Bridging the Regulatory Divide: Ensuring Safety and Equity in Wearable Health Technologies

• URL: http://arxiv.org/abs/2508.20031v2
• Date: Thu, 04 Sep 2025 20:13:06 GMT
• Title: Bridging the Regulatory Divide: Ensuring Safety and Equity in Wearable Health Technologies
• Abstract summary: Many over-the-counter tools operate in a regulatory grey zone, leveraging health-related data and outputs without clinical validation.<n>The widespread repurposing of wellness devices for medical uses can introduce safety risks beyond the reach of current oversight.<n>We propose an approach emphasizing distributed risk, patient-centered outcomes, and iterative reform.
• Score: 3.647119869145335
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As wearable health technologies have grown more sophisticated, the distinction between "wellness" and "medical" devices has become increasingly blurred. While some features undergo formal U.S. Food and Drug Administration (FDA) review, many over-the-counter tools operate in a regulatory grey zone, leveraging health-related data and outputs without clinical validation. Further complicating the issue is the widespread repurposing of wellness devices for medical uses, which can introduce safety risks beyond the reach of current oversight. Drawing on legal analysis, case studies, and ethical considerations, we propose an approach emphasizing distributed risk, patient-centered outcomes, and iterative reform. Without a more pluralistic and evolving framework, the promise of wearable health technology risks being undermined by growing inequities, misuse, and eroded public trust.

Related papers

• Frontier AI Auditing: Toward Rigorous Third-Party Assessment of Safety and Security Practices at Leading AI Companies [57.521647436515785]
  We define frontier AI auditing as rigorous third-party verification of frontier AI developers' safety and security claims.<n>We introduce AI Assurance Levels (AAL-1 to AAL-4), ranging from time-bounded system audits to continuous, deception-resilient verification.
  arXiv  Detail & Related papers  (2026-01-16T18:44:09Z)
• Medical Malice: A Dataset for Context-Aware Safety in Healthcare LLMs [0.0]
  This work advocates for a shift from universal to context-aware safety.<n>It provides the necessary resources to immunize AI against the nuanced, systemic threats inherent to high-stakes medical environments.
  arXiv  Detail & Related papers  (2025-11-24T11:55:22Z)
• Trustworthy Medical Question Answering: An Evaluation-Centric Survey [40.76054471378203]
  We systematically examine six key dimensions of trustworthiness in medical question-answering systems.<n>We analyze evaluation-guided techniques that drive model improvements.<n>We propose future research directions to advance the safe, reliable, and transparent deployment of LLM-powered medical QA.
  arXiv  Detail & Related papers  (2025-06-04T07:48:10Z)
• Transforming Medical Regulations into Numbers: Vectorizing a Decade of Medical Device Regulatory Shifts in the USA, EU, and China [3.8657431480664717]
  Navigating the regulatory frameworks that ensure the safety and efficacy of medical devices can be challenging. These frameworks often require redundant testing, slowing down the process of getting innovations to patients.
  arXiv  Detail & Related papers  (2024-11-01T13:25:14Z)
• Beyond One-Time Validation: A Framework for Adaptive Validation of Prognostic and Diagnostic AI-based Medical Devices [55.319842359034546]
  Existing approaches often fall short in addressing the complexity of practically deploying these devices. The presented framework emphasizes the importance of repeating validation and fine-tuning during deployment. It is positioned within the current US and EU regulatory landscapes.
  arXiv  Detail & Related papers  (2024-09-07T11:13:52Z)
• Health-LLM: Personalized Retrieval-Augmented Disease Prediction System [43.91623010448573]
  We propose an innovative framework, Heath-LLM, which combines large-scale feature extraction and medical knowledge trade-off scoring.<n>Compared to traditional health management applications, our system has three main advantages.
  arXiv  Detail & Related papers  (2024-02-01T16:40:32Z)
• Analysis of Blockchain Integration in the e-Healthcare Ecosystem [0.0]
  This article studies the most commonly adopted approaches in healthcare data management systems using blockchain technology. An evaluation is conducted based on a set of observed common characteristics, distinguishing one approach from the others. For effective implementation in the context of e-health, we emphasize the existence of crucial challenges.
  arXiv  Detail & Related papers  (2024-01-08T12:19:53Z)
• Leveraging Generative AI for Clinical Evidence Summarization Needs to Ensure Trustworthiness [47.51360338851017]
  Evidence-based medicine promises to improve the quality of healthcare by empowering medical decisions and practices with the best available evidence. The rapid growth of medical evidence, which can be obtained from various sources, poses a challenge in collecting, appraising, and synthesizing the evidential information. Recent advancements in generative AI, exemplified by large language models, hold promise in facilitating the arduous task.
  arXiv  Detail & Related papers  (2023-11-19T03:29:45Z)
• Bias Impact Analysis of AI in Consumer Mobile Health Technologies: Legal, Technical, and Policy [1.6114012813668934]
  This work examines the intersection of algorithmic bias in consumer mobile health technologies (mHealth) We explore what extent current mechanisms - legal, technical, and or normative - help mitigate potential risks associated with unwanted bias. We provide additional guidance on the role and responsibilities technologists and policymakers have to ensure that such systems empower patients equitably.
  arXiv  Detail & Related papers  (2022-08-29T00:15:45Z)
• Fair Machine Learning in Healthcare: A Review [90.22219142430146]
  We analyze the intersection of fairness in machine learning and healthcare disparities. We provide a critical review of the associated fairness metrics from a machine learning standpoint. We propose several new research directions that hold promise for developing ethical and equitable ML applications in healthcare.
  arXiv  Detail & Related papers  (2022-06-29T04:32:10Z)
• Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Technical Challenges and Solutions [4.325945017291532]
  We survey the technical challenges involved in creating medical machine learning systems responsibly. We discuss the underlying technical challenges, possible ways for addressing them, and their respective merits and drawbacks.
  arXiv  Detail & Related papers  (2021-07-20T15:03:05Z)
• COVI White Paper [67.04578448931741]
  Contact tracing is an essential tool to change the course of the Covid-19 pandemic. We present an overview of the rationale, design, ethical considerations and privacy strategy of COVI,' a Covid-19 public peer-to-peer contact tracing and risk awareness mobile application developed in Canada.
  arXiv  Detail & Related papers  (2020-05-18T07:40:49Z)
• The Risk to Population Health Equity Posed by Automated Decision Systems: A Narrative Review [0.0]
  Automated decisions being made have significant consequences for individual and population health. Reports of issues arising from their use in health are already appearing. There is a significant risk that use of automated decision systems in health will exacerbate existing population health inequities.
  arXiv  Detail & Related papers  (2020-01-18T06:52:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.