Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Equipping Speech-Language Clinicians for the Critical Appraisal of an Artificial Intelligence–Driven, Evidence-Based Future
3
Zitationen
2
Autoren
2025
Jahr
Abstract
PURPOSE: Artificial intelligence (AI) is more capable and accessible than ever before. But what does this mean for clinical practice? How can speech-language clinicians evaluate the efficacy, validity, and reliability of AI and machine learning tools for automating assessment and treatment? How can speech-language clinicians ethically use these clinical AI technologies? We contend that clinical AI will best serve clinicians and clients when aligned with an evidence-based framework. Therefore, this tutorial presents guidelines for the critical appraisal of clinical AI through the lens of validity, reliability, ethical use, and equitable use, facilitated by the Critical Appraisal Rubric for Ethical and Equitable Clinical Artificial Intelligence. Similarly, in order for developers of clinical AI to meet the needs of the profession, these principles should guide the development and assessment of new clinical technologies. CONCLUSIONS: The questions of efficacy, validity, reliability, ethical use, and equitable use of clinical AI can be answered through the examination of a specific clinical AI for a given user, as emphasized by culturally responsive professional practice. A framework is provided to assist clinicians in the critical appraisal of clinical AI tools.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.663 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.576 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.091 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.859 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.