Thomsen K, Iversen L, Titlestad TL, Winther O. “Systematic review of machine learning for diagnosis and prognosis in dermatology”, (in eng). J Dermatolog Treat. 2020;31(5):496–510. https://doi.org/10.1080/09546634.2019.1682500.

Article  PubMed  Google Scholar 

Char DS, Shah NH, Magnus D. “Implementing machine learning in health care - addressing ethical challenges,” (in eng). N Engl J Med. 2018;378(11):981–3. https://doi.org/10.1056/NEJMp1714229.

Article  PubMed  PubMed Central  Google Scholar 

Aminizadeh S, et al. “Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare service”, (in eng). Artif Intell Med. 2024;149: 102779. https://doi.org/10.1016/j.artmed.2024.102779.

Article  PubMed  Google Scholar 

Zhao S, et al. “Smart identification of psoriasis by images using convolutional neural networks: a case study in China”, (in eng). J Eur Acad Dermatol Venereol. 2020;34(3):518–24. https://doi.org/10.1111/jdv.15965.

Article  CAS  PubMed  Google Scholar 

Yang Y, et al. “A convolutional neural network trained with dermoscopic images of psoriasis performed on par with 230 dermatologists”, (in eng). Comput Biol Med. 2021;139: 104924. https://doi.org/10.1016/j.compbiomed.2021.104924.

Article  PubMed  Google Scholar 

Yu Z, Kaizhi S, Jianwen H, Guanyu Y, Yonggang W. A deep learning-based approach toward differentiating scalp psoriasis and seborrheic dermatitis from dermoscopic images, (in eng). Front Med (Lausanne). 2022;9:965423. https://doi.org/10.3389/fmed.2022.965423.

Dash M, Londhe ND, Ghosh S, Shrivastava VK, Sonawane RS. “Swarm intelligence based clustering technique for automated lesion detection and diagnosis of psoriasis”, (in eng). Comput Biol Chem. 2020;86: 107247. https://doi.org/10.1016/j.compbiolchem.2020.107247.

Article  CAS  PubMed  Google Scholar 

Strober B, et al. “Recategorization of psoriasis severity: delphi consensus from the international psoriasis council,” (in eng). J Am Acad Dermatol. 2020;82(1):117–22. https://doi.org/10.1016/j.jaad.2019.08.026.

Article  PubMed  Google Scholar 

Wu AG, Conway J, Barazani L, Roy B, Cline A, Pereira F. “Is clear always clear? comparison of psoriasis area and severity index (pasi) and the physician’s global assessment (PGA) in psoriasis clearance,” (in eng). Dermatol Ther (Heidelb). 2020;10(5):1155–63. https://doi.org/10.1007/s13555-020-00435-2.

Article  PubMed  Google Scholar 

Raina A, et al. “Objective measurement of erythema in psoriasis using digital color photography with color calibration”, (in eng). Skin Res Technol. 2016;22(3):375–80. https://doi.org/10.1111/srt.12276.

Article  CAS  PubMed  Google Scholar 

Fink C, Alt C, Uhlmann L, Klose C, Enk A, Haenssle HA. “Precision and reproducibility of automated computer-guided Psoriasis Area and Severity Index measurements in comparison with trained physicians”, (in eng). Br J Dermatol. 2019;180(2):390–6. https://doi.org/10.1111/bjd.17200.

Article  CAS  PubMed  Google Scholar 

Meienberger N, et al. “Observer-independent assessment of psoriasis-affected area using machine learning”, (in eng). J Eur Acad Dermatol Venereol. 2020;34(6):1362–8. https://doi.org/10.1111/jdv.16002.

Article  CAS  PubMed  Google Scholar 

Huang K, et al. Artificial intelligence-based psoriasis severity assessment: real-world study and application (in eng). J Med Internet Res. 2023;25:e44932. https://doi.org/10.2196/44932.

Okamoto T, Kawai M, Ogawa Y, Shimada S, Kawamura T. “Artificial intelligence for the automated single-shot assessment of psoriasis severity”, (in eng). J Eur Acad Dermatol Venereol. 2022;36(12):2512–5. https://doi.org/10.1111/jdv.18354.

Article  CAS  PubMed  Google Scholar 

Aggarwal P, et al. “Clinical characteristics and disease burden in prurigo nodularis”, (in eng). Clin Exp Dermatol. 2021;46(7):1277–84. https://doi.org/10.1111/ced.14722.

Article  CAS  PubMed  Google Scholar 

Folle L, et al. "DeepNAPSI multi-reader nail psoriasis prediction using deep learning", (in eng). Sci Rep. 2023;13(1):5329. https://doi.org/10.1038/s41598-023-32440-8.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Paik K, Kim BR, Youn SW. “Evaluation of the area subscore of the palmoplantar pustulosis area and severity index using an attention U-net deep learning algorithm,” (in eng). J Dermatol. 2023;50(6):787–92. https://doi.org/10.1111/1346-8138.16752.

Article  PubMed  Google Scholar 

Liu Z, Wang X, Ma Y, Lin Y, Wang G. “Artificial intelligence in psoriasis: Where we are and where we are going”, (in eng). Exp Dermatol. 2023;32(11):1884–99. https://doi.org/10.1111/exd.14938.

Article  PubMed  Google Scholar 

Hong J, Mosca M, Hadeler E, Hakimi M, Bhutani T, Liao W. The future of personalized medicine in psoriasis. Dermatol Rev. 2021;2:282–8. https://doi.org/10.1002/der2.87.

Article  Google Scholar 

Yao P, et al. “Identification of ADAM23 as a potential signature for psoriasis using integrative machine-learning and experimental verification,” (in eng). Int J Gen Med. 2023;16:6051–64. https://doi.org/10.2147/IJGM.S441262.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Deng J, et al. “Multi-omics approach identifies PI3 as a biomarker for disease severity and hyper-keratinization in psoriasis”, (in eng). J Dermatol Sci. 2023;111(3):101–8. https://doi.org/10.1016/j.jdermsci.2023.07.005.

Article  CAS  PubMed  Google Scholar 

Xing L, et al. “Exploration of biomarkers of psoriasis through combined multiomics analysis,” (in eng). Mediators Inflamm. 2022;2022:7731082. https://doi.org/10.1155/2022/7731082.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Liu Y, Cui S, Sun J, Yan X, Han D.  Machine learning analysis of human skin by optoacoustic mesoscopy for automated extraction of psoriasis and aging biomarkers. IEEE Trans Med Imag 2024. https://doi.org/10.1109/TMI.2024.3356180. Epub ahead of print. PMID: 38241120.

Song JK, et al. “Classification and biomarker gene selection of pyroptosis-related gene expression in psoriasis using a random forest algorithm”, (in eng). Front Genet. 2022;13: 850108. https://doi.org/10.3389/fgene.2022.850108.

Article  CAS  PubMed  PubMed Central  Google Scholar 

He H. et al. Machine learning analysis of human skin by optoacoustic mesoscopy for automated extraction of psoriasis and aging biomarkers (in eng). IEEE Trans Med Imag. 2024. https://doi.org/10.1109/TMI.2024.3356180.

Choksi H, et al. Identifying serum metabolomic markers associated with skin disease activity in patients with psoriatic arthritis (in eng), Int J Mol Sci. 2023;24(20). https://doi.org/10.3390/ijms242015299.

Koussiouris J, Looby N, Kotlyar M, Kulasingam V, Jurisica I, Chandran V. Classifying patients with psoriatic arthritis according to their disease activity status using serum metabolites and machine learning (in eng). Metabolomics. 2024;20(1):17. https://doi.org/10.1007/s11306-023-02079-7.

Liu J, et al. Combined single cell transcriptome and surface epitope profiling identifies potential biomarkers of psoriatic arthritis and facilitates diagnosis (in eng). Front Immunol. 2022;13.

Narbutt J, et al. “A priori estimation of the narrow-band UVB phototherapy outcome for moderate-to-severe psoriasis based on the patients’ questionnaire and blood tests using random forest classifier”, (in eng). Clin Cosmet Investig Dermatol. 2021;14:253–9. https://doi.org/10.2147/CCID.S296604.

Article  PubMed  PubMed Central  Google Scholar 

Bhutani T, Liao W. “A practical approach to home UVB phototherapy for the treatment of generalized psoriasis", (in eng). Pract Dermatol. 2010;7(2):31–5.

PubMed  PubMed Central  Google Scholar 

Foulkes AC, et al. “A framework for multi-omic prediction of treatment response to biologic therapy for psoriasis,” (in eng). J Invest Dermatol. 2019;139(1):100–7. https://doi.org/10.1016/j.jid.2018.04.041.

Article  CAS  PubMed  Google Scholar 

Correa da Rosa J, Kim J, Tian S, Tomalin LE, Krueger JG, Suárez-Fariñas M. “Shrinking the psoriasis assessment gap: early gene-expression profiling accurately predicts response to long-term treatment”, (in eng). J Invest Dermatol. 2017;137(2):305–12. https://doi.org/10.1016/j.jid.2016.09.015.

Article  CAS  PubMed  Google Scholar 

Tomalin LE, et al. “Early quantification of systemic inflammatory proteins predicts long-term treatment response to tofacitinib and etanercept”, (in eng). J Invest Dermatol. 2020;140(5):1026–34. https://doi.org/10.1016/j.jid.2019.09.023.

Article  CAS  PubMed  Google Scholar 

Bagel J, Wang Y, Montgomery P III, Abaya C, Andrade E, Boyce C, Tomich T, Lee B-I, Pariser D, Menter A, Dickerson T. A machine learning-based test for predicting response to psoriasis biologics. Skin J Cutan Med. 2021;5(6):621–38. https://doi.org/10.25251/skin.5.6.5