FDA Widens Scope for AI Diagnostics

Nevisense's core technology, Electrical Impedance Spectroscopy (EIS), sends a harmless, low-frequency electrical signal into a skin lesion to analyze the varying electrical properties of tissue. This allows the device's AI to detect cellular and structural abnormalities beneath the surface that are invisible to the naked eye, providing an objective risk assessment for melanoma. The previous FDA indication, approved in 2017, restricted the use of Nevisense to dermatologists as an additional tool when considering a biopsy for lesions between 2mm and 20mm. The expanded labeling is significant because it opens the door for primary care physicians and other clinicians to use the technology, potentially speeding up the detection of melanoma at its earliest stages. This approval is part of a much larger trend. As of early 2026, the FDA has cleared over 1,300 AI/ML-enabled medical devices, with nearly 80% of them focused on medical imaging. Companies like GE HealthCare, Siemens, and Philips lead in the number of approvals, signaling a major industry shift toward AI-assisted diagnostics. The growth in AI diagnostics aligns with a major shift in care delivery toward outpatient and freestanding imaging centers. This "outpatient imaging boom" is driven by patient preference and significant reimbursement changes from payers, including Medicare, which are pushing procedures out of more expensive hospital settings. Health systems are responding by acquiring or partnering with independent centers to retain market share. However, this shift presents financial challenges for outpatient providers. Medicare's 2025 Physician Fee Schedule included reimbursement cuts for many common imaging procedures, continuing a trend of declining inflation-adjusted payments over the last decade. This pressure makes operational efficiency and the adoption of new, value-adding technologies critical for maintaining profitability. In response to market pressures, the radiology sector is seeing significant consolidation. From 2014 to 2023, the number of medical practices with radiologists decreased by nearly 15%, while the number of radiologists per practice nearly doubled. This trend toward larger, often multispecialty, practices is driven by the need for greater negotiating power with payers and the capital to invest in new technologies like AI. To guide the responsible adoption of these new tools, the American College of Radiology (ACR) has launched programs like ARCH-AI to establish best practices for using AI in clinical imaging. The ACR is also actively engaging with HHS and CMS on reimbursement policies for AI, arguing that payment models need to recognize the increased workload and value these tools bring to diagnostics to incentivize wider adoption. For leaders in the mobile imaging space, managing the profit and loss (P&L) implications of these trends is paramount. Strategic decisions about equipment acquisition, service line expansion, and partnerships must be data-driven, balancing the cost of new AI technologies against the efficiencies they create and the competitive advantages they offer in a rapidly consolidating market.