Improving Rural Medication Safety with AI: A Scoping Review

• URL: http://arxiv.org/abs/2608.18135v1
• Date: Wed, 29 Jul 2026 03:24:18 GMT
• Title: Improving Rural Medication Safety with AI: A Scoping Review
• Abstract summary: Aim is to explore the applications and effectiveness of AI technologies in enhancing patient safety and reducing medication errors in rural health settings.<n>Scoping review was conducted through a systematic literature search spanning 2012 to 2025 across multiple databases.
• Score: 0.4433169165028139
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Introduction: Medication errors (MEs) represent a significant threat to global healthcare systems, contributing to patient harm. Introducing artificial intelligence (AI) in rural healthcare enhances patient safety. The aim is to explore the applications and effectiveness of AI technologies in enhancing patient safety and reducing medication errors in rural health settings. Methods: A scoping review was conducted through a systematic literature search spanning 2012 to 2025 across multiple databases, including EBSCohost, Emcare (Ovid), MEDLINE, and the ProQuest Consumer Health Database. Twelve primary studies from nine different nations were examined. Data were analysed thematically to obtain insights on AI interventions across the medication process. Results: AI technologies have been integrated into every stage of medication management, right from prescribing and dispensing to administration and post-administration monitoring. Four key themes came to light: (1) the various types of AI being utilised (like Clinical Decision Support Systems, Machine Learning, Natural Language Processing, and smart pumps); (2) the phases of the medication process that are affected; (3) how effective these technologies are in minimising errors and boosting workflow safety; and (4) rural-specific challenges including infrastructure, staff training, system integration, and alert fatigue. Several studies have demonstrated that machine learning-based surveillance improves incident detection and reduces prescribing and transcription errors by an impressive 34% to 80%. Barriers included lack of governance frameworks, financial limitations, and clinician resistance, which still present major obstacles. Conclusion: In rural healthcare, AI technologies hold great potential for enhancing pharmaceutical safety. They can allow data-driven monitoring, automate processes, and offer clinical decision assistance.

Related papers

• A Scoping Review of AI-Driven Digital Interventions in Mental Health Care: Mapping Applications Across Screening, Support, Monitoring, Prevention, and Clinical Education [3.9321601638434465]
  Generative AI (GenAI) and Human-Centered AI (HCAI) are increasingly used to expand access to digital psychiatry and mental health care.<n>This review maps the landscape of AI-driven mental health technologies across five critical phases: pre-treatment (screening/triage), treatment (therapeutic support), post-treatment (remote patient monitoring), clinical education, and population-level prevention.
  arXiv  Detail & Related papers  (2026-03-17T07:33:29Z)
• Industrialized Deception: The Collateral Effects of LLM-Generated Misinformation on Digital Ecosystems [47.03825808787752]
  This paper transitions from literature review to practical countermeasures.<n>We report on improved AI-generated content through Large Language Models (LLMs) and multimodal systems.<n>We discuss mitigation strategies including LLM-based detection, inoculation approaches, and the dual-use nature of generative AI.
  arXiv  Detail & Related papers  (2026-01-29T16:42:22Z)
• Artificial Intelligence in Rural Healthcare Delivery: Bridging Gaps and Enhancing Equity through Innovation [3.192479877329154]
  Rural healthcare faces persistent challenges, including inadequate infrastructure, workforce shortages, and socioeconomic disparities.<n>We systematically reviewed 109 studies published between 2019 and 2024 from PubMed, Embase, Web of Science, IEEE Xplore, and Scopus.<n>The findings reveal significant promise for AI applications, such as predictive analytics, telemedicine platforms, and automated diagnostic tools.
  arXiv  Detail & Related papers  (2025-08-15T17:08:10Z)
• Medical Reasoning in the Era of LLMs: A Systematic Review of Enhancement Techniques and Applications [59.721265428780946]
  Large Language Models (LLMs) in medicine have enabled impressive capabilities, yet a critical gap remains in their ability to perform systematic, transparent, and verifiable reasoning.<n>This paper provides the first systematic review of this emerging field.<n>We propose a taxonomy of reasoning enhancement techniques, categorized into training-time strategies and test-time mechanisms.
  arXiv  Detail & Related papers  (2025-08-01T14:41:31Z)
• From large language models to multimodal AI: A scoping review on the potential of generative AI in medicine [40.23383597339471]
  multimodal AI is capable of integrating diverse data modalities, including imaging, text, and structured data, within a single model.<n>This scoping review explores the evolution of multimodal AI, highlighting its methods, applications, datasets, and evaluation in clinical settings.<n>Our findings underscore a shift from unimodal to multimodal approaches, driving innovations in diagnostic support, medical report generation, drug discovery, and conversational AI.
  arXiv  Detail & Related papers  (2025-02-13T11:57:51Z)
• Towards Next-Generation Medical Agent: How o1 is Reshaping Decision-Making in Medical Scenarios [46.729092855387165]
  We study the choice of the backbone LLM for medical AI agents, which is the foundation for the agent's overall reasoning and action generation.<n>Our findings demonstrate o1's ability to enhance diagnostic accuracy and consistency, paving the way for smarter, more responsive AI tools.
  arXiv  Detail & Related papers  (2024-11-16T18:19:53Z)
• Explainable Artificial Intelligence for Medical Applications: A Review [42.33274794442013]
  This article reviews recent research grounded in explainable artificial intelligence (XAI)<n>It focuses on medical practices within the visual, audio, and multimodal perspectives.<n>We endeavour to categorise and synthesise these practices, aiming to provide support and guidance for future researchers and healthcare professionals.
  arXiv  Detail & Related papers  (2024-11-15T11:31:06Z)
• SoK: Security and Privacy Risks of Healthcare AI [15.655956766190256]
  The integration of artificial intelligence (AI) and machine learning (ML) into healthcare systems holds great promise for enhancing patient care.<n>However, it also exposes sensitive data and system integrity to potential cyberattacks.<n>Current security and privacy (S&P) research on healthcare AI is highly unbalanced in terms of healthcare deployment scenarios and threat models.
  arXiv  Detail & Related papers  (2024-09-11T16:59:58Z)
• Safety challenges of AI in medicine in the era of large language models [23.817939398729955]
  Large language models (LLMs) offer new opportunities for medical practitioners, patients, and researchers.<n>As AI and LLMs become more powerful and especially achieve superhuman performance in some medical tasks, public concerns over their safety have intensified.<n>This review examines emerging risks in AI utilization during the LLM era.
  arXiv  Detail & Related papers  (2024-09-11T13:47:47Z)
• AI-Driven Healthcare: A Review on Ensuring Fairness and Mitigating Bias [4.9325218275489515]
  This review paper examines the integration of AI in healthcare, highlighting critical challenges related to bias and exploring strategies for mitigation.<n>We emphasize the necessity of diverse datasets, fairness-aware algorithms, and regulatory frameworks to ensure equitable healthcare delivery.<n>The paper concludes with recommendations for future research, advocating for interdisciplinary approaches, transparency in AI decision-making, and the development of innovative and inclusive AI applications.
  arXiv  Detail & Related papers  (2024-07-29T02:39:17Z)
• The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies [97.5153823429076]
  The benefits, challenges and drawbacks of AI in this field are reviewed. The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods are also discussed.
  arXiv  Detail & Related papers  (2022-12-08T23:23:39Z)
• The Role of Robotics in Infectious Disease Crises [46.43737882437637]
  The recent coronavirus pandemic has highlighted the challenges faced by the healthcare, public safety, and economic systems when confronted with a surge in patients. There is a complementary need to anticipate and address the engineering challenges associated with infectious disease emergencies. As technical capabilities advance and as the installed base of robotic systems increases in the future, they could play a much more significant role in future crises.
  arXiv  Detail & Related papers  (2020-10-19T22:54:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.