NEW YORK – A new report from the UK suggests artificial intelligence-powered genomic health predictions (AIGHP) could exacerbate societal issues and lead to privacy and discrimination concerns, though experts spelled out a series of recommendations aimed at mitigating these risks and boosting the potential benefits of the tools..

纽约——英国的一份新报告表明，人工智能支持的基因组健康预测（AIGHP）可能会加剧社会问题，并导致隐私和歧视问题，尽管专家们提出了一系列旨在减轻这些风险并提高这些工具潜在效益的建议。。

The 'futures' research report looked at the possible effects that AI-powered genomic health predictions might have in the UK's National Health Service (NHS) health system, particularly when it comes to genomic predictions based on polygenic risk scores.

“未来”研究报告研究了人工智能支持的基因组健康预测可能对英国国家卫生服务（NHS）卫生系统产生的影响，特别是在基于多基因风险评分的基因组预测方面。

It was penned by Ada Lovelace Institute researcher Harry Farmer with contributions by Maili-Raven Adams and Andrew Strait, and released by the Ada Lovelace Institute and the Nuffield Council on Bioethics on Wednesday.

该报告由艾达·洛夫莱斯研究所研究员哈里·法默撰写，由梅利·瑞文·亚当斯和安德鲁·斯特里特贡献，并于周三由艾达·洛夫莱斯研究所和纳菲尔德生物伦理委员会发布。

'The project seeks to anticipate, assess, and navigate the potential impacts of the convergence of AI and genomics over the coming five to ten years,' Farmer explained.

Farmer解释说：“该项目旨在预测、评估和驾驭未来五到十年人工智能和基因组学融合的潜在影响。”。

Though he noted that AI-powered genomic health prediction 'is not yet the most common approach' for polygenic risk score analyses and conceded that 'polygenic scoring can be conducted without AI,' Farmer argued that AI-informed approaches are expected to ramp up significantly in the future.

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Along with a look at potential benefits of AI-powered genomic health predictions — including actionable insights into disease risk for individuals and populations, improved treatment targeting, more tailored healthcare utilization, and an increased focus on disease prevention approaches — the authors delved into possible risks of the technology such as privacy concerns, genetic discrimination, and the potential adoption of predictive approaches with yet-to-be-defined scientific certainty..

除了研究人工智能支持的基因组健康预测的潜在益处-包括对个人和人群疾病风险的可行见解，改进的治疗目标，更具针对性的医疗保健利用，以及对疾病预防方法的更多关注-作者深入研究了该技术可能存在的风险，例如隐私问题，遗传歧视以及尚未确定科学确定性的预测方法的潜在采用。。

'Large-scale deployment of AIGHP brings financial, ethical, and service-level risks,' the report explained, 'and the science underlying these techniques is still being developed.'

报告解释说，AIGHP的大规模部署带来了财务、道德和服务水平的风险，这些技术背后的科学仍在开发中

With these considerations in mind, the report highlighted 10 main recommendations for incorporating AI-based genomic health predictions into NHS-based healthcare in the future, which ranged from an emphasis on genomic data as a form of personal data to the need for related data protection laws for genomic data, biometric data, and corresponding research efforts..

考虑到这些因素，该报告强调了未来将基于人工智能的基因组健康预测纳入基于NHS的医疗保健的10项主要建议，其范围从强调基因组数据作为个人数据的一种形式到需要基因组数据，生物特征数据和相应研究工作的相关数据保护法律。。

Recommendations also addressed approaches for coming up with consistent, nuanced consent models, for example, while flagging organizations that may be tasked with performing public engagement efforts, insurance code development, and work on genomic discrimination legislation.

建议还涉及提出一致的，细微差别的同意模式的方法，例如，同时标记可能负责执行公共参与工作，保险代码制定和基因组歧视立法工作的组织。

The team also cautioned against the use of AI-powered genetic health predictions tools at the population level in the UK. Rather, the report suggested that it may be more beneficial to initially limit the approach to specific, targeted settings.

该团队还警告不要在英国人群水平上使用人工智能支持的遗传健康预测工具。相反，该报告建议，最初将这种方法限制在特定的目标环境中可能更有益。

'Our evidence suggests that while it has the potential to improve healthcare outcomes, [AI-powered genomic health prediction] may currently be an ineffective tool for mass disease prevention and reducing healthcare demand at a population level,' Farmer wrote, noting that 'wide deployment of AIGHP across the population could create greater exposure to the risks associated with the technology — and greater costs — in exchange for uncertain benefit.'.

Farmer写道：“我们的证据表明，虽然它有可能改善医疗保健结果，但人工智能支持的基因组健康预测]目前可能是大规模疾病预防和减少人口层面医疗保健需求的无效工具，并指出“在人群中广泛部署AIGHP可能会增加与技术相关的风险敞口，并增加成本，以换取不确定的收益。”。