The adoption of Electronic Health Record (EHR) and other e-health infrastructures over the years has been characterized by an increase in medical errors. This is primarily a result of the widespread usage of medical acronyms and abbreviations with multiple possible senses (i.e., ambiguous). Artificial Intelligence (AI) technology, particularly Natural Language Processing (NLP), has emerged as a promising solution for addressing the challenge of automatic sense resolution in acronyms. Machine learning (ML) techniques have been utilized efficiently in the development of such systems, gaining increasing attention in recent years. Nevertheless, there is a scarcity of comprehensive studies that investigate the global adoption of Electronic Health Records (EHR), analyze the impact of acronyms usage on medical errors in EHR, and report on the latest trends and advancements in ML-based NLP solutions to disambiguate acronyms in medical practice. In the current study, we present a detailed overview on medical error, its origins, unintended effects, and EHR-related errors as a subclass of clinical error. This study also investigates the adoption of EHR systems in developed and developing nations, and the review concludes with an examination of various artificial intelligence techniques, particularly machine learning algorithms for medical acronym and abbreviation disambiguation in EHRs.