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Send us a text 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. Support the show Get the "Digital Pathology 101" FREE E-book and join us! |