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.
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http://dx.doi.org/10.3390/fi16070254 | DOI Listing |
Eur Radiol
March 2025
Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine and Health, Technical University Munich, Munich, Germany.
Objectives: This study investigated the impact of human-large language model (LLM) collaboration on the accuracy and efficiency of brain MRI differential diagnosis.
Materials And Methods: In this retrospective study, forty brain MRI cases with a challenging but definitive diagnosis were randomized into two groups of twenty cases each. Six radiology residents with an average experience of 6.
Eur Radiol
March 2025
Computational Clinical Imaging Group, Centre for the Unknown, Champalimaud Foundation Lisbon, Lisboa, Portugal.
This special report explores the integration of artificial intelligence (AI) into prostate MRI workflows to address limitations associated with single-reader interpretations, such as inter-reader variability and diagnostic errors. We review various AI-integrated workflow strategies, from AI-assisted decision support to fully autonomous analysis, examining their benefits and challenges. AI can act as a second reader, enhancing detection sensitivity and reducing false negatives or pre-screen cases for efficient triage, thereby optimising radiologist workload.
View Article and Find Full Text PDFFuture Internet
July 2024
College of Information Sciences and Technology, Pennsylvania State University, University Park, PA 16802, USA.
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.
View Article and Find Full Text PDFBMC Proc
March 2025
World Health Organization Hub for Pandemic and Epidemic Intelligence, Berlin, Germany.
The COVID-19 pandemic accelerated the development of AI-driven tools to improve public health surveillance and outbreak management. While AI programs have shown promise in disease surveillance, they also present issues such as data privacy, prejudice, and human-AI interactions. This sixth session of the of the WHO Pandemic and Epidemic Intelligence Innovation Forum examines the use of Artificial Intelligence (AI) in public health by collecting the experience of key global health organizations, such the Boston Children's Hospital, the Global South AI for Pandemic & Epidemic Preparedness & Response (AI4PEP) network, Medicines Sans Frontières (MSF), and the University of Sydney.
View Article and Find Full Text PDFFront Robot AI
February 2025
Team Performance Laboratory, Institute for Simulation and Training, University of Central Florida, Orlando, FL, United States.
This study examines the integration of Artificial Social Intelligence (ASI) into human teams, focusing on how ASI can enhance teamwork processes in complex tasks. Teams of three participants collaborated with ASI advisors designed to exhibit Artificial Theory of Mind (AToM) while engaged in an interdependent task. A profiling model was used to categorize teams based on their taskwork and teamwork potential and study how these influenced perceptions of team processes and ASI advisors.
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