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How far can AI go in helping us diagnose disease—without losing the human judgment patients rely on?

In this episode, I break down four studies shaping the future of digital pathology, oncology, and neurology. From spatial biology updates at SITC to voice-based Alzheimer’s detection, deep learning for sarcoma prognosis, and new guidelines for safe AI deployment, this week’s digest highlights where AI is making a real impact—and where caution still matters.

Episode Highlights

1️⃣ SITC Trends & Spatial Biology (00:00 → 07:40)

I share key updates from SITC 2025, including the growing role of multiplex immunofluorescence (mIF) and the need for integrated staining-to-scanning workflows. I also preview new educational content and upcoming podcast guests in global AI research.

2️⃣ Digital Neuropathology & Alzheimer’s (07:40 → 13:01)

A major review confirms that digital neuropathology is now robust enough for large-scale Alzheimer’s studies—opening doors for computational tools to link histology with cognition.

3️⃣ Patient Safety in AI (13:01 → 19:56)

An Italian review underscores the foundations of trustworthy AI: dataset quality, transparency, oversight, and continuous validation. I discuss why “patient-centered AI” must remain our standard.

4️⃣ Voice Biomarkers for Cognitive Decline (19:56 → 26:43)

AI models analyzing short speech recordings are showing high accuracy for early Alzheimer’s detection. This could make future screening simple, noninvasive, and more accessible.

5️⃣ Deep Learning for Sarcoma Prognosis (34:06 → 35:59)

A multi-instance CNN outperforms FNCLCC grading by identifying prognostic patterns in tumor center and periphery regions, offering new insights into soft-tissue sarcoma biology.

Takeaways

• mIF is maturing quickly but needs standardized, end-to-end workflows.
• Digital neuropathology is ready for broader Alzheimer’s research.
• Safe AI requires multidisciplinary collaboration and rigorous validation.
• Voice biomarkers may become powerful tools for early cognitive assessment.
• Deep learning can refine prognosis and reveal hidden tumor patterns.

Resources

Hamamatsu (MoxiePlex) • Biocare Medical (ONCORE Pro X) • SITC Programs • Recent publications on AI biomarkers and computational pathology.

Thanks for listening—and for being part of this growing digital pathology community.

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