Natural language processing in radiology: Clinical applications and future directions

Artificial intelligence applications and aging (1995-2024): Trends, challenges, and future directions in frailty research

BACKGROUND

Frailty, a significant predictor of adverse health outcomes, has become a focal point of research, particularly with the advent of artificial intelligence (AI) technologies. This study aimed to provide a comprehensive bibliometric analysis of research trends in AI and frailty to map conceptual developments, collaborations, and emerging themes in the field.

METHODS

A systematic search was conducted using the Scopus database employing a comprehensive set of keywords related to AI and frailty. The search was refined to include only original articles in English, yielding 1213 documents. Data extraction was performed in October 2024 and exported in the CSV and BibTeX formats. Annual growth trends were analyzed using Microsoft Excel, while VOSviewer and R-package were used for bibliometric analyzes and visualization to identify key contributors, collaborations, and thematic clusters.

RESULTS

The analysis revealed rapid growth in research publications, with AI applications in frailty gaining prominence over the past decade. Thematic clusters highlight areas such as predictive modeling, machine learning applications, and geriatric care innovations. The United States, United Kingdom, and Italy emerged as leading contributors to publications and collaborations. The key topics included prediction models, dementia, sarcopenia, and rehabilitation. This bibliometric study underscores the increasing integration of AI into frailty research, revealing key trends, collaborative networks, and emerging areas of focus.

CONCLUSION

These findings can guide future research, foster collaborations, and enhance the application of AI technologies to improve frailty assessment and management.

Neuradicon: Operational representation learning of neuroimaging reports

BACKGROUND AND OBJECTIVE

Radiological reports typically summarize the content and interpretation of imaging studies in unstructured form that precludes quantitative analysis. This limits the monitoring of radiological services to throughput undifferentiated by content, impeding specific, targeted operational optimization. Here we present Neuradicon, a natural language processing (NLP) framework for quantitative analysis of neuroradiological reports.

METHODS

Our framework is a hybrid of rule-based and machine-learning models to represent neurological reports in succinct, quantitative form optimally suited to operational guidance. These include probabilistic models for text classification and tagging tasks, alongside auto-encoders for learning latent representations and statistical mapping of the latent space.

RESULTS

We demonstrate the application of Neuradicon to operational phenotyping of a corpus of 336,569 reports, and report excellent generalizability across time and two independent healthcare institutions. In particular, we report pathology classification metrics with f1-scores of 0.96 on prospective data, and semantic means of interrogating the phenotypes surfaced via latent space representations.

CONCLUSION

Neuradicon allows the segmentation, analysis, classification, representation and interrogation of neuroradiological reports structure and content. It offers a blueprint for the extraction of rich, quantitative, actionable signals from unstructured text data in an operational context.

Transformation and articulation of clinical data to understand students' clinical reasoning: a scoping review

BACKGROUND

Despite the importance of effective educational strategies to promote the transformation and articulation of clinical data while teaching and learning clinical reasoning, unanswered questions remain. Understanding how these cognitive operations can be observed and assessed is crucial, particularly considering the rapid growth of artificial intelligence and its integration into health education. A scoping review was conducted to map the literature regarding educational strategies to support transformation and articulation of clinical data, the learning tasks expected of students when exposed to these strategies and methods used to assess individuals' proficiency METHODS: Based on the Joanna Briggs Institute methodology, the authors searched 5 databases (CINAHL, MEDLINE, EMBASE, PsycINFO and Web of Science), ProQuest Dissertations & Theses electronic database and Google Scholar. The data were synthesized narratively using descriptive statistics.

RESULTS

A total of 38 articles were included in the final synthesis. Most studies were conducted in North America and Europe (n = 30, 79%) focused primarily on medical students (n = 35, 92%) and mainly used observational (n = 17, 45%) or methodological (n = 8, 21%) designs. Various educational strategies were identified, the most common were resolution of written or computerized case-based scenarios (n = 13; 52%) and simulated or real patient encounters (n = 6; 24%). The learning tasks comprised, among others, identifying key findings, translating clinical information, synthesizing cases aloud, and writing a summary statement. Furthermore, the review included assessment methods and rubrics with assessment criteria for clinical data transformation and articulation. The narrative synthesis shows positive results when integrating various educational strategies within clinical reasoning curricula compared to a single strategy used episodically.

LIMITATIONS AND CONCLUSIONS

The varying objectives, diversity of educational strategies documented, and heterogeneity of the evaluation tools or rubrics limit our conclusions. However, insights gained will help educators develop effective approaches for teaching clinical reasoning. Additional research is needed to evaluate the impacts of educational strategies aimed at developing skills for the transformation and articulation of clinical data.

CLINICAL TRIAL NUMBER

Not applicable.

Classification of Interventional Radiology Reports into Technique Categories with a Fine-Tuned Large Language Model

The aim of this study is to develop a fine-tuned large language model that classifies interventional radiology reports into technique categories and to compare its performance with readers. This retrospective study included 3198 patients (1758 males and 1440 females; age, 62.8 ± 16.8 years) who underwent interventional radiology from January 2018 to July 2024. Training, validation, and test datasets involved 2292, 250, and 656 patients, respectively. Input data involved texts in clinical indication, imaging diagnosis, and image-finding sections of interventional radiology reports. Manually classified technique categories (15 categories in total) were utilized as reference data. Fine-tuning of the Bidirectional Encoder Representations model was performed using training and validation datasets. This process was repeated 15 times due to the randomness of the learning process. The best-performed model, which showed the highest accuracy among 15 trials, was selected to further evaluate its performance in the independent test dataset. The report classification involved one radiologist (reader 1) and two radiology residents (readers 2 and 3). The accuracy and macrosensitivity (average of each category's sensitivity) of the best-performed model in the validation dataset were 0.996 and 0.994, respectively. For the test dataset, the accuracy/macrosensitivity were 0.988/0.980, 0.986/0.977, 0.989/0.979, and 0.988/0.980 in the best model, reader 1, reader 2, and reader 3, respectively. The model required 0.178 s required for classification per patient, which was 17.5-19.9 times faster than readers. In conclusion, fine-tuned large language model classified interventional radiology reports into technique categories with high accuracy similar to readers within a remarkably shorter time.

NLP-Driven Analysis of Pneumothorax Incidence Following Central Venous Catheter Procedures: A Data-Driven Re-Evaluation of Routine Imaging in Value-Based Medicine

Background: This study presents a systematic approach using a natural language processing (NLP) algorithm to assess the necessity of routine imaging after central venous catheter (CVC) placement and removal. With pneumothorax being a key complication of CVC procedures, this research aims to provide evidence-based recommendations for optimizing imaging protocols and minimizing unnecessary imaging risks. Methods: We analyzed electronic health records from four university hospitals in Salzburg, Austria, focusing on X-rays performed between 2012 and 2021 following CVC procedures. A custom-built NLP algorithm identified cases of pneumothorax from radiologists' reports and clinician requests, while excluding cases with contraindications such as chest injuries, prior pneumothorax, or missing data. Chi-square tests were used to compare pneumothorax rates between CVC insertion and removal, and multivariate logistic regression identified risk factors, with a focus on age and gender. Results: This study analyzed 17,175 cases of patients aged 18 and older, with 95.4% involving CVC insertion and 4.6% involving CVC removal. Pneumothorax was observed in 106 cases post-insertion (1.3%) and in 3 cases post-removal (0.02%), with no statistically significant difference between procedures (p = 0.5025). The NLP algorithm achieved an accuracy of 93%, with a sensitivity of 97.9%, a specificity of 87.9%, and an area under the ROC curve (AUC) of 0.9283. Conclusions: The findings indicate no significant difference in pneumothorax incidence between CVC insertion and removal, supporting existing recommendations against routine imaging post-removal for asymptomatic patients and suggesting that routine imaging after CVC insertion may also be unnecessary in similar cases. This study demonstrates how advanced NLP techniques can support value-based medicine by enhancing clinical decision making and optimizing resources.

Prevalence and Predictors of Concomitant Meniscal Surgery During Pediatric and Adolescent ACL Reconstruction: Analysis of 4729 Patients Over 20 Years at a Tertiary-Care Regional Children's Hospital

Background

The rate of concomitant meniscal procedures performed in conjunction with anterior cruciate ligament (ACL) reconstruction is increasing. Few studies have examined these procedures in high-risk pediatric cohorts.

Hypotheses

That (1) the rates of meniscal repair compared with meniscectomy would increase throughout the study period and (2) patient-related factors would be able to predict the type of meniscal operation, which would differ according to age.

Study Design

Cohort study (prevalence); Level of evidence, 2.

Methods

Natural language processing was used to extract clinical variables from notes of patients who underwent ACL reconstruction between 2000 and 2020 at a single institution. Patients were stratified to pediatric (5-13 years) and adolescent (14-19 years) cohorts. Linear regression was used to evaluate changes in the prevalence of concomitant meniscal surgery during the study period. Logistic regression was used to determine predictors of the need for and type of meniscal procedure.

Results

Of 4729 patients (mean age, 16 ± 2 years; 54.7% female) identified, 2458 patients (52%) underwent concomitant meniscal procedures (55% repair rate). The prevalence of lateral meniscal (LM) procedures increased in both pediatric and adolescent cohorts, whereas the prevalence of medial meniscal (MM) repair increased in the adolescent cohort (P = .02). In the adolescent cohort, older age was predictive of concomitant medial meniscectomy (P = .031). In the pediatric cohort, female sex was predictive of concomitant MM surgery and of undergoing lateral meniscectomy versus repair (P≤ .029). Female sex was associated with decreased odds of concomitant LM surgery in both cohorts (P≤ .018). Revision ACLR was predictive of concomitant MM surgery and of meniscectomy (medial and lateral) in the adolescent cohort (P < .001). Higher body mass index was associated with increased odds of undergoing medial meniscectomy versus repair in the pediatric cohort (P = .03).

Conclusion

More than half of the young patients who underwent ACLR had meniscal pathology warranting surgical intervention. The prevalence of MM repair compared with meniscectomy in adolescents increased throughout the study period. Patients who underwent revision ACLR were more likely to undergo concomitant meniscal surgeries, which were more often meniscectomy. Female sex had mixed effects in both the pediatric and adolescent cohorts.

Transformation and Articulation of Clinical Data to Understand Students' and Health Professionals' Clinical Reasoning: Protocol for a Scoping Review

BACKGROUND

There are still unanswered questions regarding effective educational strategies to promote the transformation and articulation of clinical data while teaching and learning clinical reasoning. Additionally, understanding how this process can be analyzed and assessed is crucial, particularly considering the rapid growth of natural language processing in artificial intelligence.

OBJECTIVE

The aim of this study is to map educational strategies to promote the transformation and articulation of clinical data among students and health care professionals and to explore the methods used to assess these individuals' transformation and articulation of clinical data.

METHODS

This scoping review follows the Joanna Briggs Institute framework for scoping reviews and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist for the analysis. A literature search was performed in November 2022 using 5 databases: CINAHL (EBSCOhost), MEDLINE (Ovid), Embase (Ovid), PsycINFO (Ovid), and Web of Science (Clarivate). The protocol was registered on the Open Science Framework in November 2023. The scoping review will follow the 9-step framework proposed by Peters and colleagues of the Joanna Briggs Institute. A data extraction form has been developed using key themes from the research questions.

RESULTS

After removing duplicates, the initial search yielded 6656 results, and study selection is underway. The extracted data will be qualitatively analyzed and presented in a diagrammatic or tabular form alongside a narrative summary. The review will be completed by February 2024.

CONCLUSIONS

By synthesizing the evidence on semantic transformation and articulation of clinical data during clinical reasoning education, this review aims to contribute to the refinement of educational strategies and assessment methods used in academic and continuing education programs. The insights gained from this review will help educators develop more effective semantic approaches for teaching or learning clinical reasoning, as opposed to fragmented, purely symptom-based or probabilistic approaches. Besides, the results may suggest some ways to address challenges related to the assessment of clinical reasoning and ensure that the assessment tasks accurately reflect learners' developing competencies and educational progress.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/50797.