cirpublications.com-2vV0-1790880679-a513365208 1790880679 Clinical Intelligence Research Press info@cirpublications.com Clinical Intelligence Research Press Journal of Artificial Intelligence for Healthcare Systems J. Artif. Intell. Healthc. Syst. 3149-8981 2023 2 2 Jinwoo Park Minji Kim Seung Lee 2023 10.68159/a513365208 https://cirpublications.com/pub/journal/1/article/a513365208 Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115-8. 10.1038/nature21056 Tschandl P, Rosendahl C, Kittler H. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci Data. 2018;5(1):180161. 10.1038/sdata.2018.161 Haenssle HA, Fink C, Schneiderbauer R, Toberer F, Buhl T, Blum A, et al. Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann Oncol. 2018;29(8):1836-42. Wang Z, Zhang L, Shu X, Wang Y, Feng Y. Consistent representation via contrastive learning for skin lesion diagnosis. Comput Methods Programs Biomed. 2023;242:107826. 10.1016/j.cmpb.2023.107826 Du S, Hers B, Bayasi N, Hamarneh G, Garbi R. FairDisCo: fairer AI in dermatology via disentanglement contrastive learning. In: Vedaldi A, Bischof H, Brox T, Frahm JM, editors. Eur Conf Comput Vis. Cham: Springer Nature Switzerland; 2022. p. 185-202. 10.1007/978-3-031-19803-8_11 Hsu BW, Tseng VS. Hierarchy-aware contrastive learning with late fusion for skin lesion classification. Comput Methods Programs Biomed. 2022;216:106666. 10.1016/j.cmpb.2022.106666 Mahapatra D, Bozorgtabar B, Ge Z. Medical image classification using generalized zero shot learning. In: Proc IEEE/CVF Int Conf Comput Vis. 2021. p. 3344-53. 10.1109/ICCV48922.2021.00335 Radford A, Kim JW, Hallacy C, Ramesh A, Goh G, Agarwal S, et al. Learning transferable visual models from natural language supervision. In: Proc Int Conf Mach Learn. 2021. p. 8748-63. Wang Z, Wu Z, Agarwal D, Sun J. MedCLIP: contrastive learning from unpaired medical images and text. In: Proc Conf Empir Methods Nat Lang Process. 2022. p. 3876-87. Zhang Y, Jiang H, Miura Y, Manning CD, Langlotz CP. Contrastive learning of medical visual representations from paired images and text. In: Mach Learn Healthc Conf. 2022. p. 2-25. Huang SC, Shen L, Lungren MP, Yeung S. GLoRIA: a multimodal global-local representation learning framework for label-efficient medical image recognition. In: Proc IEEE/CVF Int Conf Comput Vis. 2021. p. 3942-51. 10.1109/ICCV48922.2021.00393 Park J, Choi K, Yoon B, Cho HG, Hwang B. RadZero3D: bridging self-supervised video models and medical vision-language alignment for zero-shot chest CT interpretation. In: Proc IEEE/CVF Int Conf Comput Vis. 2025. p. 6742-9. Codella NC, Gutman D, Celebi ME, Helba B, Marchetti MA, Dusza SW, et al. Skin lesion analysis toward melanoma detection: a challenge at the 2017 International Symposium on Biomedical Imaging, hosted by the International Skin Imaging Collaboration. In: IEEE Int Symp Biomed Imaging. 2018. p. 168-72. 10.1109/ISBI.2018.8363547 Combalia M, Codella NC, Rotemberg V, Helba B, Vilaplana V, Reiter O, et al. BCN20000: dermoscopic lesions in the wild. arXiv. 2019;arXiv:1908.02288. Albahar MA. Skin lesion classification using convolutional neural network with novel regularizer. IEEE Access. 2019;7:38306-13. 10.1109/ACCESS.2019.2906333 Liu J, Zhang Z, Razavian N. Deep EHR: chronic disease prediction using medical notes. In: Mach Learn Healthc Conf. 2018. p. 440-64. Mahbub M, Srinivasan S, Danciu I, Peluso A, Begoli E, Tamang S, et al. Unstructured clinical notes within the 24 hours since admission predict short, mid & long-term mortality in adult ICU patients. PLoS One. 2022;17(1):e0262182. 10.1371/journal.pone.0262182 Krishnan GS, Kamath SS. Hybrid text feature modeling for disease group prediction using unstructured physician notes. In: Int Conf Comput Sci. Cham: Springer International Publishing; 2020. p. 321-33. 10.1007/978-3-030-50420-5_24 Hashir M, Sawhney R. Towards unstructured mortality prediction with free-text clinical notes. J Biomed Inform. 2020;108:103489. 10.1016/j.jbi.2020.103489 Dwarakanath B, Latha M, Annamalai R, Jagadish SK. A novel feature selection with hybrid deep learning based heart disease detection and classification in the e-healthcare environment. Comput Intell Neurosci. 2022;2022:1167494. Wu Y, Zeng D, Wang Z, Sheng Y, Yang L, James AJ, et al. Federated contrastive learning for dermatological disease diagnosis via on-device learning. In: IEEE/ACM Int Conf Comput Aided Des. 2021. p. 1-7. 10.1109/ICCAD51958.2021.9643504 Zhang J, Xie Y, Xia Y, Shen C. Attention residual learning for skin lesion classification. IEEE Trans Med Imaging. 2019;38(9):2092-103. 10.1109/TMI.2019.2893944 Rahman Z, Hossain MS, Islam MR, Hasan MM, Hridhee RA. An approach for multiclass skin lesion classification based on ensemble learning. Inform Med Unlocked. 2021;25:100659. 10.1016/j.imu.2021.100659 Lim HW, Collins SA, Resneck JS Jr, Bolognia JL, Hodge JA, Rohrer TA, et al. The burden of skin disease in the United States. J Am Acad Dermatol. 2017;76(5):958-72. 10.1016/j.jaad.2016.12.043 Shi Y, Duan C, Chen S. Contrastive learning based intelligent skin lesion diagnosis in edge computing networks. In: IEEE Glob Commun Conf. 2021. p. 1-6. 10.1109/GLOBECOM46510.2021.9685359 Eicher L, Knop M, Aszodi N, Senner S, French LE, Wollenberg A. A systematic review of factors influencing treatment adherence in chronic inflammatory skin disease: strategies for optimizing treatment outcome. J Eur Acad Dermatol Venereol. 2019;33(12):2253-63. 10.1111/jdv.15814 Bi L, Feng DD, Fulham M, Kim J. Multi-label classification of multi-modality skin lesion via hyper-connected convolutional neural network. Pattern Recognit. 2020;107:107502. 10.1016/j.patcog.2020.107502 Du-Harpur X, Watt FM, Luscombe NM, Lynch MD. What is AI? Applications of artificial intelligence to dermatology. Br J Dermatol. 2020;183(3):423-30. 10.1111/bjd.18880