FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4383499360> ?p ?o ?g. }

• W4383499360 endingPage "e25" @default.
• W4383499360 startingPage "e25" @default.
• W4383499360 abstract "Abstract As a visual-based specialty, dermatology has the opportunity to use artificial intelligence (AI) to aid diagnosis and guide precision management of skin diseases. To employ effective and safe AI, algorithms should be trained and validated on data sets that are representative of patient demographics in clinical practice. In our recent systematic review of AI image analysis studies in inflammatory skin conditions, ethnicity and Fitzpatrick skin type were reported in only four (6%) and 10 (16%) of 64 included studies, respectively. A systematic review of skin cancer image data sets similarly demonstrated poor ethnicity and Fitzpatrick skin type reporting, and under-representation of darker skin types (Wen D, Khan SM, Xu AJ et al. Characteristics of publicly available skin cancer image datasets: a systematic review. Lancet Digit Health 2022;4:e64–e74). Such bias in image acquisition limits the accuracy of AI tools when applied to real-world populations. We aimed to assess the representation of ethnic groups in our AI research and understand barriers to participation. We established a prospective observational cohort study in a national specialized psoriasis service based in South London, which seeks to develop AI algorithms for image-based assessment of psoriasis severity. Participants have photographs of their skin taken (both self-taken on participants’ own devices and professionally acquired in a studio) for AI image analysis. Images are securely stored in an ethically-approved research database in accordance with UK General Data Protection Regulations. Willingness to participate and reasons for declining were recorded. Of 557 patients approached between September 2021 and December 2022, 151 (27.1%) declined to participate in the AI study. The mean (SD) age of those willing and declining to participate was 47.4 (16.6) and 50.7 (16.2) years, respectively. Of those willing to participate, 57.5% were male vs. 53.6% who declined. Twenty-five per cent (n = 77/308) of individuals of white ethnicity declined to participate vs. 32% (n = 20/63) Indian/Pakistan/Bangladeshi, 33% (n = 6/18) black/black British, 50% (n = 5/10) Chinese/Japanese/Korean/Indochinese and 50% (1/2) of mixed ethnicity. Common reasons for declining participation included a disinclination to have photos taken, psoriasis affecting intimate sites, time constraints, medical reasons (e.g. poor mobility) and concerns regarding data security. Ninety-eight of 406 individuals who were willing to participate met the study-specific inclusion criteria (e.g. plaque psoriasis, Psoriasis Area and Severity Index &gt; 3) and were recruited. Of those recruited, 82% (n = 80) were of white ethnicity, 9% (n = 9) Indian/Pakistan/Bangladeshi and 8% (n = 8) were black/black British. Seventeen (17%) had Fitzpatrick skin type V/VI. These data highlight ethnic inequalities in participation of AI image-analysis research. Further dissection of barriers to participation may inform strategies for ensuring more diverse and representative datasets, to maximize the potential of AI in healthcare." @default.
• W4383499360 created "2023-07-08" @default.
• W4383499360 creator A5005311425 @default.
• W4383499360 creator A5014689286 @default.
• W4383499360 creator A5027004576 @default.
• W4383499360 creator A5028630228 @default.
• W4383499360 creator A5047924483 @default.
• W4383499360 creator A5051813954 @default.
• W4383499360 creator A5057058692 @default.
• W4383499360 creator A5059805150 @default.
• W4383499360 creator A5062605699 @default.
• W4383499360 creator A5070206339 @default.
• W4383499360 creator A5070521872 @default.
• W4383499360 creator A5071224282 @default.
• W4383499360 creator A5076366108 @default.
• W4383499360 creator A5084513261 @default.
• W4383499360 creator A5088410595 @default.
• W4383499360 creator A5092263545 @default.
• W4383499360 date "2023-07-01" @default.
• W4383499360 modified "2023-09-28" @default.
• W4383499360 title "P28 Understanding ethnic inequality and barriers to participation in artificial intelligence (AI) image analysis research in dermatology" @default.
• W4383499360 doi "https://doi.org/10.1093/bjd/ljad174.049" @default.
• W4383499360 hasPublicationYear "2023" @default.
• W4383499360 type Work @default.
• W4383499360 citedByCount "0" @default.
• W4383499360 crossrefType "journal-article" @default.
• W4383499360 hasAuthorship W4383499360A5005311425 @default.
• W4383499360 hasAuthorship W4383499360A5014689286 @default.
• W4383499360 hasAuthorship W4383499360A5027004576 @default.
• W4383499360 hasAuthorship W4383499360A5028630228 @default.
• W4383499360 hasAuthorship W4383499360A5047924483 @default.
• W4383499360 hasAuthorship W4383499360A5051813954 @default.
• W4383499360 hasAuthorship W4383499360A5057058692 @default.
• W4383499360 hasAuthorship W4383499360A5059805150 @default.
• W4383499360 hasAuthorship W4383499360A5062605699 @default.
• W4383499360 hasAuthorship W4383499360A5070206339 @default.
• W4383499360 hasAuthorship W4383499360A5070521872 @default.
• W4383499360 hasAuthorship W4383499360A5071224282 @default.
• W4383499360 hasAuthorship W4383499360A5076366108 @default.
• W4383499360 hasAuthorship W4383499360A5084513261 @default.
• W4383499360 hasAuthorship W4383499360A5088410595 @default.
• W4383499360 hasAuthorship W4383499360A5092263545 @default.
• W4383499360 hasConcept C121608353 @default.
• W4383499360 hasConcept C126322002 @default.
• W4383499360 hasConcept C137403100 @default.
• W4383499360 hasConcept C142724271 @default.
• W4383499360 hasConcept C144024400 @default.
• W4383499360 hasConcept C154945302 @default.
• W4383499360 hasConcept C16005928 @default.
• W4383499360 hasConcept C19165224 @default.
• W4383499360 hasConcept C20387591 @default.
• W4383499360 hasConcept C23131810 @default.
• W4383499360 hasConcept C2777789703 @default.
• W4383499360 hasConcept C41008148 @default.
• W4383499360 hasConcept C512399662 @default.
• W4383499360 hasConcept C71924100 @default.
• W4383499360 hasConceptScore W4383499360C121608353 @default.
• W4383499360 hasConceptScore W4383499360C126322002 @default.
• W4383499360 hasConceptScore W4383499360C137403100 @default.
• W4383499360 hasConceptScore W4383499360C142724271 @default.
• W4383499360 hasConceptScore W4383499360C144024400 @default.
• W4383499360 hasConceptScore W4383499360C154945302 @default.
• W4383499360 hasConceptScore W4383499360C16005928 @default.
• W4383499360 hasConceptScore W4383499360C19165224 @default.
• W4383499360 hasConceptScore W4383499360C20387591 @default.
• W4383499360 hasConceptScore W4383499360C23131810 @default.
• W4383499360 hasConceptScore W4383499360C2777789703 @default.
• W4383499360 hasConceptScore W4383499360C41008148 @default.
• W4383499360 hasConceptScore W4383499360C512399662 @default.
• W4383499360 hasConceptScore W4383499360C71924100 @default.
• W4383499360 hasIssue "1" @default.
• W4383499360 hasLocation W43834993601 @default.
• W4383499360 hasOpenAccess W4383499360 @default.
• W4383499360 hasPrimaryLocation W43834993601 @default.
• W4383499360 hasRelatedWork W1636503868 @default.
• W4383499360 hasRelatedWork W2036533781 @default.
• W4383499360 hasRelatedWork W2588713752 @default.
• W4383499360 hasRelatedWork W2596357561 @default.
• W4383499360 hasRelatedWork W2765980456 @default.
• W4383499360 hasRelatedWork W2957185431 @default.
• W4383499360 hasRelatedWork W2976382042 @default.
• W4383499360 hasRelatedWork W3033540421 @default.
• W4383499360 hasRelatedWork W4210400521 @default.
• W4383499360 hasRelatedWork W4297184974 @default.
• W4383499360 hasVolume "189" @default.
• W4383499360 isParatext "false" @default.
• W4383499360 isRetracted "false" @default.
• W4383499360 workType "article" @default.