In this episode of ByteSight, our host Dr. Manasi A-Ratnaparkhe speaks with Dr. Heather Couture, Founder of Pixel Scientia Labs and host of the Impact AI podcast. With over 20 years of experience applying machine learning to biomedical imaging, Heather helps healthtech startups and research teams translate AI from promising prototypes to clinically trustworthy tools.
Together, we explorewhy so many AI models that perform perfectly in the lab fail in real-world healthcare and what it takes to close that gap. From dataset diversity to model validation across scanners and institutions, Heather offers a candid look at what’s missing in current AI development and how better design, validation, and collaboration can turn data into diagnostics that truly make a difference.
About Dr. Heather Couture
Heather is a machine learning consultant and digital pathology expert with a PhD in Computer Science. Through her company Pixel Scientia Labs, she guides early-stage MedTech teams in designing robust, interpretable AI systems for histopathology and cancer diagnostics. Her work bridges the gap between machine learning research and real-world clinical use, focusing on fairness, reproducibility, and explainability.
As the host of the Impact AI podcast, she spotlights innovators using AI for health, sustainability, and societal good. Heather’s approach combines deep technical expertise with a passion for making AI practical, equitable, and safe for deployment in high-stakes environments.
When Lab Results Don’t Translate
Heather describes a recurring pattern she has seen across the industry:
“Most AI models perform beautifully in the lab, but they frequently fail in the real world. And that could be a biomarker that looks bulletproof on an internal test, but collapses in external trials, or a tumor classifier that breaks when applied to slides from a new scanner or staining protocol.”
The conversation explores why these failures occur often due to limited or biased training data and how teams can design for generalization from the start. She emphasizes that AI built on diverse, well-curated datasets is far more likely to serve patients equitably and withstand the variability of real-world clinical environments.
Building AI That Generalizes
A key insight from the discussion is that robustness must be intentional. Heather outlines how startups can identify data gaps early, validate across unseen labs, and integrate domain expertise throughout the AI lifecycle. From stain variability to scanner calibration, even small technical inconsistencies can derail performance and addressing them requires a culture of collaboration between pathologists, engineers, and data scientists.
Final Takeaway
For Heather, the future of digital pathology isn’t just about smarter algorithms, but it’s about reliable, interpretable, and inclusive AI. AI that only works in the lab won’t change healthcare. The real impact comes when it works for every patient, in every setting.
Learn more about Dr. Heather Couture
