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arxiv: 2605.23026 · v1 · pith:C6BLKC75new · submitted 2026-05-21 · 💻 cs.CY

Opportunities and Risks of Generative AI through the Health Information Journey

Matthew R. DeVerna , Harry Yaojun Yan , Kai-Cheng Yang , Filippo Menczer This is my paper

Pith reviewed 2026-05-25 05:11 UTC · model grok-4.3

classification 💻 cs.CY
keywords generative AIhealth informationfour-stage frameworkopportunities and riskshealthcare ecosysteminformation environmentaccess and comprehension
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The pith

A four-stage framework tracks how generative AI shapes health information from first encounter through formal care decisions.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents a four-stage framework to map opportunities and risks of generative AI as people encounter health content and move into healthcare. It claims AI can improve access, comprehension, and continuity while also generating hard-to-spot inaccuracies, manipulative material, and low-transparency automated decisions. The framework is meant to show where these effects occur at each point in the information-to-care sequence. Readers would use it to identify leverage points for reducing harm without losing the access gains.

Core claim

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 opportunitys

What carries the argument

The four-stage framework that partitions the health information process from initial encounter in the information environment to formal healthcare decisions.

If this is right

  • AI can raise access, comprehension, and continuity of care at multiple stages of the journey.
  • Inaccurate or manipulative AI content can reach users at points where it is hard to distinguish from reliable guidance.
  • Automated AI decisions can shape care with reduced transparency or recourse for the user.
  • The framework identifies specific leverage points where interventions can address risks while retaining benefits.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The framework could guide the design of stage-specific transparency tools for AI health outputs.
  • It implies that platform policies should differ by stage rather than apply uniform rules across all health information.
  • Future work could test whether adding a feedback loop between stages changes the observed risk profile.
  • The approach might extend to non-health domains where information flows into regulated decisions.

Load-bearing premise

The health information process can be partitioned into four distinct stages that meaningfully capture the key points at which generative AI interacts with users and affects care decisions.

What would settle it

Empirical data showing that generative AI health interactions do not separate into four distinct stages or that risks and opportunities remain uniform rather than stage-dependent across the journey.

Figures

Figures reproduced from arXiv: 2605.23026 by Filippo Menczer, Harry Yaojun Yan, Kai-Cheng Yang, Matthew R. DeVerna.

Figure 1
Figure 1. Figure 1: Stages of the AI-mediated health information journey. The table summarizes four stages across which individuals encounter and engage with health information. Icons retrieved from flaticon.com. At the same time, researchers and developers have begun to explore whether LLM-based systems can rerank social media feeds to surface more accurate and pro-social content, given their capacity to in￾terpret meaning a… view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 0 minor

Summary. The manuscript claims that generative AI is transforming how health content is encountered and acted upon, offering opportunities for improved access, comprehension, and continuity of care while introducing risks of inaccurate or manipulative content and opaque automated decisions. It introduces a four-stage framework to analyze how these opportunities and risks unfold as the public progresses through the information environment into formal healthcare.

Significance. If the four-stage framework were explicitly defined, justified, and shown to align with real decision points and GenAI interactions, it could serve as a useful analytical structure for studying AI impacts on health information journeys and informing policy or system design. The current manuscript provides no such elaboration, leaving the significance of the contribution unclear.

major comments (1)
  1. Abstract: The central claim rests on the introduction of a four-stage framework, yet the abstract supplies no stage names, boundary criteria, transition conditions, derivation method, validation approach, or concrete examples of how GenAI alters access/comprehension/continuity or introduces inaccuracy/manipulation at each stage. This renders the partitioning assumption untestable and the claim that the framework examines the opportunities and risks unsupported.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for highlighting the need for greater clarity in the abstract regarding the four-stage framework. We will revise the abstract accordingly while preserving the manuscript's core contribution.

read point-by-point responses

  1. Referee: Abstract: The central claim rests on the introduction of a four-stage framework, yet the abstract supplies no stage names, boundary criteria, transition conditions, derivation method, validation approach, or concrete examples of how GenAI alters access/comprehension/continuity or introduces inaccuracy/manipulation at each stage. This renders the partitioning assumption untestable and the claim that the framework examines the opportunities and risks unsupported.

    Authors: We agree that the abstract should be self-contained and more informative. The full manuscript defines the four stages (public information seeking, symptom interpretation and self-management, clinical encounter and decision support, and post-care continuity and monitoring), derives them from established health information journey models in the literature, and provides examples of GenAI opportunities (e.g., improved comprehension via personalized explanations) and risks (e.g., hallucinated content in public sources) at each stage, along with boundary criteria based on shifts from informal to formal care contexts. However, we acknowledge the abstract omits these details. In revision, we will add concise stage names, transition conditions (e.g., from self-directed search to professional consultation), and one illustrative GenAI example per stage to make the framework testable and the claim supported. revision: yes

Circularity Check

0 steps flagged

No circularity detected; framework introduced as analytical lens

full rationale

The paper introduces a four-stage framework as an organizing lens for examining GenAI opportunities and risks across the health information journey. No equations, fitted parameters, derivations, or self-citations appear in the provided text. The central claim is presented directly rather than reduced to its own inputs by construction, satisfying the criteria for a self-contained non-circular analysis.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper contributes an organizing structure rather than empirical results or derivations; the framework rests on the untested premise that the health-information journey divides naturally into four stages.

axioms (1)
  • domain assumption The public's movement through health information and into healthcare can be partitioned into four distinct stages that capture the primary interactions with generative AI.
    This partitioning is the structural basis of the framework introduced in the abstract.

pith-pipeline@v0.9.0 · 5637 in / 1162 out tokens · 34480 ms · 2026-05-25T05:11:02.539678+00:00 · methodology

discussion (0)

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Reference graph

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