Clinical Artificial Intelligence in 2026. Accuracy, Education, and Guardrails
Artificial intelligence is evolving fast in medicine. But how accurate is it. And are we building it safely?
In this episode of DigiPath Digest, I review five new studies shaping digital pathology, radiology, burn diagnostics, and agent-based large language model systems. We discuss accuracy gains, hallucination filtering, education challenges, and why safeguards are essential before clinical deployment.
Clear. Practical. Evidence-based.
⏱ Topics & Timestamps
[00:02] Introduction
Weekly journal club on digital pathology and artificial intelligence.
[05:13] Hallucination Filtering in Radiology
Using Discrete Semantic Entropy to detect hallucination-prone responses in Vision Language Models.
Accuracy improved from 51.7 percent to 76.3 percent after filtering high-entropy answers.
[15:04] Artificial Intelligence in Pathology Training
Supervised use during residency.
Balancing artificial intelligence adoption with preservation of morphological analysis and critical thinking.
[20:12] Colorectal Cancer Lymph Node Detection
Two-stage classification and segmentation model in Whole Slide Imaging.
Recall 1.0. Specificity 0.935. Dice coefficient 0.818.
Artificial intelligence as a second opinion.
[25:04] Burn Depth Prediction with Artificial Intelligence
Tissue Doppler Elastography and Harmonic B-mode ultrasound combined with artificial intelligence.
90 to 95 percent accuracy in human subjects.
[31:20] Agent-Based Large Language Model Systems
OpenManus and Manus evaluated in clinical simulations.
Up to 60.3 percent accuracy. High computational cost.
89.9 percent of hallucinations filtered by safeguards.
[40:08] Patient Access to Pathology Images
Why viewing pathology slides can empower patients and improve communication.
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