Welsh Ceilidh, Román García Susana, Barnett Gillian C, Jena Raj
Department of Oncology, University of Cambridge, Cambridge, UK.
Centre for Discovery Brain Sciences, College of Medicine & Veterinary Medicine, Biomedical Sciences, University of Edinburgh, UK.
Future Healthc J. 2024 Sep 19;11(3):100165. doi: 10.1016/j.fhj.2024.100165. eCollection 2024 Sep.
The rapid advancement and widespread adoption of artificial intelligence (AI) has ushered in a new era of possibilities in healthcare, ranging from clinical task automation to disease detection. AI algorithms have the potential to analyse medical data, enhance diagnostic accuracy, personalise treatment plans and predict patient outcomes among other possibilities. With a surge in AI's popularity, its developments are outpacing policy and regulatory frameworks, leading to concerns about ethical considerations and collaborative development. Healthcare faces its own ethical challenges, including biased datasets, under-representation and inequitable access to resources, all contributing to mistrust in medical systems. To address these issues in the context of AI healthcare solutions and prevent perpetuating existing inequities, it is crucial to involve communities and stakeholders in the AI lifecycle. This article discusses four community-driven approaches for co-developing ethical AI healthcare solutions, including understanding and prioritising needs, defining a shared language, promoting mutual learning and co-creation, and democratising AI. These approaches emphasise bottom-up decision-making to reflect and centre impacted communities' needs and values. These collaborative approaches provide actionable considerations for creating equitable AI solutions in healthcare, fostering a more just and effective healthcare system that serves patient and community needs.
人工智能（AI）的迅速发展和广泛应用开创了医疗保健领域的新时代，涵盖从临床任务自动化到疾病检测等诸多方面。人工智能算法有潜力分析医疗数据、提高诊断准确性、个性化治疗方案并预测患者预后等。随着人工智能的普及，其发展速度超过了政策和监管框架，引发了对伦理考量和协同发展的担忧。医疗保健领域面临着自身的伦理挑战，包括存在偏差的数据集、代表性不足以及资源获取不平等，所有这些都导致了对医疗系统的不信任。为了在人工智能医疗保健解决方案的背景下解决这些问题，并防止现有不平等现象持续存在，让社区和利益相关者参与人工智能生命周期至关重要。本文讨论了四种由社区驱动的共同开发符合伦理的人工智能医疗保健解决方案的方法，包括了解并优先考虑需求、定义共同语言、促进相互学习与共同创造以及使人工智能民主化。这些方法强调自下而上的决策，以反映并以受影响社区的需求和价值观为核心。这些协作方法为在医疗保健领域创建公平的人工智能解决方案提供了可操作的考量，有助于建立一个更公正、有效的医疗保健系统，满足患者和社区的需求。
