AI outperforms doctors at summarizing complex cancer pathology reports

Study authors Drs. Mohamed Abazeed (right), Yirong Liu and Troy Teo (left) demonstrates a prototype AI tool that summarizes cancer pathology reports, shown here in a radiation oncology setting.
Photo Credit: Northwestern University

Scientific Frontline: Extended "At a Glance" Summary: AI Summarization of Cancer Pathology Reports

The Core Concept: Open-source artificial intelligence models can generate more comprehensive and structured summaries of complex cancer pathology reports compared to physician-written versions.

Key Distinction/Mechanism: Unlike manual summarization, which is subject to time constraints and cognitive overload, these AI systems analyze extensive longitudinal data to consistently capture critical microscopic, immunohistochemical, and molecular findings. The AI serves as an augmentative tool to support clinical decision-making and ensure no vital genetic details are overlooked.

Origin/History: A Northwestern Medicine study published in April 2026 evaluated 94 de-identified lung cancer pathology reports to assess the efficacy of large language models in a clinical oncology setting.

Major Frameworks/Components:

• Open-Source Large Language Models (LLMs): Utilization of models that can be run locally to protect patient privacy, specifically Meta's Llama (3.0, 3.1, 3.2), Google's Gemma 9B, Mistral 7.2B, and DeepSeek-R1.
• Histopathological Analysis: Extraction and synthesis of microscopic tumor characteristics.
• Immunohistochemical Evaluation: Processing of protein expression testing results.
• Genomic and Molecular Data Processing: Reliable identification of actionable genetic markers critical for targeted cancer therapies.

Branch of Science: Clinical Oncology, Pathology, Clinical Informatics, Artificial Intelligence, and Bioinformatics.

Future Application: The development of secure, localized software applications that allow physicians to input multipage pathology reports and instantly receive standardized, highly accurate summaries to streamline chart reviews and clinical documentation.

Why It Matters: As cancer care relies increasingly on complex biomarker testing and genetic sequencing, patient reports grow exponentially in length and detail. Utilizing AI to reliably synthesize this data minimizes the risk of human error, reduces the administrative burden on physicians, and ensures that actionable clinical data is accurately leveraged to optimize patient care.

Study authors Drs. Mohamed Abazeed (left), Yirong Liu and Troy Teo (right) test a prototype AI tool that summarizes cancer pathology reports, shown here in a radiation oncology setting.
Photo Credit: Northwestern University

AI models can generate more complete summaries of complex cancer pathology reports than physicians, according to a new Northwestern Medicine study that tested six models developed by Meta, Google, DeepSeek and Mistral AI.

The findings offer a potential fix to a growing challenge in oncology. As biomarker testing expands, and patients live longer, pathology reports have become increasingly detailed and longitudinal, often spanning multiple institutions and requiring clinicians to synthesize large volumes of information under significant time pressure. The study was published in JCO Clinical Cancer Informatics, a journal from the American Society of Clinical Oncology.

In this study, several open-source AI models consistently produced summaries that were more comprehensive than physician-written versions, particularly in capturing molecular and genetic findings that are critical for treatment decisions.

“As cancer care becomes increasingly complex, the burden of synthesizing complex reports is growing rapidly,” said senior study author Dr. Mohamed Abazeed, chair and professor of radiation oncology at Northwestern University Feinberg School of Medicine. “What we’re seeing is that AI can help ensure critical pathological and genomic details are consistently captured — not as a replacement for physicians, but as a tool to augment clinical decision-making.”

How the study was conducted 

The Northwestern investigators analyzed 94 de-identified pathology reports from lung cancer patients. These reports included detailed text describing:

• Histopathological findings (microscopic tumor characteristics)
• Immunohistochemical results (protein expression testing)
• Molecular and genetic data relevant to treatment

The AI models analyzed the text content of these reports and generated structured summaries.

The AI-generated summaries were compared to clinical summaries previously written by physicians. A panel of oncologists assessed each summary for accuracy, completeness, conciseness and potential clinical risk. Across models, AI-generated summaries were consistently rated as more complete, with the largest differences observed in the inclusion of molecular and genomic findings.

“If AI can reliably synthesize these reports, clinicians can review key findings more efficiently, important genetic details are less likely to be overlooked and documentation becomes more standardized,” said study co-author Troy Teo, instructor of radiation oncology at Feinberg. “This could help physicians focus more on patient care.”

Llama 3.1 and DeepSeek performed best

The scientists evaluated six open-source language models: Meta’s Llama 3.0, 3.1 and 3.2 models, Google’s Gemma 9B, Mistral 7.2B and DeepSeek-R1. These are not chatbots like ChatGPT, but systems that researchers can download and run locally. According to the study, the strongest performers were DeepSeek and Llama 3.1.

The Northwestern team is now developing an app using Llama 3.1 to eventually allow physicians to upload pathology reports and receive AI-generated summaries for their review. But the study authors emphasize that before deploying the app, they need more testing and validation studies.

AI as a second-opinion tool

The authors said they envision AI as a support layer that enhances, rather than replaces, clinical expertise. It could help highlight key findings, identify missing information and improve consistency in documentation.

“Patients with complex cancers might benefit the most,” said study first author Dr. Yirong Liu, a fifth-year resident in radiation oncology at McGaw Medical Center of Northwestern. “In cases where missing a key pathological finding or an actionable genetic marker could change treatment decisions, ensuring that information is consistently captured is critical.”

“Patients are living longer and undergoing repeated biopsies and genetic sequencing,” Liu added. “Their reports can span dozens of pages. Even a single missed detail can impact care, and this is where AI may provide meaningful support.”

Funding: Troy Teo received funding from the Canadian Institute of Health Research (grant CIHR-472392) and from Amazon Web Services’ Social Impact funding.

Published in journal: JCO Clinical Cancer Informatics,

Title: Toward Automating the Summarization of Cancer Pathology Reports Using Large Language Models to Improve Clinical Usability

Authors: Yirong Liu, MD, PhD, Jacob John, MS, Sagnik Sarkar, MS, Abdul Zakkar, MD, Paul Kinkopf, BS, P. Troy Teo, PhD, and Mohamed E. Abazeed, MD, PhD

Source/Credit: Northwestern University | Ben Schamisso

Reference Number: ongy041026_01

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