challenging: Even the best frontier models have major competency gaps for non-text modalities in biomedicine.

• 0:07:14 Curriculum Learning: Addressed by curriculum learning over three "giant free lunches".

• 0:07:19 First Free Lunch Stems from unimodal data, such as annotated images. Pretraining modality specific encoder and decoder.

• 0:07:54 Modality Specific Challenges: Major research innovations are needed to tackle the challanges.

• 0:08:02 Digital Pathology: Digital pathology contains crucial information, but its large size poses computational challenges.

• 0:08:46 Dilated Attention: A promising direction is incorporating "dilated attention" to handle large-scale image processing.

• 0:09:51 GigaPath: Creation of GigaPath, the world's first digital pathology foundation model that scales Transformer to whole-slide images.

• 0:10:23 Uni-modal Pre-training Limitation There are bigger challenges because each modality is speaking its own distinct language.

• 0:11:04 Interlingual Modality Introduce a resource rich interlingual, such as English. Text is the ideal candidate.

• 0:12:08 LaMDA Paradigm: Leveraging the LaMDA paradigm.

• 0:13:13 Pixel-Level Decoder: Extension to include a pixel-level decoder.

• 0:13:24 BioMedParse: Development of BioMedParse for holistic image analysis (object recognition, detection, segmentation) via a natural language interface.

• 0:14:07 Third Free Lunch: The patient journeys themselves are the last free lunch, enabling self-supervised learning by predicting the next medical event.

• 0:14:53 Universal Embedding Calculus: Develop an universal embedding calculus for Precision Health.

• 0:15:12 Providence Health System: Providence uses the research system daily in tumor boards to screen patients.

• 0:16:02 Clinical Research Hypothesis: Capability to conjure up virtual case arms and control arms to conduct clinical research hypothesis generation.

• 0:16:27 Collaboration: Collaboration is crucial, involving Microsoft Research, academia, health systems, and life sciences companies.

• 0:16:53 Public Availability: Many of the highlighted biomedical models are publicly available in Azure AI Foundry.

• 0:16:59 Call to action Bring positive disruption to scale drug development and improve.