How AI Is Revolutionizing Precision Oncology With Ian Maurer and Jasmin Hundal, MD, MS, MPH

Uses of AI in Oncology

Mr. Maurer introduced GenomOncology as a precision oncology software provider specializing in molecular pathology, clinical reporting, trial optimization, and data enablement. He explained how AI can structure unstructured medical data from electronic health records (EHRs) and other sources to support clinical decision making.

As he stated: “We take all the FDA approvals, the NCCN guidelines, the clinical trials for cancer, and we codify them into a structured system to match patients to trials or therapies, but also to search patient databases to find potential matches for trial enrollment.” This approach provides clinicians with access to well-organized, actionable information to enhance patient care.

Addressing Unstructured Data With AI

One of the critical challenges in precision oncology is dealing with unstructured data, such as complex reports and fragmented EHR entries. Mr. Maurer elaborated on the development of natural language processing (NLP) systems and transformer models to address this issue. GenomOncology has built a system combining a rules-based approach and NLP to turn PDFs into a structured database.

These advancements enable healthcare providers to access curated, patient-specific insights in real time, significantly reducing the manual burden of data extraction and interpretation.

AI as an Augmentation, Not a Replacement

One recurring theme of the discussion was the role of AI as an augmentation tool rather than a replacement for clinicians. Maurer emphasized, “AI does not have judgment, and it does not have any accountability; therefore, it cannot be the one making decisions. The human has to make the decisions; the AI is just there to be of service to that.”

By acting as an “assistant” or “amplifier,” AI tools can streamline workflows, reduce decision fatigue, and support oncologists in making evidence-based decisions without removing them from the process.

Bridging Gaps in Oncology Care

Dr. Hundal highlighted the potential of AI to bridge gaps in oncology care, particularly in underserved or rural areas where access to clinical trials and specialized treatments is limited. Mr. Maurer agreed, explaining how AI can enhance efficiency and reduce clinician burnout. “There’s not enough time in the day to keep up with it all. Let’s let the large language models do the work and organize it in a searchable database that can then be provided to the doctor.”

By summarizing and categorizing vast amounts of research and clinical data, AI can enable providers to stay informed about the latest advancements without being overwhelmed.

The Future of AI in Precision Oncology

Mr. Maurer shared his vision for the future of AI in healthcare, focusing on integrating generative AI models like ChatGPT to assist with summarization, clinical documentation, and patient communications. He underscored the importance of explainable AI (XAI) to maintain trust and accountability in medical decision making: “The goal is to have explainable AI that can make recommendations with the evidence for it, showing why it’s been matched the way it has.”

He also highlighted the potential of voice-activated assistants and NLP-driven tools to transform the clinician’s interaction with EHRs, allowing them to focus more on patient care rather than administrative tasks.

Key Takeaways

1. AI in Precision Oncology: AI and ML are powerful tools for structuring unstructured data, optimizing clinical decision making, and enhancing trial enrollment processes
2. Augmentation, Not Replacement: AI serves as an assistant to clinicians, helping to reduce burnout, streamline workflows, and improve decision accuracy without replacing human judgment.
3. Bridging Care Gaps: By making clinical guidelines and trial opportunities more accessible, AI can address disparities in cancer care, particularly in rural or underserved areas
4. Explainable AI is Essential: Trustworthy AI systems must provide transparent, evidence-backed recommendations to support clinicians without generating “hallucinated” information

Conclusion

The integration of AI into precision oncology is not about replacing human expertise but augmenting it. As Mr. Maurer aptly put it, “Humans do jobs; tools do tasks.” By leveraging AI tools thoughtfully, oncologists can provide more personalized, efficient, and effective care, ultimately improving outcomes for patients.

About the Speakers

Ian Maurer is the Chief Technology Officer at GenomOncology, a precision medicine software company that provides the healthcare community with data-driven insights to improve cancer care. At GenomOncology, he leads the full lifecycle of application design, architecture, development, and quality assurance of their products.

Jasmin Hundal, MD, MS, MPH, is a second-year Hematology/Oncology Fellow at the Cleveland Clinic in Cleveland, Ohio. Previously, she completed her Internal Medicine Residency at the University of Connecticut. Dr. Hundal’s main area of interest is the overlap between cardiometabolic health and cancer, particularly for breast cancer patients and survivors. Outside of work, she enjoys traveling, cooking, and hiking.