You are here

Responses

Article menu

Download PDFPDF

XML
Evidence based practice
Research
Diagnosing malignant melanoma in ambulatory care: a systematic review of clinical prediction rules
Compose a Response to This Article
Compose Response

More information about text formats

Plain text

  • No HTML tags allowed.
  • Web page addresses and e-mail addresses turn into links automatically.
  • Lines and paragraphs break automatically.
Author Information
First or given name, e.g. 'Peter'.
Your last, or family, name, e.g. 'MacMoody'.
Your email address, e.g. higgs-boson@gmail.com
Your role and/or occupation, e.g. 'Orthopedic Surgeon'.
Your organization or institution (if applicable), e.g. 'Royal Free Hospital'.
Statement of Competing Interests

PLEASE NOTE:

  • A rapid response is a moderated but not peer reviewed online response to a published article in a BMJ journal; it will not receive a DOI and will not be indexed unless it is also republished as a Letter, Correspondence or as other content. Find out more about rapid responses.
  • We intend to post all responses which are approved by the Editor, within 14 days (BMJ Journals) or 24 hours (The BMJ), however timeframes cannot be guaranteed. Responses must comply with our requirements and should contribute substantially to the topic, but it is at our absolute discretion whether we publish a response, and we reserve the right to edit or remove responses before and after publication and also republish some or all in other BMJ publications, including third party local editions in other countries and languages
  • Our requirements are stated in our rapid response terms and conditions and must be read. These include ensuring that: i) you do not include any illustrative content including tables and graphs, ii) you do not include any information that includes specifics about any patients,iii) you do not include any original data, unless it has already been published in a peer reviewed journal and you have included a reference, iv) your response is lawful, not defamatory, original and accurate, v) you declare any competing interests, vi) you understand that your name and other personal details set out in our rapid response terms and conditions will be published with any responses we publish and vii) you understand that once a response is published, we may continue to publish your response and/or edit or remove it in the future.
  • By submitting this rapid response you are agreeing to our terms and conditions for rapid responses and understand that your personal data will be processed in accordance with those terms and our privacy notice.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.

Vertical Tabs

Other responses

Jump to comment:

  • Published on: 4 June 2018
    Diagnosis of melanoma: clinical rules versus artificial intelligence
    • Sara D. Teixeira, Undergraduate Medical Student Centro Universitário Presidente Tancredo de Almeida Neves (UNIPTAN), Brazil
    • Other Contributors:
      • Henrique A Silva, Psychiatrist, MD, professor of Epidemiology and Statistics at UNIPTAN

    In view of the high mortality and the need for early diagnosis of melanoma, Harrington et al.(1) reviewed the studies on the diagnostic rules to stratify patients with suspected melanoma and concluded that the ABCD rule is more useful than the 7-point checklist. Despite the importance of this result, future evaluations of diagnostic methods should also include among the comparisons the diagnostic tools assisted by artificial intelligence. Computational analysis of dermatological images has shown great potential as a diagnostic tool for melanoma (2,3), and can contribute to reduce costs, increase access and the scope of the examination to regions without specialists, allowing early diagnosis by primary care physicians, mainly in remote areas, lacking specialists.

    1. Harrington, E., Clyne, B., Wesseling, N., Sandhu, H., Armstrong, L., Bennett, H., & Fahey, T. (2017). Diagnosing malignant melanoma in ambulatory care: a systematic review of clinical prediction rules. BMJ Open, 7(3), e014096. http://doi.org/10.1136/bmjopen-2016-014096

    2. Safran T, Viezel-Mathieu A, Corban J, Kanevsky A, Thibaudeau S, Kanevsky J. Machine learning and melanoma: The future of screening. J Am Acad Dermatol. 2018 Mar;78(3):620-621. doi: 10.1016/j.jaad.2017.09.055. Epub 2017 Oct 6. PubMed PMID: 28989109.

    3. Jaworek-Korjakowska J, Kłeczek P. Automatic Classification of Specific Melanocytic Lesions Using Artificial Int...

    Show More
    Conflict of Interest:
    None declared.