Google Health AI

Ingest X-ray DICOM image into Google Cloud DICOM store.

Trigger Cloud Function to send image to Med-Gemini-Vision API.

Med-Gemini analyzes the image for anomalies (e.g., pneumothorax).

API returns JSON with localized heatmap and descriptive findings.

System flags the study as 'Urgent' in the PACS workstation for immediate review.

Extract clinical notes from the EHR via FHIR API.

Pass text to Vertex AI Natural Language API with healthcare entities enabled.

Map extracted symptoms and diagnoses to normalized ICD-10 codes.

Cross-validate codes against Med-Gemini knowledge base.

Feed codes into billing software for automated claim submission.

Aggregate unstructured patient history and genomic sequencing data.

Utilize Med-Gemini's long context to analyze 10 years of patient encounters.

Apply BigQuery ML to identify phenotypic patterns matching known rare disorders.

Generate a summary for the specialist highlighting diagnostic gaps.

Recommend specific confirmatory lab tests.

Connect hospital patient portal to a Vertex AI Conversation agent.

Ground the agent in the hospital's specific protocols and the patient's record.

Patient asks about post-op medication side effects via mobile app.

AI provides an answer verified against the patient's current prescription list.

Escalate to a human nurse if the patient reports severe symptoms.

Upload molecular structures to Vertex AI Life Sciences platform.

Run AlphaFold simulations to predict protein-ligand binding affinity.

Use generative models to suggest molecular modifications for better stability.

Simulate metabolic pathways using BigQuery-based biological models.

Rank candidates for laboratory synthesis.

Scan internal EHR databases using FHIR search criteria.

Apply NLP to filter patients based on complex inclusion/exclusion criteria in clinical notes.

Med-Gemini reviews matching candidates for secondary compatibility.

Automate the generation of personalized recruitment letters.

Track enrollment progress in a Looker dashboard.

Stream real-time data from wearable devices into BigQuery.

Integrate socioeconomic data (SDOH) using Google's public datasets.

Train a custom AutoML model to predict risk of diabetic complications.

Deploy the model to identify high-risk cohorts weekly.

Trigger preventative care outreach via the care management platform.