Outcomes following minimally invasive dorsal cheilectomy for hallux rigidus: A systematic review.

BACKGROUND: Cheilectomy of the 1st metatarsophalangeal joint (MTPJ) is one of the most common procedures for the management of hallux rigidus. However, there is no consensus regarding outcomes following minimally invasive dorsal cheilectomy (MIDC) for the management of hallux rigidus. AIM: To evaluate outcomes following MIDC for the management of hallux rigidus. METHODS: During November 2023, the PubMed, EMBASE and Cochrane Library databases were systematically reviewed to identify clinical studies examining outcomes following MIDC for the management of hallux rigidus. RESULTS: Six studies were included. In total, 348 patients (370 feet) underwent MIDC for hallux rigidus at a weighted mean follow-up of 37.9 ± 16.5 months. The distribution of patients by Coughlin and Shurna's classification was recorded in 4 studies as follows: I (58 patients, 27.1%), II (112 patients, 52.3%), III (44 patients, 20.6%). Three studies performed an additional 1st MTPJ arthroscopy and debridement following MIDC. Retained intra-articular bone debris was observed in 100% of patients in 1 study. The weighted mean American orthopedic foot and ankle society score improved from a preoperative score of 68.9 ± 3.2 to a postoperative score of 87.1. The complication rate was 8.4%, the most common of which was persistent joint pain and stiffness. Thirty-two failures (8.7%) were observed. Thirty-three secondary procedures (8.9%) were performed at a weighted mean time of 8.6 ± 3.2 months following the index procedure. CONCLUSION: This systematic review demonstrated improvements in subjective clinical outcomes together with a moderate complication rate following MIDC for the management of hallux rigidus at short-term follow-up. A moderate re-operation rate at short-term follow-up was recorded. The marked heterogeneity between included studies and paucity of high quality comparative studies limits the generation of any robust conclusions.

From technical to understandable: Artificial Intelligence Large Language Models improve the readability of knee radiology reports.

PURPOSE: The purpose of this study was to evaluate the effectiveness of an Artificial Intelligence-Large Language Model (AI-LLM) at improving the readability of knee radiology reports. METHODS: Reports of 100 knee X-rays, 100 knee computed tomography (CT) scans and 100 knee magnetic resonance imaging (MRI) scans were retrieved. The following prompt command was inserted into the AI-LLM: 'Explain this radiology report to a patient in layman's terms in the second person:[Report Text]'. The Flesch-Kincaid reading level (FKRL) score, Flesch reading ease (FRE) score and report length were calculated for the original radiology report and the AI-LLM generated report. Any 'hallucination' or inaccurate text produced by the AI-LLM-generated report was documented. RESULTS: Statistically significant improvements in mean FKRL scores in the AI-LLM generated X-ray report (12.7 ± 1.0-7.2 ± 0.6), CT report (13.4 ± 1.0-7.5 ± 0.5) and MRI report (13.5 ± 0.9-7.5 ± 0.6) were observed. Statistically significant improvements in mean FRE scores in the AI-LLM generated X-ray report (39.5 ± 7.5-76.8 ± 5.1), CT report (27.3 ± 5.9-73.1 ± 5.6) and MRI report (26.8 ± 6.4-73.4 ± 5.0) were observed. Superior FKRL scores and FRE scores were observed in the AI-LLM-generated X-ray report compared to the AI-LLM-generated CT report and MRI report, p < 0.001. The hallucination rates in the AI-LLM generated X-ray report, CT report and MRI report were 2%, 5% and 5%, respectively. CONCLUSIONS: This study highlights the promising use of AI-LLMs as an innovative, patient-centred strategy to improve the readability of knee radiology reports. The clinical relevance of this study is that an AI-LLM-generated knee radiology report may enhance patients' understanding of their imaging reports, potentially reducing the responder burden placed on the ordering physicians. However, due to the 'hallucinations' produced by the AI-LLM-generated report, the ordering physician must always engage in a collaborative discussion with the patient regarding both reports and the corresponding images. LEVEL OF EVIDENCE: Level IV.

From jargon to clarity: Improving the readability of foot and ankle radiology reports with an artificial intelligence large language model.

BACKGROUND: The purpose of this study was to evaluate the efficacy of an Artificial Intelligence Large Language Model (AI-LLM) at improving the readability foot and ankle orthopedic radiology reports. METHODS: The radiology reports from 100 foot or ankle X-Rays, 100 computed tomography (CT) scans and 100 magnetic resonance imaging (MRI) scans were randomly sampled from the institution's database. The following prompt command was inserted into the AI-LLM: "Explain this radiology report to a patient in layman's terms in the second person: [Report Text]". The mean report length, Flesch reading ease score (FRES) and Flesch-Kincaid reading level (FKRL) were evaluated for both the original radiology report and the AI-LLM generated report. The accuracy of the information contained within the AI-LLM report was assessed via a 5-point Likert scale. Additionally, any "hallucinations" generated by the AI-LLM report were recorded. RESULTS: There was a statistically significant improvement in mean FRES scores in the AI-LLM generated X-Ray report (33.8 ± 6.8 to 72.7 ± 5.4), CT report (27.8 ± 4.6 to 67.5 ± 4.9) and MRI report (20.3 ± 7.2 to 66.9 ± 3.9), all p < 0.001. There was also a statistically significant improvement in mean FKRL scores in the AI-LLM generated X-Ray report (12.2 ± 1.1 to 8.5 ± 0.4), CT report (15.4 ± 2.0 to 8.4 ± 0.6) and MRI report (14.1 ± 1.6 to 8.5 ± 0.5), all p < 0.001. Superior FRES scores were observed in the AI-LLM generated X-Ray report compared to the AI-LLM generated CT report and MRI report, p < 0.001. The mean Likert score for the AI-LLM generated X-Ray report, CT report and MRI report was 4.0 ± 0.3, 3.9 ± 0.4, and 3.9 ± 0.4, respectively. The rate of hallucinations in the AI-LLM generated X-Ray report, CT report and MRI report was 4%, 7% and 6%, respectively. CONCLUSION: AI-LLM was an efficacious tool for improving the readability of foot and ankle radiological reports across multiple imaging modalities. Superior FRES scores together with superior Likert scores were observed in the X-Ray AI-LLM reports compared to the CT and MRI AI-LLM reports. This study demonstrates the potential use of AI-LLMs as a new patient-centric approach for enhancing patient understanding of their foot and ankle radiology reports. Jel Classifications: IV.