利用以艺术为基础的方法,让生活在多种长期疾病的塔楼村庄的人们参与到医疗保健研究中人工智能工具的开发中
Elizabeth Remfry1, Duncan J Reynolds2, Sylvia Morgado de Queiroz3
1William Harvey Research Institute, Queen Mary University of London, London, UK.
概括
基于艺术的方法成功地使代表性不足的群体参与了医疗保健人工智能 (AI) 研究. 这些包容性的方法促进了沟通,并建立了人工智能素养,导致了新的研究重点,并为人工智能开发提供了信息.
科学领域:
- 医疗保健研究的研究.
- 人工智能 (AI) 是一种人工智能.
- 公众和患者参与和参与 (PPIE)
背景情况:
- 医疗保健人工智能研究中的传统公众参与方法往往排除少数群体.
- 越来越需要包容性的方法来让不同的人群参与人工智能开发.
研究的目的:
- 探索使用基于艺术的方法,让代表性不足的社区参与医疗保健人工智能研究.
- 收集来自有多种长期疾病 (MLTCs) 和多药学生活经验的个人的观点.
主要方法:
- 与伦敦东部的一个社区组织的合作涉及来自代表性不足的背景的22名PPIE贡献者.
- 六个研讨会利用视觉艺术 (陶,拼贴,身体映射,人工智能生成的图像) 来促进对人工智能开发的投入.
- 贡献者有过MLTC的实践经验,代表了不同年龄,性别,社会人口统计和多语言能力.
主要成果:
- 基于艺术的方法促进了沟通,使PPIE贡献者能够确定研究重点,例如任命频率对MLTC的影响.
- 贡献者强调了人工智能模型开发的关键考虑因素,包括使用当地住房数据和解决偏见.
- 视觉艺术使抽象的人工智能概念变得更有形,提高了人工智能素养和参与度.
结论:
- 基于艺术的方法是有效的,使研究参与在医疗保健AI中代表性不足的社区更容易获得.
- 包容性参与式方法至关重要,因为医疗保健研究中的人工智能继续扩大.
- 该研究成功地产生了研究重点,并通过新的参与策略告知了AI开发.
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