As healthcare increasingly shifts from reactive treatment to proactive prevention, artificial intelligence (AI) is emerging as a powerful tool for identifying disease earlier and personalizing patient care. In this episode of Exploring AI in Oncology, Dr. Waqas Haque, Hematology/Oncology Fellow at the University of Chicago, spoke with Dr. Sean Raj, Chief Innovation Officer and Chief Medical Officer at SimonMed, about how AI is transforming cancer detection, risk assessment, and patient education. Their conversation highlights how advanced imaging technologies are helping patients gain earlier insights into their health while empowering clinicians with new tools for preventive care.

Combining Human Expertise and AI for Earlier Detection

Dr. Raj, a breast radiologist by training, oversees both clinical operations and technology strategy at SimonMed, one of the largest outpatient imaging practices in the United States. With nearly 200 imaging centers nationwide, SimonMed has positioned itself as a leader in AI adoption and adaption across radiology.

“We’re probably the most AI-forward imaging practice in the country,” Dr. Raj said. “We have numerous deployments across all of clinical detection, and every aspect of our business is impacted by AI.”

For oncology and cancer prevention, the rationale is straightforward: timing matters. Earlier detection often translates into better outcomes, more treatment options, and reduced mortality. According to Dr. Raj, AI can strengthen radiologists’ ability to identify abnormalities while improving efficiency and consistency. “When we have our expert, fellowship-trained radiologists utilizing the AI that’s available today, we are helping our patients have even better care and faster results, and we’re able to do this at scale.”

Moving Healthcare from Reactive to Preventive

One of the central themes of the discussion was the need to move beyond symptom-driven medicine. “Healthcare has largely been reactive,” Dr. Raj explained. “The patient feels symptoms, the patient comes in, we do scans and labs, and sometimes we find something advanced, when treatment options are even more limited.”

SimonMed’s approach aims to identify risk factors and potential disease processes before symptoms develop. AI tools assist clinicians by analyzing imaging findings and synthesizing information from multiple sources, including electronic medical records, radiology reports, and patient histories.

Rather than delivering patients a traditional radiology report alone, SimonMed uses AI to create educational summaries that explain findings, risk factors, and recommended next steps in a highly accessible format. Dr. Raj described the experience as an “Instagram Reel–like delivery of the report,” designed to improve patient understanding and engagement.

A “One Size Fits One” Approach to Screening

Dr. Raj emphasized that prevention should not follow a uniform model. “Prevention isn’t one size fits all,” he said. “We have the technology, we have the ability, and we have the wherewithal and expertise to make preventative screening a one size fits one approach.”

For example, in breast imaging, AI can integrate information such as breast density, family history, and imaging findings to generate individualized risk assessments and screening recommendations. The technology also enables opportunistic screening—extracting additional health insights from studies that patients are already undergoing.

One example is the identification of breast arterial calcifications (BACs) on mammograms. While these findings are not directly related to breast cancer, they can provide valuable information about cardiovascular risk. “We know that BACs are a marker for disease risk or heart disease events,” Dr. Raj noted.

Patients with BACs may receive recommendations for additional evaluation, such as coronary artery calcium scoring or coronary CT angiography, depending on their risk profile. The goal is to use routine imaging encounters as opportunities to uncover broader health risks and guide preventive interventions.

Closing the Gap Between Patients and Providers

As patients increasingly seek health information online, clinicians face growing challenges in providing education while managing busy practices. “Patients are more active than ever at using ChatGPT or Google to try to find out more about their health,” Dr. Raj said. “And there’s this clash between energized patients and providers who are overwhelmed.”

SimonMed’s AI-powered educational platform, ClearPath, is designed to bridge that gap by delivering personalized explanations and actionable recommendations immediately after imaging results are finalized. Rather than leaving patients with unanswered questions, the system helps translate complex radiology findings into understandable guidance.

Addressing Incidental Findings Through Follow-Up Care

Whole-body MRI screening and advanced imaging programs often uncover incidental findings that may not require immediate intervention but still warrant monitoring. Dr. Raj stressed that SimonMed’s approach extends beyond simply identifying abnormalities. “We are not just a one-time shop where you come in, you get a scan, you get a result, and then our hands are washed,” he said.

Instead, SimonMed incorporates clinical teams that help patients understand findings and coordinate follow-up care. Patients receive clear explanations regarding what a finding means and what actions, if any, should follow.

“We don’t leave patients confused,” Dr. Raj said. “We not only give them their findings, but we tell them what it means and what the next action step is.” For many patients, the result is reassurance and ongoing monitoring when appropriate, helping distinguish benign findings from lesions that may require further evaluation.

Expanding AI Across Preventive Health

Looking ahead, SimonMed is rapidly broadening its AI portfolio beyond cancer detection. Recent initiatives include AI-powered tools for cardiovascular disease assessment, bone health evaluation, liver fat quantification, lung nodule characterization, and ultrasound lesion classification.

“Bottom line, we’re trying to attack every single part of the body and doing this at a pace and scale that no one else in the business is doing,” Dr. Raj said. The company is also integrating laboratory testing into its preventive health offerings, creating a more comprehensive assessment that combines imaging, biomarkers, and personalized education.

Conclusion

Radiology is undergoing a pivotal shift, with AI moving beyond a diagnostic aid to become the backbone of a comprehensive preventive health strategy. By integrating AI-driven detection with personalized patient education and opportunistic screening, SimonMed is working to overcome the traditional barriers of reactive medicine. As these tools continue to scale, they offer a blueprint for a more proactive healthcare system where earlier detection, clearer communication, and "one size fits one" personalization combine to improve long-term patient outcomes.

Key Takeaways

AI is helping radiologists detect abnormalities earlier and improve diagnostic accuracy.
SimonMed is using AI not only for image analysis but also for patient education and risk communication.
The organization advocates a “one size fits one” approach to preventive screening and personalized risk assessment.
Opportunistic screening, such as identifying breast arterial calcifications on mammograms, can uncover additional health risks beyond cancer.
AI-generated educational tools aim to bridge the gap between increasingly informed patients and overburdened healthcare providers.
SimonMed’s follow-up infrastructure helps patients navigate incidental findings and appropriate next steps.
The company is expanding AI applications across cardiovascular, metabolic, pulmonary, and musculoskeletal health, reflecting a broader vision for preventive medicine.

About the Speakers

Sean Raj, MD, MBA, is the Chief Medical Officer and Chief Innovation Officer at SimonMed. In this dual role, he leads evaluation, acquisition, integration, and implementation of new clinical technologies into SimonMed’s infrastructure and practice, with an emphasis on enhancing quality and elevating clinical outcomes. Dr. Raj also directs patient-centered research initiatives focused on incorporation of AI technologies into clinical practice. A board-certified radiologist, he was previously an Associate Professor of Radiology and partner in a hybrid academic-private practice in Texas, where he developed an award-winning high-risk breast cancer screening and research program.

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.

One Size Fits One: Personalizing Cancer Prevention Through AI-Enhanced Imaging