Mushtaq, Ali

Cardiac amyloidosis (CA) is a progressive condition resulting from the deposition of amyloid fibrils in the heart, which leads to severe diastolic dysfunction and restrictive cardiomyopathy. The disease has two main subtypes: light-chain and transthyretin (TTR) CA, with the latter subdivided into wild-type and hereditary forms. Despite advances in diagnostic imaging, early detection remains a challenge due to non-specific symptoms that mimic other cardiac conditions. Treatment has evolved significantly with targeted therapies like TTR stabilizers, gene silencers, and RNA interference, showing promise in altering disease progression. However, barriers such as high costs, limited availability of genetic testing, and inadequate multidisciplinary care continue to impede comprehensive management. Future strategies should focus on integrating novel gene-editing therapies, expanding access to diagnostics, and enhancing multidisciplinary care models to improve outcomes. Overall, early diagnosis, equitable access to therapies, and personalized management plans are crucial to advancing care for CA patients.

Purpose of review This review delves into the potential of artificial intelligence (AI), particularly machine learning (ML), in enhancing graft-versus-host disease (GVHD) risk assessment, diagnosis, and personalized treatment. Recent findings Recent studies have demonstrated the superiority of ML algorithms over traditional multivariate statistical models in donor selection for allogeneic hematopoietic stem cell transplantation. ML has recently enabled dynamic risk assessment by modeling time-series data, an upgrade from the static, “snapshot” assessment of patients that conventional statistical models and older ML algorithms offer. Regarding diagnosis, a deep learning model, a subset of ML, can accurately identify skin segments affected with chronic GVHD with satisfactory results. ML methods such as Q-learning and deep reinforcement learning have been utilized to develop adaptive treatment strategies (ATS) for the personalized prevention and treatment of acute and chronic GVHD. Summary To capitalize on these promising advancements, there is a need for large-scale, multicenter collaborations to develop generalizable ML models. Furthermore, addressing pertinent issues such as the implementation of stringent ethical guidelines is crucial before the widespread introduction of AI into GVHD care.