Research Study 2026

Strategic Perspectives on Healthcare AI Innovation

A multi-stakeholder qualitative study examining how healthcare AI leaders conceptualize risks, opportunities, and value drivers shaping the next decade of innovation.

Timeline: Q1-Q2 2026 Method: Expert Interviews Scope: US, China, Asia-Pacific

20+

Expert Interviews

5

Innovation Clusters

3

Regions Compared

Q2

Publication Target

Research Questions

This study addresses two central questions that shape healthcare AI strategy and policy:

  1. How do healthcare AI leaders conceptualize key risks, opportunities, and value drivers influencing AI innovation in clinical settings?
  2. How do current AI product strategies reflect perceived market demand and unmet needs within healthcare systems across different regulatory environments?

Innovation Clusters Under Study

We're examining five distinct areas where AI is transforming healthcare delivery:

Ambient Documentation

AI scribes reducing physician burnout by automating clinical notes from conversations.

Abridge, Ambience, Nabla, Microsoft DAX

Diagnostic AI

Decision support systems for faster, more accurate clinical reasoning.

Viz.ai, Regard, Google AMIE, OpenEvidence

Patient-Facing Agents

AI-powered health companions for chronic care, medication adherence, navigation.

Hippocratic AI, Ping An, K Health

Drug Discovery

Generative AI accelerating molecule design and clinical development.

Isomorphic Labs, Insilico, XtalPi, insitro

Target Interview Participants

We're reaching out to leaders across startups, big tech, academia, health systems, and investors:

Name Organization Why Interesting Links Featured
Shiv Rao, MD
Startup		 Abridge
CEO & Founder		 Practicing cardiologist who built $5.3B AI scribe. Still takes hospital shifts. Deployed at 150+ health systems (Mayo, Kaiser). 60% less documentation time. LinkedIn TIME100 AI
Munjal Shah
Startup		 Hippocratic AI
CEO & Co-Founder		 Serial entrepreneur (sold to Google & Alibaba). Stanford AI Master. Safety-first patient AI. $126M Series C at $3.5B. LLM beat GPT-4 on 105/114 healthcare exams. LinkedIn · Web Bio
Chris Mansi, MD
Startup		 Viz.ai
CEO & Co-Founder		 Neurosurgeon-turned-entrepreneur. Eric Schmidt (Google) was his Stanford professor & seed investor. 13 FDA-approved algorithms. 1,700 hospitals. LinkedIn TIME100 AI
Daphne Koller, PhD
Startup		 insitro
CEO & Founder		 Stanford's first ML hire. Co-founded Coursera. MacArthur Fellow. National Academy of Science. TIME100 2012. PhD at 25. LinkedIn WEF Profile
Demis Hassabis, PhD
Big Tech		 Google DeepMind
CEO & Co-Founder		 2024 Nobel Prize in Chemistry for AlphaFold. Created AlphaGo. AlphaFold used by 2M+ scientists in 190 countries. One of most-cited papers ever. LinkedIn · X Nobel
Karan Singhal
Big Tech		 OpenAI
Head of Health AI		 Created Med-PaLM at Google (Nature). Moved to OpenAI 2024. Building "ChatGPT for Healthcare." Co-founded Stanford's first AI ethics class. LinkedIn · X · Scholar No Priors
Eric Topol, MD
Academic		 Scripps Research
Executive VP		 "Dean of Digital Medicine." 1,300+ papers, 300k citations. 675k Twitter followers. Author: Deep Medicine. TIME100 Health 2024. Medscape Editor-in-Chief. LinkedIn · X (675k) · Scholar Scripps
Nigam Shah, PhD
Academic		 Stanford Health Care
Chief Data Scientist		 h-index 88. 350+ publications. Co-founder of 3 companies. Created OHDSI and Coalition for Health AI (CHAI). 9 patents. LinkedIn · Scholar Stanford
Suchi Saria, PhD
Academic		 Johns Hopkins
Professor & Lab Director		 Sepsis AI reduces mortality 18%. TIME Best Inventions 2023 & 2024. Lost nephew to sepsis - work is personal. Founded Bayesian Health. LinkedIn · Lab Nature Med
Micky Tripathi, PhD
Health System		 Mayo Clinic
Chief AI Officer		 Former US National Coordinator for Health IT. Former HHS Chief AI Officer. Now governs enterprise AI adoption at Mayo. LinkedIn HLTH 2025
Daniel Yang, MD
Health System		 Kaiser Permanente
VP AI & Emerging Tech		 Leading largest generative AI rollout in healthcare history (24,000 physicians on Abridge). Fastest Kaiser tech implementation in 20+ years. Search LinkedIn Industry panels
Vinod Khosla
Investor		 Khosla Ventures
Founder		 Controversial "80% of doctors" prediction. Now: doctors managing AI "interns." Sun Microsystems co-founder. India AI Summit: free AI doctors for 1.5B people. LinkedIn · X Essay
Kuan Chen
China		 Infervision
CEO & Founder		 Leading medical imaging AI in China. Scaled deployment across Chinese hospitals. COVID-19 early diagnostic use. Search LinkedIn Imaging conferences
Ma Jian
China		 XtalPi
CEO		 AI + quantum computing for drug discovery. Partnerships with Pfizer, Eli Lilly. More pharma deals than most US startups. Search LinkedIn Pharma partnerships

Full database with 50+ leaders available in shared Google Drive. Additional targets identified via systematic review of recent publications and conference appearances.

AI-Assisted Research Methodology

We use AI tools throughout the research process to maximize efficiency and rigor:

🔍

Discovery

AI-powered deep research to identify leaders, map networks, and track recent publications

📝

Data Collection

Structured surveys with AI-generated adaptive follow-ups based on responses

🧠

Analysis

LLM-assisted thematic coding and pattern recognition across interview transcripts

✍️

Synthesis

AI-augmented writing with human oversight for accuracy and nuance

Why AI-Assisted Research?

Traditional qualitative research methods can take 6-12 months. By strategically integrating AI tools, we compress the timeline to 6-8 weeks while maintaining methodological rigor:

  • Faster literature mapping: AI synthesizes hundreds of papers and identifies key themes in hours, not weeks
  • Parallel outreach: AI helps personalize outreach at scale while maintaining authenticity
  • Real-time coding: Interview responses are thematically coded as they come in, enabling iterative refinement
  • Bias detection: AI flags potential blind spots in our analysis for human review
  • Transparent process: All AI-assisted steps are documented for reproducibility

Tools used: Claude (Anthropic), GPT-4 (OpenAI), Grok Deep Research, Semantic Scholar API. Human researchers review and validate all AI outputs.

What Makes This Study Different

🌏

US-China Comparison

Systematic analysis of different innovation models rarely covered in Western literature.

🔧

Builder Perspectives

Interviews with founders and leaders actively deploying AI in clinical settings.

⚖️

Multi-Stakeholder

Combining views from startups, academia, health systems, and investors.

Key Topics Explored

Perceived Opportunities

  • Administrative burden reduction
  • Diagnostic accuracy improvement
  • Drug discovery acceleration
  • Workforce shortage mitigation
  • Personalized medicine at scale
  • Global access expansion

Perceived Risks

  • Clinical safety and hallucinations
  • Algorithmic bias and equity
  • Regulatory uncertainty
  • Data privacy and security
  • Clinician deskilling
  • Implementation barriers

Research Team

Samandika Saparamadu, PhD

Lead Researcher

Johns Hopkins Bloomberg School of Public Health. Associate Editor, APJLM. President, Asian Lifestyle Medicine Council.

Dominik Dotzauer, MD

Co-Investigator

Editor, APJLM. Digital health builder with focus on AI applications in clinical practice.

Publication Plan

Target Journals: Lancet Digital Health, NEJM AI, npj Digital Medicine

Affiliation: Johns Hopkins Bloomberg School of Public Health

Pre-print will be available on medRxiv by Q2 2026. The study will be submitted for peer review following completion of all interviews and thematic analysis.

Share Your Perspective

We're inviting healthcare AI leaders to participate in a 30-45 minute interview. Your insights will shape understanding of this rapidly evolving field.

Express Interest