201 GENERAL DISCUSSION 52. Goyal M, Knackstedt T, Yan S, Hassanpour S. Artificial intelligence-based image classification methods for diagnosis of skin cancer: challenges and opportunities. Comput Biol Med 2020;127:104065. 53. Tschandl P, Rinner C, Apalla Z, Argenziano G, Codella N, Halpern A, et al. Human-computer collaboration for skin cancer recognition. Nat Med 2020;26(8):1229-1234. 54. Vodrahalli K, Daneshjou R, Novoa RA, Chiou A, Ko JM, Zou J. TrueImage: a machine learning algorithm to improve the quality of telehealth photos. Pac Symp Biocomput 2021;26:220-231. 55. Su MY, Trefrey BL, Smith GP, Das S. Online portal-based system for improving patientgenerated photographs for teledermatology. Dermatol Ther 2020;33(6):e14453. 56. Sangers TE, Wakkee M, Moolenburgh FJ, Nijsten T, Lugtenberg M. Towards successful implementation of artificial intelligence in skin cancer care: a qualitative study exploring the views of dermatologists and general practitioners. Arch Dermatol Res 2023;315(5):1187-1195. 57. Jain A, Way D, Gupta V, Gao Y, de Oliveira Marinho G, Hartford J, et al. Development and assessment of an artificial intelligence-based tool for skin condition diagnosis by primary care physicians and nurse practitioners in teledermatology practices. JAMA Netw Open 2021;4(4):e217249. 58. Freeman K, Dinnes J, Chuchu N, Takwoingi Y, Bayliss SE, Matin RN, et al. Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies. BMJ 2020;368:m127. 59. Escalé-Besa A, Yélamos O, Vidal-Alaball J, Fuster-Casanovas A, Miró Catalina Q, Börve A, et al. Exploring the potential of artificial intelligence in improving skin lesion diagnosis in primary care. Sci Rep 2023;13(1):4293. 60. Maier K, Zaniolo L, Marques O. Image quality issues in teledermatology: a comparative analysis of artificial intelligence solutions. J Am Acad Dermatol 2022;87(1):240-242. 61. Adamson AS, Smith A. Machine learning and health care disparities in dermatology. JAMA Dermatol 2018;154(11):1247-1248. 62. Ibrahim H, Liu X, Zariffa N, Morris AD, Denniston AK. Health data poverty: an assailable barrier to equitable digital health care. Lancet Digit Health 2021;3(4):e260-e265. 63. Dermoscopedia. URL: https://dermoscopedia.org/Main_Page [accessed 2023-09-23]. 64. The International Skin Imaging Collaboration Archive. URL: https://www.isic-archive.com/ [accessed 2023-09-23]. 65. Rotemberg V, Kurtansky N, Betz-Stablein B, Caffery L, Chousakos E, Codella N, et al. A patientcentric dataset of images and metadata for identifying melanomas using clinical context. Sci Data 2021;8(1):34. 66. Daneshjou R, Barata C, Betz-Stablein B, Celebi ME, Codella N, Combalia M, et al. Checklist for evaluation of image-based artificial intelligence reports in dermatology: CLEAR derm consensus guidelines from the international skin imaging collaboration artificial intelligence working group. JAMA Dermatol 2022;158(1),90-96. 67. Sallam M. ChatGPT utility in healthcare education, research, and practice: systematic review on the promising perspectives and valid concerns. Healthcare (Basel) 2023;11(6):887. 68. Vaishya R, Misra A, Vaish A. ChatGPT: Is this version good for healthcare and research? Diabetes Metab Syndr 2023;17(4):102744. 69. Koninklijke Nederlandse Maatschappij tot bevordering der Geneeskunst (KNMG). The roles of doctors in 2040: core values and relationship with society, 2022. URL: https://data.maglr.com/3230/ issues/28816/377176/downloads/the_roles_of_doctors_in_2040.pdf [accessed 2023-09-23]. 8
RkJQdWJsaXNoZXIy MTk4NDMw