IDEAS home Printed from https://ideas.repec.org/a/abq/ijist1/v7y2025i6p107-117.html
   My bibliography  Save this article

Digital Dermatologist: An AI-Powered Mobile App for Early Detection of Skin Diseases

Author

Listed:
  • Zahid Hussain, Preh Keerio, Rehman Shahani

    (Department of Computer Science,Quaid-e-Awam University of Engineering, Science and Technology, Nawabshah, Pakistan)

Abstract

An increasing number of people are experiencing skin problems, causing overcrowding in hospitals and clinics. This situation highlights the need for a quicker and more convenient way to diagnose these conditions. To address this, we have developed a mobile application that uses artificial intelligence (AI) to detect skin diseases.The app provides fast and useful information about skin issues through AI. Its user-friendly design makes it easy for anyone to use, even without technical knowledge. This tool helps people monitor their skin health and reduces the burden on healthcare facilities. By using the app, users can identify skin problems early and receive guidance on possible treatments.

Suggested Citation

  • Zahid Hussain, Preh Keerio, Rehman Shahani, 2025. "Digital Dermatologist: An AI-Powered Mobile App for Early Detection of Skin Diseases," International Journal of Innovations in Science & Technology, 50sea, vol. 7(6), pages 107-117, May.
    • Handle: RePEc:abq:ijist1:v:7:y:2025:i:6:p:107-117
    as

    Download full text from publisher

    File URL: https://journal.50sea.com/index.php/IJIST/article/view/1282/1874
    Download Restriction: no
    File URL: https://journal.50sea.com/index.php/IJIST/article/view/1282
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Correction: Corrigendum: Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 546(7660), pages 686-686, June.
    2. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 542(7639), pages 115-118, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chinmay Belthangady & Stefanos Giampanis & Ivana Jankovic & Will Stedden & Paula Alves & Stephanie Chong & Charlotte Knott & Beau Norgeot, 2022. "Causal deep learning reveals the comparative effectiveness of antihyperglycemic treatments in poorly controlled diabetes," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    2. Maria Sial, Salman Shakeel, Muhammad Asim, Amaad Khalil, Muhammad Abeer Irfan,Atif Jan, 2024. "Skin Scan: Cutting-edge AI-Powered Skin Cancer Classification App for Early Diagnosis and Prevention," International Journal of Innovations in Science & Technology, 50sea, vol. 6(5), pages 227-235, May.
    3. Gang Yu & Kai Sun & Chao Xu & Xing-Hua Shi & Chong Wu & Ting Xie & Run-Qi Meng & Xiang-He Meng & Kuan-Song Wang & Hong-Mei Xiao & Hong-Wen Deng, 2021. "Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    4. Matthew O. Jackson & Qiaozhu Me & Stephanie W. Wang & Yutong Xie & Walter Yuan & Seth Benzell & Erik Brynjolfsson & Colin F. Camerer & James Evans & Brian Jabarian & Jon Kleinberg & Juanjuan Meng & Se, 2025. "AI Behavioral Science," Papers 2509.13323, arXiv.org.
    5. Majd Oteibi & Adam Tamimi & Kaneez Abbas & Gabriel Tamimi & Danesh Khazaei & Hadi Khazaei, 2024. "Advancing Digital Health using AI and Machine Learning Solutions for Early Ultrasonic Detection of Breast Disorders in Women," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 11(11), pages 518-527, November.
    6. Podobnik, Boris & Dabić, Marina & Wild, Dorian & Di Matteo, Tiziana, 2023. "The impact of STEM on the growth of wealth at varying scales, ranging from individuals to firms and countries: The performance of STEM firms during the pandemic across different markets," Technology in Society, Elsevier, vol. 72(C).
    7. DonHee Lee & Seong No Yoon, 2021. "Application of Artificial Intelligence-Based Technologies in the Healthcare Industry: Opportunities and Challenges," IJERPH, MDPI, vol. 18(1), pages 1-18, January.
    8. Pujin Wang & Jianzhuang Xiao & Ken’ichi Kawaguchi & Lichen Wang, 2022. "Automatic Ceiling Damage Detection in Large-Span Structures Based on Computer Vision and Deep Learning," Sustainability, MDPI, vol. 14(6), pages 1-24, March.
    9. Hailong He & Christine Schönmann & Mathias Schwarz & Benedikt Hindelang & Andrei Berezhnoi & Susanne Annette Steimle-Grauer & Ulf Darsow & Juan Aguirre & Vasilis Ntziachristos, 2022. "Fast raster-scan optoacoustic mesoscopy enables assessment of human melanoma microvasculature in vivo," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    10. Zhiming Cui & Yu Fang & Lanzhuju Mei & Bojun Zhang & Bo Yu & Jiameng Liu & Caiwen Jiang & Yuhang Sun & Lei Ma & Jiawei Huang & Yang Liu & Yue Zhao & Chunfeng Lian & Zhongxiang Ding & Min Zhu & Dinggan, 2022. "A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    11. Tan, Hongjun & Guo, Zhiling & Lin, Zhengyuan & Chen, Yuntian & Huang, Dou & Yuan, Wei & Zhang, Haoran & Yan, Jinyue, 2024. "General generative AI-based image augmentation method for robust rooftop PV segmentation," Applied Energy, Elsevier, vol. 368(C).
    12. Zheng Yan & Wenqian Robertson & Yaosheng Lou & Tom W. Robertson & Sung Yong Park, 2021. "Finding leading scholars in mobile phone behavior: a mixed-method analysis of an emerging interdisciplinary field," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(12), pages 9499-9517, December.
    13. Syed Ibrar Hussain & Elena Toscano, 2025. "Enhancing Recognition and Categorization of Skin Lesions with Tailored Deep Convolutional Networks and Robust Data Augmentation Techniques," Mathematics, MDPI, vol. 13(9), pages 1-36, April.
    14. Jasper Tromp & David Bauer & Brian L. Claggett & Matthew Frost & Mathias Bøtcher Iversen & Narayana Prasad & Mark C. Petrie & Martin G. Larson & Justin A. Ezekowitz & Scott D. Solomon, 2022. "A formal validation of a deep learning-based automated workflow for the interpretation of the echocardiogram," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    15. von Walter, Benjamin & Wentzel, Daniel & Raff, Stefan, 2023. "Should service firms introduce algorithmic advice to their existing customers? The moderating effect of service relationships," Journal of Retailing, Elsevier, vol. 99(2), pages 280-296.
    16. Kuofeng Hung & Andy Wai Kan Yeung & Ray Tanaka & Michael M. Bornstein, 2020. "Current Applications, Opportunities, and Limitations of AI for 3D Imaging in Dental Research and Practice," IJERPH, MDPI, vol. 17(12), pages 1-18, June.
    17. Chowdhury, Emon Kalyan, 2019. "Use of Artificial Intelligence in Stock Trading," MPRA Paper 118175, University Library of Munich, Germany, revised 18 Apr 2019.
    18. Mendes, Carlos Frederico S. da F. & Krohling, Renato A., 2022. "Deep and handcrafted features from clinical images combined with patient information for skin cancer diagnosis," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    19. Butros M. Dahu & Carlos I. Martinez-Villar & Imad Eddine Toubal & Mariam Alshehri & Anes Ouadou & Solaiman Khan & Lincoln R. Sheets & Grant J. Scott, 2024. "Application of Machine Learning and Deep Neural Visual Features for Predicting Adult Obesity Prevalence in Missouri," IJERPH, MDPI, vol. 21(11), pages 1-23, November.
    20. Romain Cadario & Chiara Longoni & Carey K. Morewedge, 2021. "Understanding, explaining, and utilizing medical artificial intelligence," Nature Human Behaviour, Nature, vol. 5(12), pages 1636-1642, December.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:abq:ijist1:v:7:y:2025:i:6:p:107-117. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Iqra Nazeer (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.