Opportunities and Risks of Generative AI through the Health Information Journey
- URL: http://arxiv.org/abs/2605.23026v1
- Date: Thu, 21 May 2026 20:49:21 GMT
- Title: Opportunities and Risks of Generative AI through the Health Information Journey
- Abstract summary: AI systems generate claims, curate information, interpret symptoms, synthesize evidence, and guide decisions.<n>Potential benefits include improvements in access, comprehension, and continuity of care.<n>At the same time, AI can introduce inaccurate or manipulative content that is difficult to distinguish from reliable guidance.
- Score: 3.282845873351502
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Artificial intelligence is fundamentally changing how health content is encountered and acted upon across both the information and healthcare ecosystems. AI systems now generate claims, curate information, interpret symptoms, synthesize evidence, and guide decisions, with significant opportunities and risks for the public. Potential benefits include improvements in access, comprehension, and continuity of care. At the same time, AI can introduce inaccurate or manipulative content that is difficult to distinguish from reliable guidance, and encourage automated decisions that affect care with little transparency or recourse. We introduce a four-stage framework to examine how these opportunities and risks unfold as the public moves through the information environment and into formal healthcare.
Related papers
- Public perceptions of AI-driven decision-making in healthcare: A structural equation modeling approach [0.0]
Successful implementation of AI depends not only on technical performance but also on public perceptions of its helpfulness, riskiness, and fairness.<n>This study examines public perceptions of automated decision-making (ADM) in healthcare.
arXiv Detail & Related papers (2026-07-21T09:14:00Z) - "Where is this coming from?" Uncovering Trustworthiness Ideals in AI-powered Peripartum Information Seeking [10.118301488103064]
Current AI and NLP-powered systems aim to improve access to vetted maternal health information.<n>In high-stakes health contexts shaped by historical inequities, trustworthiness must be inspectable and not asserted.<n>We propose design artifacts that are mistrust-aware and promote principled governance mechanisms for transparent, pluralistic AI systems.
arXiv Detail & Related papers (2026-06-08T20:36:57Z) - Information Retrieval in the Age of Generative AI: The RGB Model [77.96475639967431]
This paper presents a novel quantitative approach to shed light on the complex information dynamics arising from the growing use of generative AI tools.<n>We propose a model to characterize the generation, indexing, and dissemination of information in response to new topics.<n>Our findings suggest that the rapid pace of generative AI adoption, combined with increasing user reliance, can outpace human verification, escalating the risk of inaccurate information proliferation.
arXiv Detail & Related papers (2025-04-29T10:21:40Z) - Beware of "Explanations" of AI [16.314859121110945]
Understanding the decisions made and actions taken by increasingly complex AI system remains a key challenge.<n>This has led to an expanding field of research in explainable artificial intelligence (XAI)<n>The question of what constitutes a "good" explanation is dependent on the goals, stakeholders, and context.
arXiv Detail & Related papers (2025-04-09T11:31:08Z) - Open Problems in Machine Unlearning for AI Safety [61.43515658834902]
Machine unlearning -- the ability to selectively forget or suppress specific types of knowledge -- has shown promise for privacy and data removal tasks.<n>In this paper, we identify key limitations that prevent unlearning from serving as a comprehensive solution for AI safety.
arXiv Detail & Related papers (2025-01-09T03:59:10Z) - SoK: Security and Privacy Risks of Healthcare AI [15.655956766190256]
The integration of artificial intelligence (AI) and machine learning (ML) into healthcare systems holds great promise for enhancing patient care.<n>However, it also exposes sensitive data and system integrity to potential cyberattacks.<n>Current security and privacy (S&P) research on healthcare AI is highly unbalanced in terms of healthcare deployment scenarios and threat models.
arXiv Detail & Related papers (2024-09-11T16:59:58Z) - 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) - Navigating Healthcare Insights: A Birds Eye View of Explainability with
Knowledge Graphs [0.0]
Knowledge graphs (KGs) are gaining prominence in Healthcare AI, especially in drug discovery and pharmaceutical research.
This overview summarizes recent literature on the impact of KGs in healthcare and their role in developing explainable AI models.
We emphasize the importance of making KGs more interpretable through knowledge-infused learning in healthcare.
arXiv Detail & Related papers (2023-09-28T16:57:03Z) - The Design and Implementation of a National AI Platform for Public
Healthcare in Italy: Implications for Semantics and Interoperability [62.997667081978825]
The Italian National Health Service is adopting Artificial Intelligence through its technical agencies.
Such a vast programme requires special care in formalising the knowledge domain.
Questions have been raised about the impact that AI could have on patients, practitioners, and health systems.
arXiv Detail & Related papers (2023-04-24T08:00:02Z) - Adaptive cognitive fit: Artificial intelligence augmented management of
information facets and representations [62.997667081978825]
Explosive growth in big data technologies and artificial intelligence [AI] applications have led to increasing pervasiveness of information facets.
Information facets, such as equivocality and veracity, can dominate and significantly influence human perceptions of information.
We suggest that artificially intelligent technologies that can adapt information representations to overcome cognitive limitations are necessary.
arXiv Detail & Related papers (2022-04-25T02:47:25Z) - Explainable AI meets Healthcare: A Study on Heart Disease Dataset [0.0]
The aim is to enlighten practitioners on the understandability and interpretability of explainable AI systems using a variety of techniques.
Our paper contains examples based on the heart disease dataset and elucidates on how the explainability techniques should be preferred to create trustworthiness.
arXiv Detail & Related papers (2020-11-06T05:18:43Z) - 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.