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Mohamed I, Bera K, Ramaiya N. The Undermined ACGME Subcompetency: A Roadmap for Radiology Residency Programs to Foster Residents-as-Educators. Acad Radiol 2024; 31:1189-1197. [PMID: 38052673 DOI: 10.1016/j.acra.2023.10.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 10/09/2023] [Accepted: 10/13/2023] [Indexed: 12/07/2023]
Abstract
Radiology Residency programs in the United States use a set of six core competencies as laid out by the Accreditation Council for Graduate Medical Education (ACGME) to evaluate the foundational skills of every resident. Despite the fact that educational skills are included under the heading of Practice-Based Learning and Improvement in the ACGME guidelines for radiology residents, it is often underappreciated and undervalued, when compared with medical knowledge or patient care. In this paper, the authors lay out the important role of residents-as-educators and how it can be inculcated as part of formal training during residency. They enunciate five pillars for academic programs to build and maintain the pedagogical skills of their radiology residents: Training, Practicing, Providing Feedback, Mentoring, and Changing the Culture. The authors believe that implementing this will holistically benefit radiology residents as well as radiology in building future educators. The authors also delineate the challenges that programs currently face in implementation and ways to overcome them.
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Affiliation(s)
- Inas Mohamed
- Department of Radiology, University Hospitals Cleveland Medical Center, 11000 Euclid Avenue, Cleveland, OH 44106
| | - Kaustav Bera
- Department of Radiology, University Hospitals Cleveland Medical Center, 11000 Euclid Avenue, Cleveland, OH 44106.
| | - Nikhil Ramaiya
- Department of Radiology, University Hospitals Cleveland Medical Center, 11000 Euclid Avenue, Cleveland, OH 44106
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Chien A, Tang H, Jagessar B, Chang KW, Peng N, Nael K, Salamon N. AI-Assisted Summarization of Radiologic Reports: Evaluating GPT3davinci, BARTcnn, LongT5booksum, LEDbooksum, LEDlegal, and LEDclinical. AJNR Am J Neuroradiol 2024; 45:244-248. [PMID: 38238092 DOI: 10.3174/ajnr.a8102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 11/09/2023] [Indexed: 02/09/2024]
Abstract
BACKGROUND AND PURPOSE The review of clinical reports is an essential part of monitoring disease progression. Synthesizing multiple imaging reports is also important for clinical decisions. It is critical to aggregate information quickly and accurately. Machine learning natural language processing (NLP) models hold promise to address an unmet need for report summarization. MATERIALS AND METHODS We evaluated NLP methods to summarize longitudinal aneurysm reports. A total of 137 clinical reports and 100 PubMed case reports were used in this study. Models were 1) compared against expert-generated summary using longitudinal imaging notes collected in our institute and 2) compared using publicly accessible PubMed case reports. Five AI models were used to summarize the clinical reports, and a sixth model, the online GPT3davinci NLP large language model (LLM), was added for the summarization of PubMed case reports. We assessed the summary quality through comparison with expert summaries using quantitative metrics and quality reviews by experts. RESULTS In clinical summarization, BARTcnn had the best performance (BERTscore = 0.8371), followed by LongT5Booksum and LEDlegal. In the analysis using PubMed case reports, GPT3davinci demonstrated the best performance, followed by models BARTcnn and then LEDbooksum (BERTscore = 0.894, 0.872, and 0.867, respectively). CONCLUSIONS AI NLP summarization models demonstrated great potential in summarizing longitudinal aneurysm reports, though none yet reached the level of quality for clinical usage. We found the online GPT LLM outperformed the others; however, the BARTcnn model is potentially more useful because it can be implemented on-site. Future work to improve summarization, address other types of neuroimaging reports, and develop structured reports may allow NLP models to ease clinical workflow.
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Affiliation(s)
- Aichi Chien
- From the Department of Radiological Science (A.C., H.T., B.J., K.N., N.S.), David Geffen School of Medicine at UCLA, Los Angeles, California
| | - Hubert Tang
- From the Department of Radiological Science (A.C., H.T., B.J., K.N., N.S.), David Geffen School of Medicine at UCLA, Los Angeles, California
| | - Bhavita Jagessar
- From the Department of Radiological Science (A.C., H.T., B.J., K.N., N.S.), David Geffen School of Medicine at UCLA, Los Angeles, California
| | - Kai-Wei Chang
- Department of Computer Science (K.C., N.P.), University of California, Los Angeles, Los Angeles, California
| | - Nanyun Peng
- Department of Computer Science (K.C., N.P.), University of California, Los Angeles, Los Angeles, California
| | - Kambiz Nael
- From the Department of Radiological Science (A.C., H.T., B.J., K.N., N.S.), David Geffen School of Medicine at UCLA, Los Angeles, California
| | - Noriko Salamon
- From the Department of Radiological Science (A.C., H.T., B.J., K.N., N.S.), David Geffen School of Medicine at UCLA, Los Angeles, California
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Truhn D, Weber CD, Braun BJ, Bressem K, Kather JN, Kuhl C, Nebelung S. A pilot study on the efficacy of GPT-4 in providing orthopedic treatment recommendations from MRI reports. Sci Rep 2023; 13:20159. [PMID: 37978240 PMCID: PMC10656559 DOI: 10.1038/s41598-023-47500-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 11/14/2023] [Indexed: 11/19/2023] Open
Abstract
Large language models (LLMs) have shown potential in various applications, including clinical practice. However, their accuracy and utility in providing treatment recommendations for orthopedic conditions remain to be investigated. Thus, this pilot study aims to evaluate the validity of treatment recommendations generated by GPT-4 for common knee and shoulder orthopedic conditions using anonymized clinical MRI reports. A retrospective analysis was conducted using 20 anonymized clinical MRI reports, with varying severity and complexity. Treatment recommendations were elicited from GPT-4 and evaluated by two board-certified specialty-trained senior orthopedic surgeons. Their evaluation focused on semiquantitative gradings of accuracy and clinical utility and potential limitations of the LLM-generated recommendations. GPT-4 provided treatment recommendations for 20 patients (mean age, 50 years ± 19 [standard deviation]; 12 men) with acute and chronic knee and shoulder conditions. The LLM produced largely accurate and clinically useful recommendations. However, limited awareness of a patient's overall situation, a tendency to incorrectly appreciate treatment urgency, and largely schematic and unspecific treatment recommendations were observed and may reduce its clinical usefulness. In conclusion, LLM-based treatment recommendations are largely adequate and not prone to 'hallucinations', yet inadequate in particular situations. Critical guidance by healthcare professionals is obligatory, and independent use by patients is discouraged, given the dependency on precise data input.
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Grants
- ODELIA, 101057091 European Union's Horizon Europe programme
- COMFORT, 101079894 European Union's Horizon Europe programme
- TR 1700/7-1 Deutsche Forschungsgemeinschaft
- NE 2136/3-1 Deutsche Forschungsgemeinschaft
- DEEP LIVER, ZMVI1-2520DAT111 Bundesministerium für Gesundheit
- #70113864 Max-Eder-Programme of the German Cancer Aid
- PEARL, 01KD2104C German Federal Ministry of Education and Research
- CAMINO, 01EO2101 German Federal Ministry of Education and Research
- SWAG, 01KD2215A German Federal Ministry of Education and Research
- TRANSFORM LIVER, 031L0312A German Federal Ministry of Education and Research
- TANGERINE, 01KT2302 through ERA-NET Transcan German Federal Ministry of Education and Research
- SECAI, 57616814 Deutscher Akademischer Austauschdienst
- Transplant.KI, 01VSF21048 German Federal Joint Committee
- ODELIA, 101057091 European Union's Horizon Europe and innovation programme
- GENIAL, 101096312 European Union's Horizon Europe and innovation programme
- NIHR, NIHR213331 National Institute for Health and Care Research
- European Union’s Horizon Europe programme
- European Union’s Horizon Europe and innovation programme
- RWTH Aachen University (3131)
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Affiliation(s)
- Daniel Truhn
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Pauwels Street 30, 52074, Aachen, Germany
| | - Christian D Weber
- Department of Orthopaedics and Trauma Surgery, University Hospital RWTH Aachen, Aachen, Germany
| | - Benedikt J Braun
- University Hospital Tuebingen on Behalf of the Eberhard-Karls-University Tuebingen, BG Hospital, Schnarrenbergstr. 95, Tübingen, Germany
| | - Keno Bressem
- Department of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Jakob N Kather
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
- Department of Medicine I, University Hospital Dresden, Dresden, Germany
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
- Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany
| | - Christiane Kuhl
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Pauwels Street 30, 52074, Aachen, Germany
| | - Sven Nebelung
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Pauwels Street 30, 52074, Aachen, Germany.
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