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Research Articles

Exploring the feasibility of an artificial intelligence based clinical decision support system for cutaneous melanoma detection in primary care – a mixed method study

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Pages 51-60 | Received 28 Sep 2022, Accepted 08 Nov 2023, Published online: 20 Nov 2023

References

  • Jones O, Ranmuthu CKI, Hall P, et al. Recognising skin cancer in primary care. Adv Ther. 2020;37(1):603–616. doi: 10.6084/m9.figshare.9976475.
  • Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394–424. doi: 10.3322/CAAC.21492.
  • Erdei E, Torres SM. A new understanding in the epidemiology of melanoma. Expert Rev Anticancer Ther. 2010;10(11):1811–1823. doi: 10.1586/era.10.170.
  • Martens MC, Seebode C, Lehmann J, et al. Photocarcinogenesis and skin cancer prevention strategies: an update. Anticancer Res. 2018;38(2):1153–1158. doi: 10.21873/ANTICANRES.12334.
  • Guy GP, Ekwueme DU, Tangka FK, et al. Melanoma treatment costs: a systematic review of the literature, 1990–2011. Am J Prev Med. 2012;43(5):537–545. doi: 10.1016/J.AMEPRE.2012.07.031.
  • Vashist S, Schneider E, Luong J. Commercial smartphone-based devices and smart applications for personalized healthcare monitoring and management. Diagnostics (Basel). 2014;4(3):104–128. doi: 10.3390/DIAGNOSTICS4030104.
  • Blease C, Kaptchuk TJ, Bernstein MH, et al. Artificial intelligence and the future of primary care: exploratory qualitative study of UK general practitioners’ views. J Med Internet Res. 2019;21(3):e12802. doi: 10.2196/12802.
  • Wang F, Preininger A. AI in health: state of the art, challenges, and future directions. Yearb Med Inform. 2019;28(1):16–26. doi: 10.1055/S-0039-1677908.
  • Menvielle L, Audrain-Pontevia AF, Menvielle W. The digitization of healthcare: new challenges and opportunities. Palgrave Macmillan London (Book Publisher), 1st Edition. 2017:1–454. doi: 10.1057/978-1-349-95173-4/COVER.
  • Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44–56. doi: 10.1038/S41591-018-0300-7.
  • apão, L.V. (2019). The Future of Healthcare: The Impact of Digitalization on Healthcare Services Performance. In: Pereira Neto, A., Flynn, M. (eds) The Internet and Health in Brazil. Springer, Cham. doi: 10.1007/978-3-319-99289-1_22/COVER/.
  • Frisinger A, Papachristou P. The voice of healthcare: introducing digital decision support systems into clinical practice - a qualitative study. BMC Prim Care. 2023;24(1):67. doi: 10.1186/S12875-023-02024-6/TABLES/1.
  • Shinners L, Aggar C, Grace S, et al. Exploring healthcare professionals’ understanding and experiences of artificial intelligence technology use in the delivery of healthcare: an integrative review. Health Informatics J. 2020;26(2):1225–1236. doi: 10.1177/1460458219874641.
  • Watson HA, Tribe RM, Shennan AH. The role of medical smartphone apps in clinical decision-support: a literature review. Artif Intell Med. 2019;100:101707. doi: 10.1016/J.ARTMED.2019.101707.
  • Adamidi ES, Mitsis K, Nikita KS. Artificial intelligence in clinical care amidst COVID-19 pandemic: a systematic review. Comput Struct Biotechnol J. 2021;19:2833–2850. doi: 10.1016/j.csbj.2021.05.010.
  • Nasr-Esfahani E, Samavi S, Karimi N, et al. Melanoma detection by analysis of clinical images using convolutional neural network. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE; 2016. p. 1373–1376. Available from: http://ieeexplore.ieee.org/document/7590963/. doi: 10.1109/EMBC.2016.7590963.
  • Baig R, Bibi M, Hamid A, et al. Deep learning approaches towards skin lesion segmentation and classification from dermoscopic images - A review. Curr Med Imaging. 2020;16(5):513–533. doi: 10.2174/1573405615666190129120449.
  • Dick V, Sinz C, Mittlböck M, et al. Accuracy of computer-aided diagnosis of melanoma: a meta-analysis. JAMA Dermatol. 2019;155(11):1291–1299. doi: 10.1001/JAMADERMATOL.2019.1375.
  • Yu K-H, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018;2(10):719–731. doi: 10.1038/s41551-018-0305-z.
  • Haenssle HA, Fink C, Schneiderbauer R, 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):1836–1842. doi: 10.1093/annonc/mdy166.
  • Fink C, Blum A, Buhl T, et al. Diagnostic performance of a deep learning convolutional neural network in the differentiation of combined naevi and melanomas. J Eur Acad Dermatol Venereol. 2020;34(6):1355–1361. doi: 10.1111/jdv.16165.
  • Polesie S, Gillstedt M, Kittler H, et al. Assessment of melanoma thickness based on dermoscopy images: an open, web-based, international, diagnostic study. J Eur Acad Dermatol Venereol. 2022;36(11):2002–2007. doi: 10.1111/JDV.18436.
  • Gillstedt M, Mannius L, Paoli J, et al. Evaluation of melanoma thickness with clinical close-up and dermoscopic images using a convolutional neural network. Acta Derm Venereol. 2022;102:adv00790. doi: 10.2340/ACTADV.V102.2681.
  • Winkler JK, Sies K, Fink C, et al. Melanoma recognition by a deep learning convolutional neural network—performance in different melanoma subtypes and localisations. Eur J Cancer. 2020;127:21–29. doi: 10.1016/J.EJCA.2019.11.020.
  • Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3(2):77–101. doi: 10.1191/1478088706qp063oa.
  • Proudfoot K. Inductive/deductive hybrid thematic analysis in mixed methods research. J Mix Methods Res. 2022;2023:308–326. doi: 10.1177/15586898221126816/FORMAT/EPUB.
  • Fereday J, Muir-Cochrane E. Demonstrating rigor using thematic analysis: a hybrid approach of inductive and deductive coding and theme development. Int J Qual Methods. 2006;5(1):80–92. doi: 10.1177/160940690600500107.
  • The ISIC. Challenge Dataset | ISIC 2020 challenge dataset [Internet]; 2020. [cited 2021 May 19]. Available from: https://challenge2020.isic-archive.com/.
  • Wiklund PE, Me Kendler J, Strochlic AY, et al. Usability testing of medical devices. Boca Raton: CRC Press; 2010. doi: 10.1201/b19082.
  • Richardson S, Mishuris R, O’Connell A, et al. “Think aloud” and “near live” usability testing of two complex clinical decision support tools. Int J Med Inform. 2017;106:1–8. doi: 10.1016/J.IJMEDINF.2017.06.003.
  • Li AC, Kannry JL, Kushniruk A, et al. Integrating usability testing and think-aloud protocol analysis with “near-live” clinical simulations in evaluating clinical decision support. Int J Med Inform. 2012;81(11):761–772. doi: 10.1016/j.ijmedinf.2012.02.009.
  • Nowell LS, Norris JM, White DE, et al. Thematic analysis. Int J Qual Methods. 2017;16(1):160940691773384. doi: 10.1177/1609406917733847.
  • Morse JM. Confusing categories and themes. Qual Health Res. 2008;18(6):727–728. doi: 10.1177/1049732308314930.
  • Nguyen KA, Patel H, Haggstrom DA, et al. Utilizing a user-centered approach to develop and assess pharmacogenomic clinical decision support for thiopurine methyltransferase. BMC Med Inform Decis Mak. 2019;19(1):194. doi: 10.1186/S12911-019-0919-4/TABLES/4.
  • Peres SC, Pham T, Phillips R. Validation of the system usability scale (SUS): SUS in the wild. 2013;192–196. doi: 10.1177/1541931213571043.
  • Lewis JR, Sauro J. The factor structure of the system usability scale. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2009;5619 LNCS:94–103. doi: 10.1007/978-3-642-02806-9_12/COVER/.
  • World Medical Association. Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA. 2013;310(20):2191–2194. doi: 10.1001/jama.2013.281053.
  • Ministry of Education. Swedish Ethical Review Act (2003:460) [Internet]; 2023. [cited 2022 Apr 10]. Available from: https://www.riksdagen.se/sv/dokument-lagar/dokument/svensk-forfattningssamling/lag-2003460-om-etikprovning-av-forskning-som_sfs-2003-460.
  • Tong A, Sainsbury P, Craig J. Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups. Int J Qual Health Care. 2007;19(6):349–357. doi: 10.1093/intqhc/mzm042.
  • Bangor A, Kortum P, Miller J. Determining what individual SUS scores mean: adding an adjective rating scale. J Usability Stud. 2009;4:114–123.
  • Kamradt M, Poß-Doering R, Szecsenyi J. Exploring physician perspectives on using real-world care data for the development of artificial intelligence–based technologies in health care: qualitative study. JMIR Form Res. 2022;6(5):e35367. https://formative.jmir.org/2022/5/e35367. 2022;6:e35367. doi: 10.2196/35367.
  • Petkus H, Hoogewerf J, Wyatt JC. What do senior physicians think about AI and clinical decision support systems: quantitative and qualitative analysis of data from specialty societies. Clin Med (Lond). 2020;20(3):324–328. doi: 10.7861/CLINMED.2019-0317.
  • Lim K, Neal-Smith G, Mitchell C, et al. Perceptions of the use of artificial intelligence in the diagnosis of skin cancer: an outpatient survey. Clin Exp Dermatol. 2022;47(3):542–546. doi: 10.1111/CED.14969.
  • Nelson CA, Pérez-Chada LM, Creadore A, et al. Patient perspectives on the use of artificial intelligence for skin cancer screening: a qualitative study. JAMA Dermatol. 2020;156(5):501–512. doi: 10.1001/JAMADERMATOL.2019.5014.
  • Buck C, Doctor E, Hennrich J, et al. General practitioners’ attitudes toward artificial intelligence-enabled systems: interview study. J Med Internet Res. 2022;24(1):e28916. doi: 10.2196/28916.
  • Keskinbora KH. Medical ethics considerations on artificial intelligence. J Clin Neurosci. 2019;64:277–282. doi: 10.1016/J.JOCN.2019.03.001.