人工智能在药物警戒领域的应用--机遇与挑战

Q2 Medicine
Mira Kirankumar Desai
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引用次数: 0

摘要

药物警戒(PV)是一个以数据为驱动的过程,通过处理可疑不良事件(AE)报告和提取健康数据,尽早发现药品安全问题。药物警戒案例处理周期始于数据收集、数据录入、初步检查完整性和有效性、编码、因果关系、预期性、严重性和严重程度的医学评估、随后提交报告、质量检查以及数据存储和维护。这需要大量人力和专业技术知识,因此既昂贵又耗时。由于个人病例安全报告的智能收集和报告,PV 数据库中疑似 AE 报告的数量呈指数级增长,医疗保健专业人员和患者的意识和参与程度提高,从而扩大了基础。对于制药公司、监管机构、医疗和 PV 专家以及国家药物警戒项目管理人员等主要利益相关者来说,如何处理数量庞大、种类繁多的数据、合理利用这些数据并将其从 "大海捞针 "中分离出来是一项挑战。医疗保健领域的人工智能(AI)在严重依赖医学影像解读的专业领域表现抢眼。同样,人们对采用人工智能工具来补充和自动化 PV 流程的兴趣也在不断增长。先进的技术当然可以补充病例处理过程中的常规、重复和人工任务,并提高效率;然而,在整个病例处理生命周期中实施这种技术并产生实际影响,却提出了一些问题和挑战。光伏系统的完全自动化是一把双刃剑,需要考虑两个方面--人员和流程。重点应该是将专业技术(人员)与智能技术(流程)相结合的协作方法,以增强人的才能,从而实现光伏系统的目标,并使所有利益相关者受益。人工智能技术应增强人类智慧,而不是取代人类专家。重要的是要强调并确保人工智能能为光伏行业带来更多益处,而不是挑战。本综述介绍了印度医疗保健系统的优势和突出的科学、技术和政策问题,以及人工智能工具在实现完全自动化方面的成熟度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial intelligence in pharmacovigilance – Opportunities and challenges
Pharmacovigilance (PV) is a data-driven process to identify medicine safety issues at the earliest by processing suspected adverse event (AE) reports and extraction of health data. The PV case processing cycle starts with data collection, data entry, initial checking completeness and validity, coding, medical assessment for causality, expectedness, severity, and seriousness, subsequently submitting report, quality checking followed by data storage and maintenance. This requires a workforce and technical expertise and therefore, is expensive and time-consuming. There has been exponential growth in the number of suspected AE reports in the PV database due to smart collection and reporting of individual case safety reports, widening the base by increased awareness and participation by health-care professionals and patients. Processing of the enormous volume and variety of data, making its sensible use and separating “needles from haystack,” is a challenge for key stakeholders such as pharmaceutical firms, regulatory authorities, medical and PV experts, and National Pharmacovigilance Program managers. Artificial intelligence (AI) in health care has been very impressive in specialties that rely heavily on the interpretation of medical images. Similarly, there has been a growing interest to adopt AI tools to complement and automate the PV process. The advanced technology can certainly complement the routine, repetitive, manual task of case processing, and boost efficiency; however, its implementation across the PV lifecycle and practical impact raises several questions and challenges. Full automation of PV system is a double-edged sword and needs to consider two aspects – people and processes. The focus should be a collaborative approach of technical expertise (people) combined with intelligent technology (processes) to augment human talent that meets the objective of the PV system and benefit all stakeholders. AI technology should enhance human intelligence rather than substitute human experts. What is important is to emphasize and ensure that AI brings more benefits to PV rather than challenges. This review describes the benefits and the outstanding scientific, technological, and policy issues, and the maturity of AI tools for full automation in the context to the Indian health-care system.
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来源期刊
Perspectives in Clinical Research
Perspectives in Clinical Research Medicine-Medicine (all)
CiteScore
2.90
自引率
0.00%
发文量
41
审稿时长
36 weeks
期刊介绍: This peer review quarterly journal is positioned to build a learning clinical research community in India. This scientific journal will have a broad coverage of topics across clinical research disciplines including clinical research methodology, research ethics, clinical data management, training, data management, biostatistics, regulatory and will include original articles, reviews, news and views, perspectives, and other interesting sections. PICR will offer all clinical research stakeholders in India – academicians, ethics committees, regulators, and industry professionals -a forum for exchange of ideas, information and opinions.
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