{
  "abstract": "Melanoma has a growing incidence, and early and accurate detection can enhance survival rates. However, its complex diagnoses pose challenges that could be overcome by Artificial Intelligence (AI), and the ABCD(E) rule (Asymmetry, Border irregularity, Color variegation, Diameter, and Evolution) is a major screening pattern enabling melanoma detection. This systematic review assessed advancements in Artificial Intelligence (AI) for melanoma diagnosis, particularly focusing on systems incorporating or informed by ABCD(E) criteria. Guided by Cochrane and PRISMA guidelines, this review included experimental or observational studies indexed on PubMed, LILACS, Latindex, and Cochrane Central Register of Controlled Trials platforms. Independent screening and data extraction were performed by two authors. 2,735 records were assessed and 21 studies were included in the final screening, analyzing over 90,000 images/cases. AI systems, notably CNNs and DCNNs, demonstrated promising diagnostic performance, often matching or exceeding human expert evaluation, achieving an Area Under the Curve (AUC) of 90.3% for early melanoma differentiation. Patient selection bias was observed, limiting applicability of the findings. AI shows significant and evolving capability in skin lesion diagnosis, increasingly integrating ABCD(E) criteria. While offering enhanced diagnostic consistency, challenges persist, including data limitations, model generalizability, and interpretability.\n\nSupplementary Information: The online version contains supplementary material available at https://doi.org/10.1007/s00432-026-06519-4.",
  "abstract_shown": true,
  "affiliations": [
    "Federal University of Minas Gerais, Av. Prof. Alfredo Balena, 190 - Santa Efigênia, Belo Horizonte, Minas Gerais 30130-100 Brazil",
    "Universidade José do Rosário Vellano, Belo Horizonte, Minas Gerais Brazil",
    "Instituto Nacional do Câncer, Rio de Janeiro, Brazil",
    "Faculdade de Ciências Médicas de Ipatinga, Ipatinga, Minas Gerais Brazil",
    "Department of Gynecology and Obstetrics, Federal University of Minas Gerais, Belo Horizonte, Brazil"
  ],
  "article_type": "review article",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Otto Muller Silva Lopes",
      "orcid": "0009-0005-9300-1487"
    },
    {
      "affs": [
        0
      ],
      "name": "Juliana Almeida Oliveira",
      "orcid": "0000-0002-4704-2318"
    },
    {
      "affs": [
        1
      ],
      "name": "Victor Brunno Alves Nogueira",
      "orcid": "0009-0000-7749-8781"
    },
    {
      "affs": [
        0
      ],
      "name": "Ludmila Fausto Ribeiro",
      "orcid": "0009-0009-6703-3775"
    },
    {
      "affs": [
        2
      ],
      "name": "Mara Inês Stefanini Zamae",
      "orcid": "0009-0009-2376-2618"
    },
    {
      "affs": [
        1
      ],
      "name": "Matheus Figueiredo Garrocho Ottoni Vieira",
      "orcid": "0009-0005-5685-8270"
    },
    {
      "affs": [
        3
      ],
      "name": "Vinicius Marciano Dantas e Pimenta",
      "orcid": "0009-0000-5865-2595"
    },
    {
      "affs": [
        4
      ],
      "name": "Zilma Silveira Nogueira Reis",
      "orcid": "0000-0001-6374-9295"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8kdjkk09ac4sn.bib",
  "cited_by": 0,
  "doi": "10.1007/s00432-026-06519-4",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13713948/",
  "id": "8kdjkk09ac4sn",
  "issns": [
    "0171-5216",
    "1432-1335"
  ],
  "landing_url": "https://doi.org/10.1007/s00432-026-06519-4",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Review"
    ],
    [
      "lcc",
      "RC254-282"
    ],
    [
      "openalex",
      "2730|Cutaneous Melanoma Detection and Management"
    ]
  ],
  "openalex": "W7171964898",
  "pdf_url": null,
  "pmcid": "PMC13713948",
  "pmid": "42946891",
  "published": "2026-07-01",
  "publisher": "Springer",
  "ris": "https://research.draconitic.com/paper/8kdjkk09ac4sn.ris",
  "sources": [
    {
      "first_seen": "2026-11-01T12:21:32Z",
      "native_id": "PMC13713948",
      "source": "pmc",
      "url": "https://doi.org/10.1007/s00432-026-06519-4"
    },
    {
      "first_seen": "2026-11-01T14:21:16Z",
      "native_id": "02e8b5566ba540fc9976e75ef4e58e4f",
      "source": "doaj",
      "url": "https://doi.org/10.1007/s00432-026-06519-4"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2730|Cutaneous Melanoma Detection and Management",
      "subfield": "oncology"
    }
  ],
  "title": "Artificial intelligence (AI) in melanocytic lesions diagnosis: a systematic review of methods incorporating ABCD(E)-derived features",
  "title_text": "Artificial intelligence (AI) in melanocytic lesions diagnosis: a systematic review of methods incorporating ABCD(E)-derived features",
  "type": "peer-reviewed",
  "updated_at": "2026-11-03T14:20:43Z",
  "url": "https://research.draconitic.com/paper/8kdjkk09ac4sn",
  "venue": "Journal of Cancer Research and Clinical Oncology",
  "versions": []
}