Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
DR. INFO at the Point of Care: A Prospective Pilot Study of Physician-Perceived Value of an Agentic Artificial Intelligence Clinical Assistant
0
Zitationen
9
Autoren
2026
Jahr
Abstract
Background Clinical documentation and information retrieval consume over half of physicians’ working hours, contributing to cognitive overload and burnout. While artificial intelligence (AI) offers a potential solution, concerns over hallucinations and source reliability have limited adoption at the point of care. This study aimed to evaluate physician-perceived time efficiency, decision-making support, and satisfaction with DR. INFO, an agentic AI clinical assistant, in routine clinical practice. Methodology In this prospective, single-arm, pilot feasibility study, 29 physicians and medical students across multiple specialties in Portuguese healthcare institutions used DR. INFO v1.0 over five working days within a two-week period. Outcomes were assessed via daily Likert-scale evaluations (time saving and decision support) and a final Net Promoter Score (NPS). Non-parametric methods were used throughout, with bootstrap confidence intervals (CIs) and sensitivity analysis to address non-response. Results Physicians reported high perceived time saving (mean = 4.27/5; 95% CI = 3.97-4.57) and decision support (mean = 4.16/5; 95% CI = 3.86-4.45), with ratings stable across the five-day study window. Among the 16 (55%) participants who completed the final evaluation, the NPS was 81.2, with no detractors; sensitivity analysis indicated an NPS of 44.8 under conservative non-response assumptions. Conclusions Physicians across specialties and career stages reported positive perceptions of DR. INFO for both time efficiency and clinical decision support within the study window. These findings are preliminary and should be confirmed in larger, controlled studies that include objective performance measures and independent accuracy verification.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.508 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.393 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.864 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.564 Zit.