From Curiosity to Contribution: How Oncologists Can Engage in AI and Innovation

In this edition of The Way Ahead, Waqas Haque, MD, MPH, examines how oncologists can move from passive awareness of artificial intelligence (AI) to meaningful participation in the innovation shaping cancer care. As AI becomes increasingly embedded in oncology workflows—from diagnosis and pathology to documentation, trial matching, and patient communication—the central question is no longer whether these technologies matter, but how clinicians can engage with them in ways that improve patient outcomes.

The Growing Role of AI in Oncology 

Artificial intelligence is no longer a future concept. Over half of oncologists are using AI to help diagnose malignancies or ask questions about clinical management, according to small survey data. It is already influencing how we read scans, interpret pathology, match patients to trials, document care, and communicate with patients. Recent reviews highlight its growing role across genomics, clinical decision support, and operational workflows, with future directions pointing toward multimodal data integration and increasingly autonomous systems that may reshape how care is delivered.

For oncologists and oncology fellows, the more relevant question now is not whether AI matters, but how to engage with it in a way that ultimately promotes patient outcomes. The good news is that there is not just one path, and most do not require providers stepping away from clinical medicine for a job in Silicon Valley or Wall Street.

Venture and Innovation Exposure

One route is venture and innovation exposure. This does not mean becoming a solo founder or joining a venture fund. It means understanding how new technologies are evaluated, funded, and implemented. A recent NEJM Catalyst article makes the case that physicians benefit from exposure to innovation frameworks such as user-centered design, systems thinking, and market validation—skills that help translate clinical insight into real-world solutions. It also notes that more than 25% of healthcare unicorns between 2015 and 2024 had at least one clinician founder (not necessarily with residency training under their belt), underscoring that physicians are already shaping the innovation ecosystem. 

For oncologists, this type of literacy is particularly valuable. AI tools are entering workflows that are already complex and emotionally demanding. Clinicians who understand both the bedside problem and the implementation challenges are uniquely positioned to evaluate whether a tool improves care (or simply adds friction). 

Self-Paced Learning 

A second route is self-paced learning. This is often the most realistic starting point. While formal innovation programs are still concentrated at select institutions, the same article mentioned above emphasizes that exposure can come through online courses, workshops, and peer-led initiatives that are accessible regardless of geography.

In practice, the most effective approach is not to “learn AI” broadly, but to follow specific clinical questions. How is AI changing pathology workflows? Where is it being used in radiation planning? How are large language models affecting documentation or patient communication? Narrow questions tend to lead to deeper understanding—and more clinically relevant insights. 

Joining a Community

A third route is joining a community. Innovation is easier to sustain when it is social, and AI hasn’t found a way to replace that (yet). Communities provide language, mentorship, and exposure to people working across clinical, technical, and industry domains. The NEJM Catalyst article highlights physician-led and open-access communities as scalable ways to build fluency in innovation, even for clinicians outside major academic centers. 

This is especially important in oncology, where meaningful progress often sits at the intersection of disciplines. Many of the most impactful ideas emerge not from isolated work, but from collaboration between clinicians, data scientists, informaticians, and industry partners.

Content Creation

A fourth route is content creation. This may be the most underestimated path. Writing, speaking, and curating thoughtful commentary are not peripheral—they represent a form of leadership that can be part of community building. In a space filled with rapid advances alongside frequent hype, oncologists who can interpret new developments through a clinical lens provide real value. Content creation also sharpens thinking. It forces clarity about what matters, what is overpromised, and where gaps still exist.

Conclusion

Importantly, these routes are not mutually exclusive. An oncologist might begin with self-study, join a community, start writing about emerging trends, and eventually contribute to evaluating or advising on new technologies. The broader point is not that every oncologist should become an entrepreneur. It is that oncology expertise should remain central as AI tools are designed, implemented, and scaled.

Innovation literacy should be viewed as complementary to clinical training, and not in competition with it. Looking ahead, AI will continue to expand across the oncology workflow, from diagnosis to survivorship. But the trajectory of that expansion will depend, in part, on whether oncologists choose to engage—not just as users, but as evaluators, collaborators, and interpreters.

About the Speaker

Waqas Haque, MD, MPH, is a second-year Hematology/Oncology Fellow at the University of Chicago. He recently completed his Internal Medicine Residency at New York University (NYU) in a Clinical Investigator Track. Dr. Haque has balanced his patient care work with a variety of research projects. During his fellowship training at the University of Chicago, he plans to further his work in innovative clinical trial design, value-based care delivery to cancer patients, and clinical investigation.