Clinical bottom line
AI-enhanced imaging techniques show promise as adjunctive tools for the early detection of cardiac sarcoidosis, a challenging diagnosis with significant clinical implications. Integration of artificial intelligence algorithms with cardiac magnetic resonance (CMR) and positron emission tomography (PET) imaging may improve sensitivity and specificity by enabling more precise identification of subtle myocardial inflammation and fibrosis patterns. However, current evidence remains preliminary and largely hypothesis-generating, warranting further validation in prospective cohorts before routine clinical implementation. Clinicians should continue to rely on established multimodality imaging protocols combined with clinical assessment while monitoring emerging AI applications.
What the evidence shows
Cardiac sarcoidosis is a granulomatous inflammatory disease that can cause arrhythmias, heart failure, and sudden cardiac death. Early diagnosis is critical but difficult due to heterogeneous clinical presentations and limitations of conventional imaging (Morgan et al., 2014). CMR and PET are the cornerstone imaging modalities, with CMR detecting myocardial scarring and PET identifying active inflammation (Slivnick et al., 2022; Patel et al., 2017).
Recent advances in AI have enabled the development of algorithms that analyze complex imaging data to detect subtle phenotypic patterns indicative of early cardiac sarcoidosis. Priya et al. (2026) demonstrated that AI models integrating multi-modal imaging features with clinical data improved risk prediction and early diagnosis compared to standard interpretation alone. Similarly, Levin et al. (2021) highlighted that AI-enhanced CMR could identify early myocardial involvement before overt clinical manifestations.
Divakaran et al. (2025) emphasized the potential of AI to augment diagnostic accuracy by automating image segmentation and quantifying inflammation and fibrosis burden, which may be challenging for human readers. Miller et al. (2018) established evidence-based imaging criteria for cardiac sarcoidosis diagnosis, which could serve as a foundation for training AI models to recognize disease-specific patterns.
Radionuclide imaging, including PET, remains essential, and AI applications may optimize image reconstruction and interpretation, enhancing detection of active granulomatous inflammation (Sarcoidosis, 2012). However, the literature currently consists mainly of retrospective analyses and proof-of-concept studies with limited sample sizes.
Caveats and uncertainty
While AI-enhanced imaging holds promise, several limitations temper enthusiasm. Most studies to date are preliminary, with heterogeneous methodologies and lack of standardized AI model validation. The generalizability of AI algorithms across different scanners, imaging protocols, and patient populations is uncertain. Overfitting and bias remain concerns, especially given the rarity and clinical heterogeneity of cardiac sarcoidosis.
Moreover, the incremental diagnostic value of AI over expert human interpretation has not been definitively established in prospective, multicenter trials. The clinical impact on patient outcomes, including changes in management or prognosis, remains unproven.
Interpretation of AI outputs requires careful integration with clinical context and other diagnostic modalities. Regulatory and ethical considerations regarding AI transparency and accountability also warrant attention before widespread adoption.
How this may change practice
If validated, AI-enhanced imaging could become a valuable adjunct in the diagnostic workup of suspected cardiac sarcoidosis by improving early detection and risk stratification. This may facilitate timely initiation of immunosuppressive therapy and closer monitoring, potentially improving outcomes.
Radiologists and cardiologists should stay informed about emerging AI tools and participate in multidisciplinary discussions to integrate these technologies responsibly. Training programs may need to incorporate AI literacy to enable effective interpretation and oversight.
In the near term, AI applications are best viewed as complementary to established imaging protocols rather than replacements. Ongoing research and clinical trials will clarify their role and inform evidence-based guidelines.