What if the AI tools we trust for cancer diagnosis are not always correct? This episode of DigiPath Digest takes on the uncomfortable but critical question: can AI “lie” to us—and how do we verify its performance before adopting it in clinical practice?
Highlights:
- [00:02:00] Foundation models in action: Deployment of a fine-tuned pathology foundation model for EGFR biomarker detection in lung cancer—reducing the need for rapid molecular tests by 43%.
- [00:08:41] Bone marrow AI misclassifications: Why automated digital morphology still struggles with consistency across leukemia and lymphoma cases.
- [00:14:45] Lossy DICOM conversion: How file format changes can subtly—but significantly—affect AI model performance.
- [00:21:45] Federated tumor segmentation challenge: Coordinating 32 international institutions to benchmark healthcare AI fairly across diverse datasets.
- [00:27:47] AI in gynecologic cytology: Reviewing AI-driven Pap smear screening—promise, limitations, and why rigorous validation remains essential.
- [00:32:27] Takeaway: Trust but verify—AI tools must be validated before they can support or replace clinical decisions.
Resources from this Episode
- Nature Medicine – Fine-tuned pathology foundation model for lung cancer EGFR biomarker detection.
- Scientific Reports (Germany) – Study on how DICOM conversion impacts AI performance in digital pathology.
- Federated Tumor Segmentation Challenge – Benchmarking AI across 32 global institutions.
- Acta Cytologica – Review on AI in gynecologic cytology and Pap smear screening.
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