
AI Outperforms Doctors on Cancer Reports
When algorithms read pathology reports better than the oncologists who ordered them, the bottleneck in cancer care shifts from diagnostic accuracy to human administrative capacity.
Discover the newest research about AI innovations in 🧪 Pathology.

When algorithms read pathology reports better than the oncologists who ordered them, the bottleneck in cancer care shifts from diagnostic accuracy to human administrative capacity.

High-tech cancer diagnostics are currently a luxury of the wealthiest health systems, but a shift in how we analyze basic tissue slides could soon level the playing field.

Hiring a commercial AI to read breast cancer biopsies does not just introduce errors; it forces clinicians to choose which specific flavor of diagnostic failure they can tolerate.

A new AI tool reveals that many prostate cancers upgraded during active surveillance were actually mischaracterized from the very start.

A new study shows that small, locally hosted AI models can clean up messy clinical text better than expected, without risking patient privacy.

A new deep-learning model diagnoses autoimmune hepatitis with expert-level accuracy in some scenarios, but its failure against hepatitis B reveals a critical diagnostic blind spot.

A new computational pathology model exposes the limits of human sight in oncology, diagnosing complex brain tumors in minutes rather than weeks.

Regulators just crossed a digital rubicon by accepting AI-generated pathology data to evaluate metabolic liver disease treatments.

AI models outperform physicians in summarizing complex cancer pathology reports, according to a recent study. 📊🩺

Philips expands digital pathology with cloud-enabled IntelliSite, enhancing diagnostics and productivity in healthcare organizations. 🏥💻