دورية أكاديمية
From Bench to Bedside With Large Language Models: AJR Expert Panel Narrative Review.
| العنوان: | From Bench to Bedside With Large Language Models: AJR Expert Panel Narrative Review. |
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| المؤلفون: | Bhayana R; University Medical Imaging Toronto, Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, ON, Canada., Biswas S; Department of Radiology, Le Bonheur Children's Hospital, University of Tennessee Health Science Center, Memphis, TN, USA., Cook TS; Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA., Kim W; Department of Radiology, Palo Alto VA Medical Center, Palo Alto, CA., Kitamura FC; Department of Diagnostic Imaging, Universidade Federal de São Paulo, São Paulo, Brazil.; Dasa, São Paulo, Brazil., Gichoya J; Department of Radiology, Emory University School of Medicine, Georgia, U.S.A., Yi PH; Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD. |
| المصدر: | AJR. American journal of roentgenology [AJR Am J Roentgenol] 2024 Apr 10. Date of Electronic Publication: 2024 Apr 10. |
| Publication Model: | Ahead of Print |
| نوع المنشور: | Journal Article; Review |
| اللغة: | English |
| بيانات الدورية: | Publisher: American Roentgen Ray Society Country of Publication: United States NLM ID: 7708173 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1546-3141 (Electronic) Linking ISSN: 0361803X NLM ISO Abbreviation: AJR Am J Roentgenol Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: <2004-> : Leesburg, VA : American Roentgen Ray Society Original Publication: Springfield, Ill., Thomas. |
| مستخلص: | Large language models (LLMs) hold immense potential to revolutionize radiology. However, their integration into practice requires careful consideration. Artificial intelligence (AI) chatbots and general-purpose LLMs have potential pitfalls related to privacy, transparency, and accuracy, limiting their current clinical readiness. Thus, LLM-based tools must be optimized for radiology practice to overcome these limitations. While research and validation for radiology applications remain in their infancy, commercial products incorporating LLMs are becoming available alongside promises of transforming practice. To help radiologists navigate this landscape, this AJR Expert Panel Narrative Review provides a multidimensional perspective on LLMs, encompassing considerations from bench (development and optimization) to bedside (use in practice). At present, LLMs are not autonomous entities that can replace expert decision-making, and radiologists remain responsible for the content of their reports. Patient-facing tools, particularly medical AI chatbots, require additional guardrails to ensure safety and prevent misuse. Still, if responsibly implemented, LLMs are well-positioned to transform efficiency and quality in radiology. Radiologists must be well-informed and proactively involved in guiding the implementation of LLMs in practice to mitigate risks and maximize benefits to patient care. |
| تواريخ الأحداث: | Date Created: 20240410 Latest Revision: 20240410 |
| رمز التحديث: | 20240411 |
| DOI: | 10.2214/AJR.24.30928 |
| PMID: | 38598354 |
| قاعدة البيانات: | MEDLINE |
| تدمد: | 1546-3141 |
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| DOI: | 10.2214/AJR.24.30928 |