Evidence map›Paper›PMID 39061614›Full record

ReviewDiagnostics (Basel, Switzerland)2024

The Role of Artificial Intelligence in Early Diagnosis and Molecular Classification of Head and Neck Skin Cancers: A Multidisciplinary Approach.

Zeliha Merve Semerci, Havva Serap Toru, Esra Çobankent Aytekin, Hümeyra Tercanlı, Diana Maria Chiorean, Yalçın Albayrak, Ovidiu Simion Cotoi

Abstract readReview
In one paragraph

Review in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Zeliha Merve SemerciDepartment of Oral and Maxillofacial Radiology, Faculty of Dentistry, Akdeniz University, 07070 Antalya, Turkey.ORCID 0000-0002-0323-4940
Havva Serap ToruDepartment of Pathology, Faculty of Medicine, Akdeniz University, 07070 Antalya, Turkey.ORCID 0000-0002-4438-0434
Esra Çobankent AytekinDepartment of Pathology, Konya Numune Hospital, 42060 Konya, Turkey.
Hümeyra TercanlıDepartment of Oral and Maxillofacial Radiology, Faculty of Dentistry, Akdeniz University, 07070 Antalya, Turkey.ORCID 0000-0003-0869-875X
Diana Maria ChioreanDepartment of Pathology, County Clinical Hospital of Targu Mures, 540072 Targu Mures, Romania.ORCID 0000-0002-7024-2916
Yalçın AlbayrakDepartment of Electric and Electronic Engineering, Faculty of Engineering, Akdeniz University, 07010 Antalya, Turkey.
Ovidiu Simion CotoiDepartment of Pathology, County Clinical Hospital of Targu Mures, 540072 Targu Mures, Romania.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancer remains a significant global health concern, with increasing genetic and metabolic irregularities linked to its onset. Among various forms of cancer, skin cancer, including squamous cell carcinoma, basal cell carcinoma, and melanoma, is on the rise worldwide, often triggered by ultraviolet (UV) radiation. The propensity of skin cancer to metastasize highlights the importance of early detection for successful treatment. This narrative review explores the evolving role of artificial intelligence (AI) in diagnosing head and neck skin cancers from both radiological and pathological perspectives. In the past two decades, AI has made remarkable progress in skin cancer research, driven by advances in computational capabilities, digitalization of medical images, and radiomics data. AI has shown significant promise in image-based diagnosis across various medical domains. In dermatology, AI has played a pivotal role in refining diagnostic and treatment strategies, including genomic risk assessment. This technology offers substantial potential to aid primary clinicians in improving patient outcomes. Studies have demonstrated AI's effectiveness in identifying skin lesions, categorizing them, and assessing their malignancy, contributing to earlier interventions and better prognosis. The rising incidence and mortality rates of skin cancer, coupled with the high cost of treatment, emphasize the need for early diagnosis. Further research and integration of AI into clinical practice are warranted to maximize its benefits in skin cancer diagnosis and treatment.

Indexed as

artificial intelligencediagnostic imagingdiagnostic molecular pathologyhead and neck neoplasmsskin neoplasms

Identifiers

PMID39061614
PMCPMC11276319

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.