Sahin Boydas
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AI in Healthcare

AI diagnostics, drug discovery, clinical workflows, regulatory navigation

121 articles·5 content types
Audience

Health tech founders, clinicians, health investors, policy makers

Pain Points

Regulatory hurdles, data privacy, clinical validation, adoption resistance

Subtopics
medical imaging AIdrug discoveryclinical trialshealth datatelemedicine AI
Opinion2026-03-14

Can AI Predict Clinical Trial Success?

However accurate, a trial-prediction model is worthless if no one can use it. Healthcare AI lessons: fix a workflow, embrace messy data, explain results.

Opinion2026-03-14

The Future of Personalized Medicine Isn't Genomics, It's AI-Driven Predictive Health

Genomics is one piece of personalized medicine. AI on real-world data from wearables, sleep, diet and records, plus a usable UI, drives prediction.

Opinion2026-03-10

Nobody Talks About the Data Moat Problem in AI Diagnostics—Here's How to Solve It

A static pile of medical data is no moat. The real one is a data engine: clinical workflow fit, human-in-the-loop labeling and a regulatory flywheel.

Opinion2026-02-25

What Investors Want in AI Mental Health

What healthcare AI investors weigh beyond the model: trust, compliance and execution, plus four rules for founders building AI mental health apps.

Opinion2026-02-12

Why 'AI in Radiology' Is a Bubble About to Burst (And What to Focus on Instead)

Radiology AI stalls on messy hospital data, tools outside PACS workflows and long sales cycles. Better bets: trial recruitment, admin work, drug discovery.

Opinion2026-02-11

Can AI Predict Clinical Trial Success?

What an AI model of clinical trial success weighs: team mix, FDA strategy, investor quality and adaptive design, and the case for outcome-based pricing.

Opinion2026-02-01

Can AI Predict Clinical Trial Success?

Nine in ten drugs fail in trials. How AI can flag likely Phase II and III outcomes early, and three founder mistakes: tech over problem, weak data, FDA.

List2026-01-31

7 Things I Learned Angel Investing in 20+ AI Healthcare Startups

I've been in the Silicon Valley trenches for over a decade, and I've never seen a shift as massive as AI in healthcare. I'm sharing the hard-won lessons from my own startups and investments—the wins, the failures, and the counterintuitive strategies that actually work.

Opinion2026-01-30

AI Drug Discovery: Why a Niche Wins

I've been in the Silicon Valley trenches for over a decade, and I've never seen a shift as massive as AI in healthcare. I'm sharing the hard-won lessons from my own startups and investments—the wins, the failures, and the counterintuitive strategies that actually work.

Opinion2026-01-26

Why AI Diagnostic Tools Fail in Hospitals

Why an accurate AI diagnostic tool can still fail: poor workflow fit, FDA clearance, biased data, buyers who want ROI and no billing code for payment.

How-To2026-01-23

How to Build a Clinical AI That Doctors Actually Trust: The Counterintuitive Guide

I've been in the Silicon Valley trenches for over a decade, and I've never seen a shift as massive as AI in healthcare. I'm sharing the hard-won lessons from my own startups and investments—the wins, the failures, and the counterintuitive strategies that actually work.

Opinion2026-01-21

The $1.2 Billion Mistake: New Data Shows Why Most Hospitals Are Implementing AI Wrong

Hospitals buy AI models, not solutions. Why tools that skip PACS and EHR integration fail clinicians, and a five-step playbook for workflow-native AI.

Opinion2026-01-17

Why 'AI in Radiology' Is a Bubble About to Burst (And What to Focus on Instead)

Radiology AI competes on accuracy, a race to the bottom; distribution, data and FDA clearance are the real moats. Better bets: mental health, drug R&D.

Opinion2026-01-09

The $1.2 Billion Mistake: New Data Shows Why Most Hospitals Are Implementing AI Wrong

Hospital AI fails on workflow and data, not algorithms. Why a strong sepsis model lost its pilot, and the fix: clinician shadowing, co-design, data work.

Opinion2026-01-06

Taking an AI Medical Device to Market

A strong lab model is the easy part of medical AI; messy hospital data, FDA 510(k) review and PACS integration are not. Start with workflow and the FDA.

Opinion2026-01-06

Why AI Diagnostic Tools Fail in Hospitals

Lab accuracy collapses on real hospital data, hospital sales cycles run 18 months and doctors resist replacement: why AI diagnostic startups fail.

How-To2026-01-01

How to Navigate the FDA Approval Process for Your Medical AI (The 2026 Playbook)

FDA strategy is the moat: hire a regulatory expert first, nail the intended use (510(k) vs PMA), build representative data and prove clinical utility.

Opinion2025-12-28

The Future of Personalized Medicine Isn't Genomics, It's AI-Driven Predictive Health

Predictive health AI wins on more than the model: data infrastructure, an early FDA plan, ROI for hospital buyers, EMR workflow fit and outcomes pricing.

Opinion2025-12-24

The 5 Unspoken Rules of Building a Defensible AI Moat in Biotech

Model accuracy isn't a moat in biotech AI. Five rules that are: workflow integration, stakeholder selling, explainability, FDA clearance and reimbursement.

Opinion2025-12-18

Labtree — Portfolio Company | Angel Investment by Sahin Boydas

AI-powered laboratory management and experiment optimization platform.