Gautam, NiteshGhanta, Sai NikhilaMueller, JoshuaMansour, MunthirChen, ZhongningPuente, ClaraHa, Yu MiTarun, TusharDhar, GauravSivakumar, KalaiZhang, YiyeHalimeh, Ahmed AbuNakarmi, UkashAl-Kindi, SadeerDeMazumder, DeeptankarAl’Aref, Subhi J.2023-10-042023-10-042022-11-26Gautam N, Ghanta SN, Mueller J, et al. Artificial Intelligence, Wearables and Remote Monitoring for Heart Failure: Current and Future Applications. Diagnostics (Basel). 2022;12(12):2964. Published 2022 Nov 26. doi:10.3390/diagnostics12122964https://hdl.handle.net/1805/36128Substantial milestones have been attained in the field of heart failure (HF) diagnostics and therapeutics in the past several years that have translated into decreased mortality but a paradoxical increase in HF-related hospitalizations. With increasing data digitalization and access, remote monitoring via wearables and implantables have the potential to transform ambulatory care workflow, with a particular focus on reducing HF hospitalizations. Additionally, artificial intelligence and machine learning (AI/ML) have been increasingly employed at multiple stages of healthcare due to their power in assimilating and integrating multidimensional multimodal data and the creation of accurate prediction models. With the ever-increasing troves of data, the implementation of AI/ML algorithms could help improve workflow and outcomes of HF patients, especially time series data collected via remote monitoring. In this review, we sought to describe the basics of AI/ML algorithms with a focus on time series forecasting and the current state of AI/ML within the context of wearable technology in HF, followed by a discussion of the present limitations, including data integration, privacy, and challenges specific to AI/ML application within healthcare.en-USAttribution 4.0 InternationalHeart failureMachine learningPressure sensorsRemote monitoringTime-series analysisArticle