Artificial intelligence is quickly becoming part of oncology data workflows, raising a real question for Oncology Data Specialists (ODS-C): is it here to support the work, or replace it?
As AI tools continue to evolve, it’s worth taking a closer look at what they can realistically do within cancer registry and abstracting workflows, and where human expertise remains essential.
What AI Is Already Doing in Oncology
AI continues to evolve as a powerful tool in healthcare. In oncology, it has the ability to process massive amounts of patient data, including clinical findings, imaging, genetics, and pathology. It can assist with grading, staging, treatment planning, and even predicting patient outcomes or identifying the most effective therapies.
Much of this advancement is currently geared toward supporting physicians—particularly surgical, medical, and radiation oncologists—by enhancing decision making and improving patient care.
These capabilities are increasingly being applied within oncology data management and cancer registry workflows, particularly in large health systems working to improve speed and consistency.
How AI Supports Cancer Registry Workflows
For ODS-C professionals, AI is beginning to play a role in streamlining certain tasks. It can assist with pre-coding fields and extracting structured data from clinical documentation such as labs, imaging, and pathology reports, potentially reducing the time it takes to complete an abstract.
At first glance, this sounds like a major efficiency gain. It is. But it also raises an important question: Can AI handle the full complexity of cancer abstracting?
The Limitations of AI in Cancer Abstracting
Despite its capabilities, AI still faces significant challenges when it comes to replacing the work of an ODS-C.
Cancer abstracting is rarely black and white. In many cases, it exists in a gray area that requires critical thinking, interpretation, and experience. AI is only as reliable as the data it is given, and in healthcare, that data can often be incomplete, inconsistent, or ambiguous.
When applied to real-world cancer registry work, several challenges quickly emerge, including:
- Data quality and accuracy
- Patient privacy and security
- Keeping up with evolving standards like the AJCC Staging System
- Potential bias based on facility-specific data
- The high cost of implementing and customizing AI systems
These are not small challenges, and they highlight the gap between automation and true understanding.
Why AI Won’t Replace ODS-C Professionals
While AI will undoubtedly shape the future of oncology data, it is unlikely to replace ODS-C professionals entirely.
AI can assist, but it cannot replicate human judgment. It cannot:
- Reliably interpret ambiguous terminology in pathology reports
- Identify subtle inconsistencies across records
- Apply nuanced decision making in complex cases
- Ensure the level of data integrity required for cancer registries
Human oversight remains critical to maintaining data accuracy, compliance, and registry integrity. The ODS-C role goes far beyond entering data. It involves interpreting complex information, validating what’s documented, and ensuring the overall quality and accuracy of each case.
The Future of Oncology Data: Evolving Roles
Rather than replacing ODS-C professionals, AI is more likely to become a collaborative tool that enhances productivity and reduces manual workload.
We may see a future where:
- Abstracting becomes faster and more efficient
- Routine data entry is automated
- ODS-C professionals focus more on quality review and complex cases
AI isn’t removing the role. It’s shifting the focus toward higher-value work.
Where Oncology Data and AI Are Headed
Organizations like the National Cancer Institute and the National Library of Medicine continue to publish research exploring both the potential and the limitations of AI in healthcare. These resources offer valuable insight into where the field is headed and the challenges that remain.
The Bottom Line for ODS-C Professionals
AI will continue to influence how oncology data is collected and processed, particularly as tools improve in speed and data extraction.
But cancer abstracting goes beyond pulling information from a chart. It requires interpretation, validation, and accountability, areas where human expertise remains essential.
For ODS-C professionals, the role isn’t disappearing. It is becoming more focused on ensuring data accuracy, consistency, and integrity across increasingly complex cases.
As AI evolves, the value of experienced oncology data specialists becomes even more important, not less.
As oncology data workflows continue to evolve, having experienced ODS-C professionals in place remains critical to maintaining accuracy and consistency. RCM supports healthcare organizations with oncology data and cancer registry professionals equipped to navigate complex cases and changing requirements.