Harnessing artificial intelligence for enhanced veterinary diagnostics: A look to quality assurance, Part I Model development.

IF 1.2 4区 农林科学 Q3 VETERINARY SCIENCES
Christina Pacholec, Bente Flatland, Hehuang Xie, Kurt Zimmerman
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引用次数: 0

Abstract

Artificial intelligence (AI) has transformative potential in veterinary pathology in tasks ranging from cell enumeration and cancer detection to prognosis forecasting, virtual staining techniques, and individually tailored treatment plans. Preclinical testing and validation of AI systems (AIS) are critical to ensure diagnostic safety, efficacy, and dependability. In this two-part series, challenges such as the AI chasm (ie, the discrepancy between the AIS model performance in research settings and real-world applications) and ethical considerations (data privacy, algorithmic bias) are reviewed and underscore the importance of tailored quality assurance measures that address the nuances of AI in veterinary pathology. This review advocates for a multidisciplinary approach to AI development and implementation, focusing on image-based tasks, highlighting the necessity for collaboration across veterinarians, computer scientists, and ethicists to successfully navigate the complex landscape of using AI in veterinary medicine. It calls for a concerted effort to bridge the AI chasm by addressing technical, ethical, and regulatory challenges, facilitating AI integration into veterinary pathology. The future of veterinary pathology must balance harnessing AI's potential while intentionally mitigating its risks, ensuring the welfare of animals and the integrity of the veterinary profession are safeguarded. Part I of this review focuses on considerations for model development, and Part II focuses on external validation of AI.

利用人工智能增强兽医诊断:质量保证的展望,第一部分模型开发。
人工智能(AI)在兽医病理学领域具有变革潜力,从细胞计数和癌症检测到预后预测、虚拟染色技术和个性化治疗计划。人工智能系统(AIS)的临床前测试和验证对于确保诊断的安全性、有效性和可靠性至关重要。在这个由两部分组成的系列文章中,对人工智能鸿沟(即研究环境中人工智能模型性能与现实世界应用之间的差异)和伦理考虑(数据隐私、算法偏差)等挑战进行了回顾,并强调了定制质量保证措施的重要性,这些措施可以解决兽医病理学中人工智能的细微差别。这篇综述提倡采用多学科方法来开发和实施人工智能,重点关注基于图像的任务,强调兽医、计算机科学家和伦理学家之间合作的必要性,以成功驾驭在兽医学中使用人工智能的复杂局面。它呼吁通过解决技术、伦理和监管方面的挑战,共同努力弥合人工智能鸿沟,促进人工智能融入兽医病理学。兽医病理学的未来必须平衡利用人工智能的潜力,同时有意降低其风险,确保动物福利和兽医职业的完整性得到保障。本综述的第一部分侧重于模型开发的考虑,第二部分侧重于人工智能的外部验证。
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
Veterinary clinical pathology
Veterinary clinical pathology 农林科学-兽医学
CiteScore
1.70
自引率
16.70%
发文量
133
审稿时长
18-36 weeks
期刊介绍: Veterinary Clinical Pathology is the official journal of the American Society for Veterinary Clinical Pathology (ASVCP) and the European Society of Veterinary Clinical Pathology (ESVCP). The journal''s mission is to provide an international forum for communication and discussion of scientific investigations and new developments that advance the art and science of laboratory diagnosis in animals. Veterinary Clinical Pathology welcomes original experimental research and clinical contributions involving domestic, laboratory, avian, and wildlife species in the areas of hematology, hemostasis, immunopathology, clinical chemistry, cytopathology, surgical pathology, toxicology, endocrinology, laboratory and analytical techniques, instrumentation, quality assurance, and clinical pathology education.
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