arXiv subjects

Frank W. Samuelson

Publications and source records attributed to Frank W. Samuelson.

4 recordsLinked to original sources

arXiv2026

A theory of ROC analysis of rule-out and rule-in diagnostics with applications to mammography data

Multiple diagnostic tests are frequently used to determine the presence of a disease condition in patients. In this paper, we use bivariate copulas to examine the properties of receiver operating characteristic (ROC) curves formed when two correlated diagnostic tests are used together to rule-out ("believe the negative") and rule-in ("believe the positive") patients for disease. We use this theory to analyze three mammography data sets where AI devices are applied to reduce radiologists' workload or improve diagnostic performance. Our analysis shows with generality that increasing the radiologist-AI correlation for diseased cases enhances the area under the ROC curve (AUC) of a radiologist-AI rule-out curve, whereas decreasing correlation for non-diseased cases has a similar effect. The opposite trends hold for rule-in scenarios. Applications to clinical mammography data show that projected empirical radiologist performance under a rule-out or rule-in scenario is consistent with the theory.

stat.ME↗

arXiv2026

Image Quality in the Era of Artificial Intelligence

Artificial intelligence (AI) is being deployed within radiology at a rapid pace. AI has proven an excellent tool for reconstructing and enhancing images that appear sharper, smoother, and more detailed, can be acquired more quickly, and allowing clinicians to review them more rapidly. However, incorporation of AI also introduces new failure modes and can exacerbate the disconnect between perceived quality of an image and information content of that image. Understanding the limitations of AI-enabled image reconstruction and enhancement is critical for safe and effective use of the technology. Hence, the purpose of this communication is to bring awareness to limitations when AI is used to reconstruct or enhance a radiological image, with the goal of enabling users to reap benefits of the technology while minimizing risks.

cs.AI↗

arXiv2025

A Quantitative Framework to Predict Wait-Time Impacts Due to AI-Triage Devices in a Multi-AI, Multi-Disease Workflow

The deployment of multiple AI-triage devices in radiology departments has grown rapidly, yet the cumulative impact on patient wait-times across different disease conditions remains poorly understood. This research develops a comprehensive mathematical and simulation framework to quantify wait-time trade-offs when multiple AI-triage devices operate simultaneously in a clinical workflow. We created multi-QuCAD, a software tool that models complex multi-AI, multi-disease scenarios using queueing theory principles, incorporating realistic clinical parameters including disease prevalence rates, radiologist reading times, and AI performance characteristics from FDA-cleared devices. The framework was verified through four experimental scenarios ranging from simple two-disease workflows to complex nine-disease systems, comparing preemptive versus non-preemptive scheduling disciplines and priority versus hierarchical triage protocols. Analysis of brain imaging workflows demonstrated that while AI-triage devices significantly reduce wait-times for target conditions, they can substantially delay diagnosis of non-targeted, yet urgent conditions. The study revealed that hierarchical protocol generally provides more wait-time savings for the highest-priority conditions compared to the priority protocol, though at the expense of more delays to lower-priority patients with other time-sensitive conditions. The quantitative framework presented provides essential insights for orchestrating multi-AI deployments to maximize overall patient time-saving benefits while minimizing unintended delay for other important patient populations.

math.NA↗

arXiv2025

Impact of AI-Triage on Radiologist Report Turnaround Time: Real-World Time-Savings and Insights from Model Predictions

Objective: To quantify the impact of workflow parameters on time-savings in report turnaround time (TAT) due to an AI-triage device that prioritized pulmonary embolism (PE) in chest CT pulmonary angiography (CTPA) exams. Methods: This retrospective study analyzed 11252 adult CTPA exams conducted for suspected PE at a single tertiary academic medical center. Data was divided into two periods: pre-AI and post-AI. For PE-positive exams, TAT - defined as the duration from patient scan completion to the first preliminary report completion - was compared between the two periods. Time-savings were reported separately for work-hour and off-hour cohorts. To characterize radiologist workflow, 527234 records were retrieved from the PACS and workflow parameters such as exam inter-arrival time and radiologist read-time extracted. These parameters were input into a computational model to predict time-savings following deployment of an AI-triage device and to study the impact of workflow parameters. Results: The pre-AI dataset included 4694 chest CTPA exams with 13.3% being PE-positive. The post-AI dataset comprised 6558 exams with 16.2% being PE-positive. The mean TAT for pre-AI and post-AI during work hours are 68.9 [95% CI" 55.0, 82.8] and 46.7 [38.1, 55.2] minutes respectively, and those during off-hours are 44.8 [33.7, 55.9] and 42.0 [33.6, 50.3] minutes. Clinically-observed time-savings during work hours (22.2 [95% CI: 5.85, 38.6] minutes) were significant (p=0.004), while off-hour (2.82 [-11.1, 16.7] minutes) were not (p=0.345). Observed time-savings aligned with model predictions (29.6 [95% range: 23.2, 38.1] minutes for work hours; 2.10 [1.76, 2.58] minutes for off-hours). Discussion: Consideration and quantification of clinical workflow contribute to an accurate assessment of the expected time-savings in TAT following deployment of an AI-triage device.

cs.PF↗