Evidence map›Paper›PMID 40051468›Full record

ArticleFuture internet2024

Human-AI Collaboration for Remote Sighted Assistance: Perspectives from the LLM Era.

Rui Yu, Sooyeon Lee, Jingyi Xie, Syed Masum Billah, John M Carroll

Abstract read
In one paragraph

Article in Future internet, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Enhancing the Travel Experience for People with Visual Impairments through Multimodal Interaction: NaviGPT, A Real-Time AI-Driven Mobile Navigation System.GROUP ... : proceedings of the International ACM SIGCHI Conference on Supporting Group Work. ACM SIGCHI International Conference on Supporting Group Work · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Rui YuDepartment of Computer Science and Engineering, University of Louisville, Louisville, KY 40208, USA.ORCID 0000-0002-0946-6769
Sooyeon LeeDepartment of Informatics, Ying Wu College of Computing, New Jersey Institute of Technology, Newark, NJ 07102, USA.ORCID 0000-0002-4971-2004
Jingyi XieCollege of Information Sciences and Technology, Pennsylvania State University, University Park, PA 16802, USA.ORCID 0000-0002-2753-2360
Syed Masum BillahCollege of Information Sciences and Technology, Pennsylvania State University, University Park, PA 16802, USA.ORCID 0000-0001-5063-3808
John M CarrollCollege of Information Sciences and Technology, Pennsylvania State University, University Park, PA 16802, USA.ORCID 0000-0001-5189-337X

Funding

SCH: INT: Conversations for Vision: Human-Computer Synergies in Prosthetic Interactions R01LM013330 · NLM · PENNSYLVANIA STATE UNIVERSITY, THE · PI CARROLL, JOHN MILLAR · 2019 to 2021
$713k
NLM NIH HHS R01 LM013330
6 · The paper itself

Abstract

Remote sighted assistance (RSA) has emerged as a conversational technology aiding people with visual impairments (VI) through real-time video chat communication with sighted agents. We conducted a literature review and interviewed 12 RSA users to understand the technical and navigational challenges faced by both agents and users. The technical challenges were categorized into four groups: agents' difficulties in orienting and localizing users, acquiring and interpreting users' surroundings and obstacles, delivering information specific to user situations, and coping with poor network connections. We also presented 15 real-world navigational challenges, including 8 outdoor and 7 indoor scenarios. Given the spatial and visual nature of these challenges, we identified relevant computer vision problems that could potentially provide solutions. We then formulated 10 emerging problems that neither human agents nor computer vision can fully address alone. For each emerging problem, we discussed solutions grounded in human-AI collaboration. Additionally, with the advent of large language models (LLMs), we outlined how RSA can integrate with LLMs within a human-AI collaborative framework, envisioning the future of visual prosthetics.

Indexed as

artificial intelligencecomputer visionconversational assistancehuman–AI collaborationlarge language modelspeople with visual impairmentsremote sighted assistance

Identifiers

PMID40051468
PMCPMC11884418

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.