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Abstract As artificial intelligence (AI) methods are increasingly used to develop new guidance intended for operational use by forecasters, it is critical to evaluate whether forecasters deem the guidance trustworthy. Past trust-related AI research suggests that certain attributes (e.g., understanding how the AI was trained, interactivity, and performance) contribute to users perceiving the AI as trustworthy. However, little research has been done to examine the role of these and other attributes for weather forecasters. In this study, we conducted 16 online interviews with National Weather Service (NWS) forecasters to examine (i) how they make guidance use decisions and (ii) how the AI model technique used, training, input variables, performance, and developers as well as interacting with the model output influenced their assessments of trustworthiness of new guidance. The interviews pertained to either a random forest model predicting the probability of severe hail or a 2D convolutional neural network model predicting the probability of storm mode. When taken as a whole, our findings illustrate how forecasters’ assessment of AI guidance trustworthiness is a process that occurs over time rather than automatically or at first introduction. We recommend developers center end users when creating new AI guidance tools, making end users integral to their thinking and efforts. This approach is essential for the development of useful andusedtools. The details of these findings can help AI developers understand how forecasters perceive AI guidance and inform AI development and refinement efforts. Significance StatementWe used a mixed-methods quantitative and qualitative approach to understand how National Weather Service (NWS) forecasters 1) make guidance use decisions within their operational forecasting process and 2) assess the trustworthiness of prototype guidance developed using artificial intelligence (AI). When taken as a whole, our findings illustrate that forecasters’ assessment of AI guidance trustworthiness is a process that occurs over time rather than automatically and suggest that developers must center the end user when creating new AI guidance tools to ensure that the developed tools are useful andused.
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This content will become publicly available on July 9, 2026
Medical needles in the hands of AI: Advancing toward autonomous robotic navigation
Safely and accurately navigating needles percutaneously or endoscopically to sites deep within the body is essential for many medical procedures, from biopsies to localized drug deliveries to tumor ablations. The advent of image guidance decades ago gave physicians information about the patient’s anatomy. We are now entering the era of AI (artificial intelligence) guidance, where AI can automatically analyze images, identify targets and obstacles, compute safe trajectories, and autonomously navigate a needle to a site with unprecedented accuracy and precision. We survey recent advances in the building blocks of AI guidance for medical needle deployment robots (perceiving anatomy, planning motions, perceiving instrument state, and performing motions) and discuss research opportunities to maximize the benefits of AI guidance for patient care.
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- PAR ID:
- 10637885
- Publisher / Repository:
- Science Robotics
- Date Published:
- Journal Name:
- Science Robotics
- Volume:
- 10
- Issue:
- 104
- ISSN:
- 2470-9476
- Page Range / eLocation ID:
- eadt1874
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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