Accuracy of endobronchial ultrasound (EBUS) in the staging of lung cancer - A comparison of staging EBUS with postoperative pathological nodal staging.

BACKGROUND: Endobronchial ultrasound-guided transbronchial fine-needle aspiration (EBUS-TBNA) has replaced mediastinoscopy as the preferred investigation for evaluating mediastinum in staging lung cancer. There is little evidence of mediastinal staging by EBUS-TBNA from India. OBJECTIVES: To study endobronchial ultrasound's diagnostic accuracy in staging lung cancer. METHODOLOGY: We retrospectively analysed patients operated on for lung cancer where EBUS was performed preoperatively for mediastinal staging. We compared the histological findings obtained from different mediastinal lymph nodes (LNs) by EBUS-TBNA with the pathology of the same LNs obtained after surgical dissection as the reference standard. RESULTS: Seventy-six patients underwent curative surgery for lung cancer. The diagnostic accuracy, sensitivity, specificity, positive predictive value and negative predictive value of EBUS-TBNA in predicting mediastinal metastasis were 93.9%, 40%, 99%, 80% and 94.6%, respectively. Of the 115 LNs sampled, EBUS-TBNA was false negative in six nodes, resulting in an up-staging of six patients. CONCLUSIONS: EBUS-TBNA has a high diagnostic accuracy for lung cancer staging.

AI Education for Fourth-Year Medical Students: Two-Year Experience of a Web-Based, Self-Guided Curriculum and Mixed Methods Study.

BACKGROUND: Artificial intelligence (AI) and machine learning (ML) are poised to have a substantial impact in the health care space. While a plethora of web-based resources exist to teach programming skills and ML model development, there are few introductory curricula specifically tailored to medical students without a background in data science or programming. Programs that do exist are often restricted to a specific specialty. OBJECTIVE: We hypothesized that a 1-month elective for fourth-year medical students, composed of high-quality existing web-based resources and a project-based structure, would empower students to learn about the impact of AI and ML in their chosen specialty and begin contributing to innovation in their field of interest. This study aims to evaluate the success of this elective in improving self-reported confidence scores in AI and ML. The authors also share our curriculum with other educators who may be interested in its adoption. METHODS: This elective was offered in 2 tracks: technical (for students who were already competent programmers) and nontechnical (with no technical prerequisites, focusing on building a conceptual understanding of AI and ML). Students established a conceptual foundation of knowledge using curated web-based resources and relevant research papers, and were then tasked with completing 3 projects in their chosen specialty: a data set analysis, a literature review, and an AI project proposal. The project-based nature of the elective was designed to be self-guided and flexible to each student's interest area and career goals. Students' success was measured by self-reported confidence in AI and ML skills in pre and postsurveys. Qualitative feedback on students' experiences was also collected. RESULTS: This web-based, self-directed elective was offered on a pass-or-fail basis each month to fourth-year students at Emory University School of Medicine beginning in May 2021. As of June 2022, a total of 19 students had successfully completed the elective, representing a wide range of chosen specialties: diagnostic radiology (n=3), general surgery (n=1), internal medicine (n=5), neurology (n=2), obstetrics and gynecology (n=1), ophthalmology (n=1), orthopedic surgery (n=1), otolaryngology (n=2), pathology (n=2), and pediatrics (n=1). Students' self-reported confidence scores for AI and ML rose by 66% after this 1-month elective. In qualitative surveys, students overwhelmingly reported enthusiasm and satisfaction with the course and commented that the self-direction and flexibility and the project-based design of the course were essential. CONCLUSIONS: Course participants were successful in diving deep into applications of AI in their widely-ranging specialties, produced substantial project deliverables, and generally reported satisfaction with their elective experience. The authors are hopeful that a brief, 1-month investment in AI and ML education during medical school will empower this next generation of physicians to pave the way for AI and ML innovation in health care.