Artificial Intelligence Challenges in the Healthcare Industry: A Systematic Review of Recent Evidence

IF 3.3 Q3 ENGINEERING, BIOMEDICAL

Healthcare Technology Letters Pub Date : 2025-09-02 DOI:10.1049/htl2.70017

Esmaeil Mehraeen, Haleh Siami, Sarah Montazeryan, Reza Molavi, Akram Feyzabadi, Iman Parvizy, Zeynab Ataei Masjedlu, Maryam Naseri Dehkalani, Sanam Mahmoudi, Alihasan Ahmadipour

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引用次数: 0

Abstract

While AI is essential to the development of electronic health, it has challenges that, if resolved, might improve the standard of healthcare services. The purpose of this study is to classify and identify these issues in the healthcare field. The study utilised a systematic review approach, drawing data from the Scopus, Web of Science, and PubMed databases. The search results were imported into EndNote software, and experienced experts reviewed the relevant articles. The selection criteria focused on original research articles in English, published between 2019 and July 2024, that provided full text and sufficient data on AI challenges. Forty-seven articles were included in the final analysis out of the 1453 that were identified. There were 17 categories for the obstacles, and the most common ones were technical challenges (29.8%), technological adoption (25.5%) and reliability and validity (23.4%). There are 24 categories into which the healthcare domains were divided. This article emphasises the critical importance of addressing technical challenges, enhancing reliability and validity, safeguarding patient data, and overcoming the lack of knowledge and understanding of artificial intelligence among patients and the general public to ensure the responsible and equitable implementation of AI in healthcare.

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医疗保健行业的人工智能挑战：最近证据的系统回顾

虽然人工智能对电子保健的发展至关重要，但它也面临挑战，如果解决这些挑战，可能会提高保健服务的标准。本研究的目的是对医疗保健领域的这些问题进行分类和识别。该研究采用了系统评价方法，从Scopus、Web of Science和PubMed数据库中提取数据。将检索结果导入EndNote软件，由经验丰富的专家对相关文章进行评审。评选标准侧重于2019年至2024年7月之间发表的英文原创研究文章，这些文章提供了有关人工智能挑战的全文和足够的数据。在鉴定的1453篇文章中，最终分析了47篇。障碍有17个类别，最常见的是技术挑战（29.8%）、技术采用（25.5%）和信度和效度（23.4%）。医疗保健领域分为24个类别。本文强调了解决技术挑战、提高可靠性和有效性、保护患者数据以及克服患者和公众对人工智能缺乏知识和理解的关键重要性，以确保在医疗保健中负责任和公平地实施人工智能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Healthcare Technology Letters Health Professions-Health Information Management

CiteScore

6.10

自引率

4.80%

发文量

审稿时长

22 weeks

期刊介绍： Healthcare Technology Letters aims to bring together an audience of biomedical and electrical engineers, physical and computer scientists, and mathematicians to enable the exchange of the latest ideas and advances through rapid online publication of original healthcare technology research. Major themes of the journal include (but are not limited to): Major technological/methodological areas: Biomedical signal processing Biomedical imaging and image processing Bioinstrumentation (sensors, wearable technologies, etc) Biomedical informatics Major application areas: Cardiovascular and respiratory systems engineering Neural engineering, neuromuscular systems Rehabilitation engineering Bio-robotics, surgical planning and biomechanics Therapeutic and diagnostic systems, devices and technologies Clinical engineering Healthcare information systems, telemedicine, mHealth.