In January 2023, Lecanemab’s approval excited neurologists and radiologists, marking a historic breakthrough as the first successful Alzheimer’s treatment. With an effective pharmaceutical option now available, MRI-based brain diagnostics gain tremendous relevance for early neurodegenerative disease detection. This shift elevates the status of AI tools like automated brain volumetry from “nice-to-have” to absolutely essential. For other indications such as systematic oncological screening and early-stage detection of cancer, the potential value of AI tools is undeniable. However, there’s always the looming question: Is it economically viable in the mid- and long-term?
Reimbursement is imminent
The implementation of the new CPT Category III codes in the US for the AI tool of quantitative MRI brain analysis as of January 1, 2024, represents a significant milestone. This advancement provides a standardized framework for evaluating various neurological conditions, including dementia or multiple sclerosis, enhancing patient care across these diverse medical domains.
Another promising example is the progress in prostate cancer diagnostics. With the proven efficacy of mpMRIs, health insurances in Germany are now starting to cover for them. AI plays a crucial role in enabling efficient and large-scale screening, especially through automated longitudinal assessment monitoring methods like Active Surveillance.
ROI beyond reimbursement
Both examples indicate that healthcare decision-makers understand the importance of integrating AI throughout the entire patient journey for large-scale integrated care. While direct reimbursement aids the adoption of technology, it’s unrealistic to expect reimbursement codes for all relevant indications soon. Therefore, it’s crucial for organizations preparing for the future amidst increasing workloads and limited resources to integrate AI now. The goal must be increased efficiency and improved diagnostic outcomes, even without direct reimbursement.
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