Park CW, et al. J Korean Med Sci. 2020 Oct;35(42):e379. https://doi.org/10.3346/jkms.2020.35.e379

Purpose: Given the increasing significance and potential impact of artificial intelligence (AI) technology on health care delivery, there is an increasing demand to integrate AI into medical school curricula. This study aimed to define medical AI competencies and identify the essential competencies for medical graduates in South Korea. Method: An initial Delphi survey conducted in 2022 involving 4 groups of medical AI experts (n = 28) yielded 42 competency items. Subsequently, an online questionnaire survey was carried out with 1,955 participants (1,174 students and 781 professors) from medical schools across South Korea, utilizing the list of 42 competencies developed from the first Delphi round. A subsequent Delphi survey was conducted with 33 medical educators from 21 medical schools to differentiate the essential AI competencies from the optional ones. Results: The study identified 6 domains encompassing 36 AI competencies essential for medical graduates: (1) understanding digital health and changes driven by AI; (2) fundamental knowledge and skills in medical AI; (3) ethics and legal aspects in the use of medical AI; (4) medical AI application in clinical practice; (5) processing, analyzing, and evaluating medical data; and (6) research and development of medical AI, as well as subcompetencies within each domain. While numerous competencies within the first 4 domains were deemed essential, a higher percentage of experts indicated responses in the last 2 domains, data science and medical AI research and development, were optional. Conclusions: This medical AI framework of 6 competencies and their subcompetencies for medical graduates exhibits promising potential for guiding the integration of AI into medical curricula. Further studies conducted in diverse contexts and countries are necessary to validate and confirm the applicability of these findings. Additional research is imperative for developing specific and feasible educational models to integrate these proposed competencies into pre-existing curricula.

Paik ES, et al. J Gynecol Oncol. 2018 Aug;30:e65. https://doi.org/10.3802/jgo.2019.30.e65

Purpose: Early detection and classification of bone tumors in the proximal femur is crucial for their successful treatment. This study aimed to develop an artificial intelligence (AI) model to classify bone tumors in the proximal femur on plain radiographs. Methods: Standard anteroposterior hip radiographs were obtained from a single tertiary referral center. A total of 538 femoral images were set for the AI model training, including 94 with malignant, 120 with benign, and 324 without tumors. The image data were pre-processed to be optimized for training of the deep learning model. The state-of-the-art convolutional neural network (CNN) algorithms were applied to pre-processed images to perform three-label classification (benign, malignant, or no tumor) on each femur. The performance of the CNN model was verified using fivefold cross-validation and was compared against that of four human doctors. Results: The area under the receiver operating characteristic (AUROC) of the best performing CNN model for the three-label classification was 0.953 (95% confidence interval, 0.926–0.980). The diagnostic accuracy of the model (0.853) was significantly higher than that of the four doctors (0.794) (P = 0.001) and also that of each doctor individually (0.811, 0.796, 0.757, and 0.814, respectively) (P<0.05). The mean sensitivity, specificity, precision, and F1 score of the CNN models were 0.822, 0.912, 0.829, and 0.822, respectively, whereas the mean values of four doctors were 0.751, 0.889, 0.762, and 0.797, respectively. Conclusions: The AI-based model demonstrated high performance in classifying the presence of bone tumors in the proximal femur on plain radiographs. Our findings suggest that AI-based technology can potentially reduce the misdiagnosis of doctors who are not specialists in musculoskeletal oncology.

Background: High expression of P-glycoprotein is one of the well-known mechanisms of chemoresistance in chondrosarcomas. However, the role of antiapoptotic proteins, a common mechanism responsible for chemoresistance in other tumors, has not been well studied in chondrosarcomas. We examined the importance of P-glycoprotein and antiapoptotic proteins in the chemoresistance to doxorubicin of two Grade II chondrosarcoma cell lines, JJ012 and SW1353.Results: We confirmed that both chondrosarcoma cell types expressed P-glycoprotein and antiapoptotic proteins (Bcl-2, Bcl-xL and XIAP). siRNA knockdown as well as pharmacologic inhibitors of cell survival proteins (Bcl-2, Bcl-xL and XIAP) enhanced apoptosis of chemoresistant chondrosarcoma cells by up to 5.5 fold at 0.1 μmol and 5.5 fold at 1 μmol doxorubicin. These chemosensitizing effects were comparable to those of P-glycoprotein inhibition by siRNA or pharmacologic inhibitor.Conclusion: These findings suggest that antiapoptotic proteins play a significant role in the chemoresistance of chondrosarcoma cells independent of P-glycoprotein. Based on the results, a new siRNA-based therapeutic strategy targeting antiapoptotic genes can be designed to overcome the chemoresistance of chondrosarcomas which is often conferred by P-glycoprotein.