HealthCare Business News sat down with Shyam Natarajan, CEO of Avenda Health, to talk about the evolution of prostate cancer AI, and how new breakthroughs are opening the doors to better treatment outcomes for patients

HCB News: What are some specific ways in which in-clinic AI imaging platforms can improve access to prostate cancer screening and treatment for patients?
Shyam Natarajan: In-clinic AI imaging platforms hold immense potential to democratize care and reduce healthcare disparities in prostate cancer screening and treatment. By providing highly accurate and consistent imaging analysis, AI ensures that patients receive the same level of diagnostic precision whether they are in a major metropolitan hospital or a rural clinic. AI systems can assist novice practitioners in underserved areas, empowering them with the same diagnostic capabilities as top specialists, thereby leveling the playing field. This democratization of expertise means that high-quality care is no longer confined to well-funded institutions, but is accessible to anyone, anywhere. By ensuring that diagnostic standards are uniformly applied, AI reduces the risk of misdiagnosis and ensures that all patients, regardless of socioeconomic status or geographic location, receive the best possible care. This transformative approach addresses healthcare disparities head-on, fostering a more accessible and equitable healthcare system.

HCB News: What are the benefits to both the patient and the provider of offering these kinds of solutions in-office?
SN: By providing this service right in the clinic, physicians can meet patients where they already are. There’s no further need to refer them out to a hospital outpatient or imaging center where they may have to travel farther to for their appointments. Providers benefit as well, now offering a service they previously had to refer out for, creating a new revenue stream.

HCB News: How do you envision the future of AI-powered imaging technologies in the field of cancer care?
SN: AI in oncologic care has evolved from predicting a patient’s risk factor to now being able to actually diagnose cancer. The industry has moved past simple automation and pattern finding and is actually improving processes and outcomes in comparison to non-AI assisted methods. In prostate cancer, the biggest challenge is visualizing the tumor location with its given structure. Other cancer specialties have a different problem - they can see the lesions just fine and the challenge comes in characterizing the lesion and matching that subtype with the right therapy. The hope is to create a foundational model of cancer care that can adapt and has a fundamental knowledge of tumor pathology regardless of the type or location of the cancer.

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