Role of AI in diagnostic imaging error reduction.

The topic of diagnostic imaging error and the tools and strategies for error mitigation are poorly investigated in veterinary medicine. The increasing popularity of diagnostic imaging and the high demand for teleradiology make mitigating diagnostic imaging errors paramount in high-quality services. The different sources of error have been thoroughly investigated in human medicine, and the use of AI-based products is advocated as one of the most promising strategies for error mitigation. At present, AI is still an emerging technology in veterinary medicine and, as such, is raising increasing interest among in board-certified radiologists and general practitioners alike. In this perspective article, the role of AI in mitigating different types of errors, as classified in the human literature, is presented and discussed. Furthermore, some of the weaknesses specific to the veterinary world, such as the absence of a regulatory agency for admitting medical devices to the market, are also discussed.

Instagram reels versus image posts in radiology education.

OBJECTIVE: In January 2016, we created an Instagram page for radiology education. Numerous publications in different fields have reported that Instagram "reels," introduced in 2020 as a short-form video feature, are more popular than image posts. These findings and our familiarity with Instagram prompted us to analyze our own data to better understand how image posts compared with reels when used in the context of radiology education. MATERIALS AND METHODS: For each post category, metric values were extracted from the Instagram platform and analyzed as continuous variables, reported as medians with interquartile ranges (IQR). Metrics were compared between image categories using the Kruskal-Wallis test, with resulting p-values adjusted for multiple comparisons using the Bonferroni correction. Corrected p-values of less than 0.05 were considered statistically significant. RESULTS: We included 128 images and 96 reels in the analysis. Images generally reached a larger audience, with a median of 18,745 [IQR: 13,478-27,243] impressions vs. 11,972 [IQR: 9,310.0-13,844.5] for reels (p < 0.01). Images also tended to be shared more frequently (median 19 vs. 20, p < 0.01), liked more often (median 480 vs. 296, p < 0.01), and saved more by users (median 138 vs. 84, p < 0.01) than reels, respectively. Both images and reels received a similar number of comments, with a median of 3 comments for both (p > 0.99). We also explored the performance differences of image post subcategories. Within images, our "You Make the Call!" (YMTC) questions (n = 23) displayed higher performance metrics across the board than the three other types of image posts combined (n = 105). When compared, the median number of impressions for YMTC images was 36,735 [IQR: 31,343-40,742] vs. 15,992 [IQR:12,774-21,873] for other types of images (p < 0.01). YMTC images were shared more often (median 25 vs. 17, p < 0.01), received more likes (median 809 vs. 445, p < 0.01) and saves (median 206 vs. 119, p < 0.01) than non-YMTC images, respectively. User engagement showed slightly different trends with YMTC reels being the most liked, while quiz reels receiving the most comments and talking clips being the most saved. CONCLUSION: Our findings on the use of Instagram in radiology education suggest that static images perform much better than reels. Consequently, we recommend to radiology educators seeking to establish an Instagram presence that using static image posts is an appropriate approach for reaching a radiology audience, particularly with image posts that engage an audience with participatory opportunities such as answering quiz-like questions aimed at making a diagnosis.

Radiology education for medical students: a qualitative exploration of educational topics, teaching methods and future strategies.

BACKGROUND: Imaging techniques play a central role in modern medicine and therefore it would be beneficial for all medical students to incorporate radiology education in medical school curricula. However, a formal undergraduate radiology curriculum with well-defined learning objectives remains lacking in The Netherlands. This study aims to qualitatively ascertain opinions from clinicians (radiologists and non-radiologists) with regard to radiology education in the medical school curricula, including topics, teaching methods and strategies. METHODS: A qualitative study with in-depth semi-structured interviews was conducted. Inclusion was carried out until saturation was achieved, after which 2 additional interviews were held. Interviews were conducted using open-ended questions, following a predefined topic list. The constant comparative method was applied in order to include new questions when unexpected topics arose during the interviews. All interviews were transcribed verbatim and coded using a thematic analysis approach. Codes were organized into categories and themes by discussion between the researchers. RESULTS: Forty-four clinicians were interviewed (8 radiologists, 36 non-radiologists). The three main themes that were derived from the interviews were: (1) expectations of indispensable knowledge and skills on radiology, (2) organization of radiology education within the medical curriculum and (3) promising educational innovations for the radiology curriculum. The qualitative study design provides more in-depth knowledge on clinicians' views on educational topics. CONCLUSIONS: The themes and statements of this study provided new insights into educational methods, timing of radiology education and new topics to teach. More research is needed to gain consensus on these subjects and inclusion of the opinion of medical students with regard to radiology education is needed.

Advancing radiology with GPT-4: Innovations in clinical applications, patient engagement, research, and learning.

The rapid evolution of artificial intelligence (AI) in healthcare, particularly in radiology, underscores a transformative era marked by a potential for enhanced diagnostic precision, increased patient engagement, and streamlined clinical workflows. Amongst the key developments at the heart of this transformation are Large Language Models like the Generative Pre-trained Transformer 4 (GPT-4), whose integration into radiological practices could potentially herald a significant leap by assisting in the generation and summarization of radiology reports, aiding in differential diagnoses, and recommending evidence-based treatments. This review delves into the multifaceted potential applications of Large Language Models within radiology, using GPT-4 as an example, from improving diagnostic accuracy and reporting efficiency to translating complex medical findings into patient-friendly summaries. The review acknowledges the ethical, privacy, and technical challenges inherent in deploying AI technologies, emphasizing the importance of careful oversight, validation, and adherence to regulatory standards. Through a balanced discourse on the potential and pitfalls of GPT-4 in radiology, the article aims to provide a comprehensive overview of how these models have the potential to reshape the future of radiological services, fostering improvements in patient care, educational methodologies, and clinical research.

From Revisions to Insights: Converting Radiology Report Revisions into Actionable Educational Feedback Using Generative AI Models.

Expert feedback on trainees' preliminary reports is crucial for radiologic training, but real-time feedback can be challenging due to non-contemporaneous, remote reading and increasing imaging volumes. Trainee report revisions contain valuable educational feedback, but synthesizing data from raw revisions is challenging. Generative AI models can potentially analyze these revisions and provide structured, actionable feedback. This study used the OpenAI GPT-4 Turbo API to analyze paired synthesized and open-source analogs of preliminary and finalized reports, identify discrepancies, categorize their severity and type, and suggest review topics. Expert radiologists reviewed the output by grading discrepancies, evaluating the severity and category accuracy, and suggested review topic relevance. The reproducibility of discrepancy detection and maximal discrepancy severity was also examined. The model exhibited high sensitivity, detecting significantly more discrepancies than radiologists (W = 19.0, p < 0.001) with a strong positive correlation (r = 0.778, p < 0.001). Interrater reliability for severity and type were fair (Fleiss' kappa = 0.346 and 0.340, respectively; weighted kappa = 0.622 for severity). The LLM achieved a weighted F1 score of 0.66 for severity and 0.64 for type. Generated teaching points were considered relevant in ~ 85% of cases, and relevance correlated with the maximal discrepancy severity (Spearman ρ = 0.76, p < 0.001). The reproducibility was moderate to good (ICC (2,1) = 0.690) for the number of discrepancies and substantial for maximal discrepancy severity (Fleiss' kappa = 0.718; weighted kappa = 0.94). Generative AI models can effectively identify discrepancies in report revisions and generate relevant educational feedback, offering promise for enhancing radiology training.

Beyond milestones and percentiles: Revisiting non-cognitive and non-interpretive skills in radiology.

While there is no precise formula for a great radiology resident, certain attributes and achievements may herald success during training. We briefly review prior works exploring predictive factors and evaluation metrics of top resident performance, noting that those focusing on non-cognitive attributes are over twenty years old. As radiology practice and education has substantially evolved in the interim, we revisit this topic from a contemporary perspective. Inspired by the literature and our own personal experiences, we suggest that the following non-cognitive traits are invaluable for radiology trainees: communication expertise, workplace adaptability, self-awareness, tech savvy and genuine interest in one's individual work and greater community. These characteristics should be highlighted by applicants, sought by selection committees, cultivated by mentors, evaluated by programs and valued by colleagues.

Using PACS for teaching radiology to undergraduate medical students.

BACKGROUND: Traditional radiology education for medical students predominantly uses textbooks, PowerPoint files, and hard-copy radiographic images, which often lack student interaction. PACS (Picture Archiving and Communication System) is a crucial tool for radiologists in viewing and reporting images, but its use in medical student training remains limited. OBJECTIVE: This study investigates the effectiveness of using PACS (Picture Archiving and Communication System) for teaching radiology to undergraduate medical students compared to traditional methods. METHODS: Fifty-three medical students were divided into a control group (25 students) receiving traditional slide-based training and an intervention group (28 students) using PACS software to view complete patient images. Pre- and post-course tests and satisfaction surveys were conducted for both groups, along with self-evaluation by the intervention group. The validity and reliability of the assessment tools were confirmed through expert review and pilot testing. RESULTS: No significant difference was found between the control and intervention groups regarding, gender, age, and GPA. Final multiple-choice test scores were similar (intervention: 10.89 ± 2.9; control: 10.76 ± 3.5; p = 0.883). However, the intervention group demonstrated significantly higher improvement in the short answer test for image interpretation (intervention: 8.8 ± 2.28; control: 5.35 ± 2.39; p = 0.001). Satisfaction with the learning method did not significantly differ between groups (intervention: 36.54 ± 5.87; control: 39.44 ± 7.76; p = 0.129). The intervention group reported high familiarity with PACS capabilities (75%), CT principles (71.4%), interpretation (64.3%), appropriate window selection (75%), and anatomical relationships (85.7%). CONCLUSION: PACS-based training enhances medical students' diagnostic and analytical skills in radiology. Further research with larger sample sizes and robust assessment methods is recommended to confirm and expand upon theses results.

Multidisciplinary approach to diagnostic radiology education: a novel educational intervention for Turkish medical students.

Teleconferencing can facilitate a multidisciplinary approach to teaching radiology to medical students. This study aimed to determine whether an online learning approach enables students to appreciate the interrelated roles of radiology and other specialties during the management of different medical cases. Turkish medical students attended five 60-90-minute online lectures delivered by radiologists and other specialists from the United States and Canada through Zoom meetings between November 2020 and January 2021. Student ambassadors from their respective Turkish medical schools recruited their classmates with guidance from the course director. Students took a pretest and posttest to assess the knowledge imparted from each session and a final course survey to assess their confidence in radiology and the value of the course. A paired t-test was used to assess pretest and posttest score differences. A 4-point Likert-type scale was used to assess confidence rating differences before and after attending the course sessions. A total of 1,458 Turkish medical students registered for the course. An average of 437 completed both pre- and posttests when accounting for all five sessions. Posttest scores were significantly higher than pretest scores for each session (P < 0.001). A total of 546 medical students completed the final course survey evaluation. Students' rating of their confidence in their radiology knowledge increased after taking the course (P < 0.001). Students who took our course gained an appreciation for the interrelated roles of different specialties in approaching medical diagnoses and interpreting radiological findings. These students also reported an increased confidence in radiology topics and rated the course highly relevant and insightful. Overall, our findings indicated that multidisciplinary online education can be feasibly implemented for medical students by video teleconferencing.