Every time I explore a new healthcare sector, most recently at the RSNA radiology conference in Chicago, I'm struck by how fundamentally similar these specialized fields are, despite vastly different technologies, economics, and diagnoses.
Here's what radiology and behavioral health share in common:
Early Detection as the Future of Care
Both fields have enormous potential to find patients earlier in their care journey. Today's healthcare system is often criticized as "sickcare"—we treat illness reactively rather than preventing it. Full-body scans, AI-powered anomaly detection, and companies like Imeka, applying AI to brain imaging, are changing this paradigm. Just as behavioral health screening can identify mental health conditions before they become crises, advanced radiology can detect physical abnormalities long before symptoms appear.
Both fields face significant access challenges, albeit in different forms. Behavioral health struggles with psychiatrist shortages and, depending on geography and specialty, therapist shortages too. Radiology faces radiologist shortages, but also a hardware constraint: the machines themselves limit how many patients can be scanned.
Healthcare as a Team Sport
These access constraints are driving innovation in care delivery models. In behavioral health, I saw emerging models like Integrated Behavioral Health and Collaborative Care at NeuroFlow, where psychiatrists practice at the top of their license by partnering with social workers and PCPs. The psychiatrist's role shifts from 1-to-1 to 1-to-many, enabling broader impact. We saw these models work exceptionally well at the VA.
Radiology is following a similar path. Companies such as iMorgon are enabling technicians to handle more routine tasks, while AI acts as a digital assistant for radiologists. As explained to me by the team at ShadowfaxAI, there's no reason for a radiologist to spend time documenting negative findings, what they don't see.
Just as social workers can't prescribe antidepressants, techs can't provide final impressions on scans. But automating intermediate steps frees expensive physician time for complex cases.
The Patient Follow-Through Problem
Even with adequate capacity, patient behavior creates challenges. No-shows and last-minute cancellations create gaps in utilization. Companies like Alpha Nodus automate scheduling and last-minute booking to maximize patient flow in radiology. Similar tools exist for behavioral health engagement. At NeuroFlow, we drove patient engagement between appointments because the best way to improve behavioral health outcomes is keeping patients in treatment. Adherence is always a struggle, regardless of specialty.
Specialized Technology for Specialized Care
Finally, both fields operate outside the core competency of dominant EHRs like Epic. Radiology workflows revolve around images requiring massive data management from the "modality," the scanning hardware itself. This has spawned specialized technologies: DICOM/PACS systems for image management such as Intelerad (owned by GE), interoperability solutions like Dicom Systems, Interlinx, and DataFirst to shuttle data between systems, and AI tools that add value throughout the workflow.
Understanding these parallels matters. It shows both industries are moving in the right direction. It creates analogies for solutions that should work across fields. And it gets me excited about the future of radiology and healthcare more broadly.











