Stakeholder Guidance to Anticipate and Address Ethical Challenges in Applications of Machine Learning and Artificial Intelligence in Algorithmic Medicine: a Novel Empirical Approach
利益相关者指导预测和解决机器学习和人工智能在算法医学中的应用中的伦理挑战:一种新颖的经验方法
基本信息
- 批准号:10267034
- 负责人:
- 金额:$ 44.29万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2020
- 资助国家:美国
- 起止时间:2020-09-20 至 2024-07-31
- 项目状态:已结题
- 来源:
- 关键词:AddressAdoptionAffectAgreementAlgorithm DesignAlgorithmsArtificial IntelligenceAttentionAttitudeAwarenessClinicalClinical InvestigatorComplexDataDecision MakingDevelopmentDiagnosisDimensionsDisclosureDisease ManagementEffectivenessEmpirical ResearchEmployeeEnsureEthical IssuesEthicistsEthicsEvaluationExpert SystemsExplosionFaceFailureFamiliarityFundingFutureGoalsHealthHealthcare SystemsHumanHuman ResourcesIndividualInterviewInvestigationJudgmentJusticeKnowledgeLearningMachine LearningMedicalMedicineMethodologyPatient CarePatient-Focused OutcomesPatientsPerceptionPersonsPhysiciansPlayRandomizedResearchResearch PersonnelRoleScienceShapesSocietiesStructureSurveysSystemTimeTrainingTranslationsTrustUnited States National Institutes of HealthUniversitiesWorkclinical decision-makingclinical riskcostcourtdeep learning algorithmevidence baseexperienceimprovedinnovationinsightmeetingsmultidisciplinarynovelpatient populationpopulation healthprecision medicinepreventrecruitresponsestakeholder perspectivestool
项目摘要
PROJECT ABSTRACT
The potential for artificial intelligence applications, specifically machine learning, to prevent, predict, and help
manage disease sparks immense hope not only for the individuals affected, but also for the overall health of
populations. Particularly exciting examples of these novel computing strategies are increasingly found in the
development of deep learning algorithms for medical use. Already embedded in our daily lives, algorithms have
begun to impact human-decision making, from recruitment and hiring of employees to criminal sentencing.
Outside of medicine, recognition of the ways algorithms may reflect, reproduce, and perpetuate bias has led to
an explosion of theoretical and empirical research on the subject. There is an increasing awareness of
potential algorithmic weaknesses, including some that raise concerns about fundamental issues of fairness,
justice, and bias. The need to anticipate and address emerging ethical issues in algorithmic medicine is time-
sensitive. As health care systems increasingly utilize algorithms for patient identification, diagnosis, and
treatment direction, the consequences of algorithmic bias yield real and significant costs. Numerous
stakeholders are responsible for the development, application and interpretation of algorithms in medicine, and
yet there has been very little engagement of stakeholders most affected by these learning systems and tools.
The overarching goal of this empirical and hypothesis driven project is to articulate the landscape of ethical
concerns and the issues emerging in the context of the development, refinement, and application of machine
learning in algorithmic medicine. First, we determine the distinct ethical issues and problems encountered in
the development, refinement, and application of machine learning, by querying the perspectives of a diverse
array of stakeholders involved—machine learning researchers, clinicians, ethicists, and patients. Using the
new insights generated from the first half, we will conduct an evidence-based, information-sharing vignette
survey to understand the impact of the contexts of algorithms on the ethically salient perspectives of
physicians—those poised to implement such innovation in their own decision-making for the care of patients.
Maximizing our established record of expertise in empirical ethics investigations, this sequence of projects
leverages access to the exceptional machine learning research conducted at Stanford University, including
work by NIH-funded investigators, and provides extensive, systematically collected data on ethical issues
encountered and anticipated throughout the development and implementation of algorithms. Finally, the
project develops and refines an evidence-informed information-sharing survey for use in better understanding
how physicians react to intelligent systems.
项目摘要
人工智能应用的潜力,特别是机器学习,以预防,预测和帮助
控制疾病不仅为受影响的个人,而且为整体健康带来了巨大的希望。
人口。这些新颖的计算策略的特别令人兴奋的例子越来越多地出现在
开发用于医疗用途的深度学习算法。算法已经嵌入我们的日常生活,
开始影响人类的决策,从招聘和雇用员工到刑事判决。
在医学之外,对算法可能反映、复制和延续偏见的方式的认识导致了
关于这个问题的理论和实证研究的激增。人们越来越意识到
潜在的算法弱点,包括一些引起人们对公平性基本问题的担忧,
公正和偏见预测和解决算法医学中新出现的伦理问题的需要是时间-
敏感随着医疗保健系统越来越多地利用算法来进行患者识别、诊断和诊断,
治疗方向,算法偏差的后果产生真实的和显著的成本。许多
利益相关者负责医学算法的开发、应用和解释,
然而,受这些学习系统和工具影响最大的利益攸关方很少参与。
这个经验和假设驱动的项目的总体目标是阐明道德的景观
在机器的开发、改进和应用方面出现的问题和问题
学习算法医学。首先,我们确定了不同的伦理问题和遇到的问题,
机器学习的发展,完善和应用,通过查询不同的观点,
一系列利益相关者参与-机器学习研究人员,临床医生,伦理学家和患者。使用
从上半年产生的新见解,我们将进行一个以证据为基础的信息共享小插曲
调查,以了解算法的背景对道德上突出的观点,
医生-那些准备在他们自己的决策中实施这样的创新来照顾病人的人。
最大限度地发挥我们在经验道德调查方面的专业知识,这一系列项目
利用斯坦福大学进行的卓越机器学习研究,包括
由NIH资助的研究人员开展工作,并提供广泛的、系统收集的有关伦理问题的数据
在整个算法的开发和实现过程中遇到和预期的。最后
项目制定并完善了一项循证信息共享调查,用于更好地了解
医生对智能系统的反应
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Jane Paik Kim其他文献
Effects of a Digital Therapeutic Adjunct to Eating Disorder Treatment on Health Care Service Utilization and Clinical Outcomes: Retrospective Observational Study Using Electronic Health Records
数字治疗辅助进食障碍治疗对医疗保健服务利用和临床结果的影响:使用电子健康记录的回顾性观察研究
- DOI:
10.2196/59145 - 发表时间:
2024-01-01 - 期刊:
- 影响因子:5.800
- 作者:
Jorge E Palacios;Kathryn K Erickson-Ridout;Jane Paik Kim;Stuart Buttlaire;Samuel Ridout;Stuart Argue;Jenna Tregarthen - 通讯作者:
Jenna Tregarthen
Users' Perceptions and Trust in AI in Direct-to-Consumer mHealth: Qualitative Interview Study
直接面向消费者的移动医疗中用户对人工智能的认知与信任:定性访谈研究
- DOI:
10.2196/64715 - 发表时间:
2025-01-01 - 期刊:
- 影响因子:6.200
- 作者:
Katie Ryan;Justin Hogg;Max Kasun;Jane Paik Kim - 通讯作者:
Jane Paik Kim
Jane Paik Kim的其他文献
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{{ truncateString('Jane Paik Kim', 18)}}的其他基金
Stakeholder Guidance to Anticipate and Address Ethical Challenges in Applications of Machine Learning and Artificial Intelligence in Algorithmic Medicine: a Novel Empirical Approach
利益相关者指导预测和解决机器学习和人工智能在算法医学中的应用中的伦理挑战:一种新颖的经验方法
- 批准号:
10367404 - 财政年份:2021
- 资助金额:
$ 44.29万 - 项目类别:
Stakeholder Guidance to Anticipate and Address Ethical Challenges in Applications of Machine Learning and Artificial Intelligence in Algorithmic Medicine: a Novel Empirical Approach
利益相关者指导预测和解决机器学习和人工智能在算法医学中的应用中的伦理挑战:一种新颖的经验方法
- 批准号:
10674548 - 财政年份:2020
- 资助金额:
$ 44.29万 - 项目类别:
Stakeholder Guidance to Anticipate and Address Ethical Challenges in Applications of Machine Learning and Artificial Intelligence in Algorithmic Medicine: a Novel Empirical Approach
利益相关者指导预测和解决机器学习和人工智能在算法医学中的应用中的伦理挑战:一种新颖的经验方法
- 批准号:
10099785 - 财政年份:2020
- 资助金额:
$ 44.29万 - 项目类别:
Stakeholder Guidance to Anticipate and Address Ethical Challenges in Applications of Machine Learning and Artificial Intelligence in Algorithmic Medicine: a Novel Empirical Approach
利益相关者指导预测和解决机器学习和人工智能在算法医学中的应用中的伦理挑战:一种新颖的经验方法
- 批准号:
10455006 - 财政年份:2020
- 资助金额:
$ 44.29万 - 项目类别:
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