AI in Dermatology: A Systematic Review on Skin Cancer Detection
Open accessAbstract
Skin cancer is the most common type of cancer worldwide and poses a significant public health challenge. Its visible nature often leads individuals to seek medical attention, highlighting the importance of early detection for better patient outcomes. In recent years, Artificial Intelligence (AI) has shown promise in improving the detection and diagnosis of skin cancer, offering the potential to enhance clinical outcomes. A systematic review was conducted, involving a comprehensive literature search to identify studies focused on AI techniques in detecting, diagnosing, or treating skin cancer. Strict inclusion and exclusion criteria were applied to assess the eligibility of scientific articles, resulting in the selection of nine relevant studies. These studies were analyzed to address predefined research questions about the effectiveness of AI in diagnosing skin cancer. The review found that AI-assisted clinicians achieved higher sensitivity and specificity in diagnosing skin cancer than those without assistance. Various AI algorithms demonstrated high sensitivity in detecting skin cancers, highlighting their potential to support primary care clinicians in evaluating suspicious lesions. The analysis also highlighted the effectiveness of smartphone applications designed for skin cancer risk assessment, which could facilitate self-examinations and enhance early detection rates. Despite these promising findings, the field of AI in skin cancer diagnosis is still in its early stages. Challenges remain, including developing robust algorithms, addressing data quality issues, and improving the interpretability of AI-generated results. Collaboration between AI developers and healthcare professionals is crucial to ensure these tools' clinical effectiveness and safety. The review emphasizes the need for continued validation of AI technologies and their integration into clinical practice to improve patient outcomes and alleviate the burden on healthcare systems.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Extensive research has shown that convolutional neural network accuracy is comparable to that of dermatologists.
“Extensive research has shown that the accuracy of CNNs is comparable to that of dermatologists [ 32 - 34 ].”
From Psychological Factors Influencing Appropriate Reliance on AI-enabled Clinical Decision Support Systems: Experimental Web-Based Study Among Dermatologists (2025) · cites this paper as [34]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
The PRISMA 2020 statement: An updated guideline for reporting systematic reviews
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., et al. (2021) · BMJ · doi:10.1136/bmj.n71 · Cited by 108,283 · see citing works
The PRISMA 2020 statement: an updated guideline for reporting systematic reviews
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., et al. (2021) · Systematic Reviews · doi:10.1186/s13643-021-01626-4 · Cited by 13,874 · cited 1× · see citing works
“The structure and presentation of the items have been modified to facilitate more straightforward implementation [24].”
BackgroundSkin cancer detection: A review using deep learning techniques
Dildar, M., Akram, S., Irfan, M., Khan, H. U., Ramzan, M., Mahmood, A. R., Alsaiari, S. A., Saeed, A. H. M., Alraddadi, M. O., & Mahnashi, M. H. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18105479 · Cited by 597 · cited 1× · see citing works
“These methods analyze lesion features such as symmetry, color, size, and shape to distinguish between benign and malignant skin cancers [13].”
MethodTriandini, E., Jayanatha, S., Indrawan, A., et al. (2019) · Indonesian Journal of Information Systems · doi:10.24002/ijis.v1i2.1916 · Cited by 592 · cited 1×
“Therefore, an SLR is not just a narrative review but a structured and guided research method that provides a deep understanding of a research topic [23].”
BackgroundGoyal, M., Knackstedt, T., Yan, S., & Hassanpour, S. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104065 · Cited by 461 · cited 1× · see citing works
“AI-based methods, especially deep learning algorithms, have shown improved accuracy in categorizing skin lesions from various image types, including dermoscopic, clinical, and histopathological images [14].”
MethodJones, O. T., Matin, R. N., van der Schaar, M., Prathivadi Bhayankaram, K., Ranmuthu, C. K. I., Islam, M. S., Behiyat, D., Boscott, R., Calanzani, N., Emery, J., et al. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00023-1 · Cited by 272 · cited 1× · see citing works
“Studies have demonstrated that AI/ML algorithms can perform as well as, or better than, consultant dermatologists in diagnosing skin cancers, offering significant support to clinicians in their diagnostic processes [17].”
BackgroundSkin Cancer Detection Using Deep Learning—A Review
Naqvi, M., Gilani, S. Q., Syed, T., Marques, O., & Kim, H. (2023) · Diagnostics · doi:10.3390/diagnostics13111911 · Cited by 203 · cited 1× · see citing works
“Recent advancements in deep learning and image processing techniques have shown promise in improving the accuracy of skin cancer detection [10], [12].”
BackgroundMelarkode, N., Srinivasan, K., Qaisar, S. M., & Plawiak, P. (2023) · Cancers · doi:10.3390/cancers15041183 · Cited by 144 · see citing works
Artificial Intelligence in Skin Cancer Diagnosis: A Reality Check
Brancaccio, G., Balato, A., Malvehy, J., Puig, S., Argenziano, G., & Kittler, H. (2023) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2023.10.004 · Cited by 137 · cited 1× · see citing works
“AI in Dermatology…(Safrian Andromeda) dermoscopy benefited the most from using AI, showing the most remarkable improvement in their diagnostic accuracy [8].”
BackgroundA reinforcement learning model for AI-based decision support in skin cancer
Barata, C., Rotemberg, V. M., Codella, N., Tschandl, P., Rinner, C., Akay, B. N., Apalla, Z., Argenziano, G., Halpern, A. C., Lallas, A., et al. (2023) · Nature Medicine · doi:10.1038/s41591-023-02475-5 · Cited by 109 · cited 1× · see citing works
“These findings suggest that integrating human preferences into AI decision-support systems can lead to better outcomes in clinical practice, particularly in complex scenarios like skin cancer diagnosis [30].”
BackgroundHuman-AI interaction in skin cancer diagnosis: a systematic review and meta-analysis
Krakowski, I., Kim, J., Cai, Z. R., Daneshjou, R., Lapins, J., Eriksson, H., Lykou, A., & Linos, E. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01031-w · Cited by 87 · cited 1× · see citing works
“%) in comparison to unassisted clinicians (74.8% and 81.5%, respectively) in diagnosing skin cancer [18].”
ResultInfluentialSaeed, M., Naseer, A., Masood, H., Rehman, S. U., & Gruhn, V. (2023) · IEEE Access · doi:10.1109/access.2023.3332628 · Cited by 81 · cited 1× · see citing works
“In summary, the integration of deep learning techniques played a crucial role in significantly enhancing the accuracy and effectiveness of skin cancer classification in this study [32].”
MethodSKIN CANCER DETECTION USING IMAGE PROCESSING
Aishasiddhika, N., & Jenovajosephine, P. (2024) · International Research Journal of Modernization in Engineering Technology and Science · doi:10.56726/irjmets52335 · Cited by 80 · cited 1× · see citing works
“Recent advancements in deep learning and image processing techniques have shown promise in improving the accuracy of skin cancer detection [10], [12].”
BackgroundFurriel, B. C. R. S., Oliveira, B. D. A., Prôa, R., Paiva, J. Q., Loureiro, R. M., Calixto, W. P., Reis, M. R. C., & Bianchi, M. G. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2023.1305954 · Cited by 77 · cited 3× · see citing works
“While AI has the potential to revolutionize early skin cancer detection, it is crucial to validate these tools and collaborate with healthcare professionals to ensure clinical effectiveness and safety [16].”
BackgroundInfluentialArtificial intelligence and skin cancer
Wei, M. L., Tada, M., So, A., & Torres, R. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2024.1331895 · Cited by 69 · cited 2× · see citing works
“Overall, the background sets the stage for exploring the dynamics between technology and user preferences in the context of skin cancer screening, highlighting the need for further research to improve the adoption of AI-enabled health solutions [11].”
BackgroundLearnings from the first AI-enabled skin cancer device for primary care authorized by FDA
Venkatesh, K. P., Kadakia, K. T., & Gilbert, S. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01161-1 · Cited by 63 · cited 3× · see citing works
“Overall, the results suggest that DermaSensor can enhance the diagnostic abilities of PCPs, address access limitations in dermatology, and improve patient outcomes by facilitating earlier detection of skin cancers [31].”
BackgroundMethodInfluentialMidasala, V., Prabhakar, B., Chaitanya, J. K., Sirnivas, K., Eshwar, D., & Kumar, P. M. (2024) · Engineering Science and Technology an International Journal · doi:10.1016/j.jestch.2024.101632 · Cited by 51 · cited 1× · see citing works
“Additionally, the study emphasizes the importance of interpretability in deep learning models, which is crucial for gaining the trust of healthcare professionals and patients [27].”
BackgroundSimamora, S. C., Gaffar, V., Arief, M., et al. (2024) · Jurnal Ilmiah M-Progress · doi:10.35968/m-pu.v14i1.1182 · Cited by 46 · cited 1×
“One of the main features developed is the systematic literature search using the PRISMA method, which assists researchers in conducting comprehensive and structured literature reviews [25].”
MethodMarsden, H., Morgan, C., Austin, S., DeGiovanni, C., Venzi, M., Kemos, P., Greenhalgh, J., Mullarkey, D., & Palamaras, I. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1288521 · Cited by 32 · cited 1× · see citing works
“The statistical analysis showed that the algorithm's confidence scale was closely associated with the histopathology-confirmed diagnoses, indicating that the AIaMD could serve as a reliable tool for dermatologists in diagnosing skin cancer [34].”
MethodMatin, R., & Dinnes, J. (2021) · British Journal of Cancer · doi:10.1038/s41416-021-01302-3 · Cited by 30 · cited 1× · see citing works
“On the other hand, TeleSkin’s skinScan app, designed for skin lesion risk stratification, is available for download, but there is limited published evidence regarding its effectiveness [29].”
BackgroundManolakos, D., Patrick, G., Geisse, J. K., et al. (2023) · JAAD International · doi:10.1016/j.jdin.2023.08.019 · Cited by 23 · cited 1×
“%) in the detection of various skin cancers, potentially assisting primary care clinicians in evaluating suspicious lesions [19].”
BackgroundComparing preferences for skin cancer screening: AI-enabled app vs dermatologist
Gaube, S., Biebl, I., Engelmann, M. K. M., Kleine, A., & Lermer, E. (2024) · Social Science & Medicine · doi:10.1016/j.socscimed.2024.116871 · Cited by 20 · see citing works
Sunlight Exposure and Protection Behavior as Prevention of Skin Cancer in Nursing Students
Fauziyyah, R. N. P., Komariah, M., & Herliani, Y. K. (2023) · Indonesian Journal of Cancer · doi:10.33371/ijoc.v17i1.921 · Cited by 17
Analysis of Skin Cancer and Patient Healthcare Using Data Mining Techniques
Arivazhagan, N., Mukunthan, M. A., Sundaranarayana, D., et al. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/2250275 · Cited by 15 · see citing works
Sustainability Finance Dan Green Investment: Literature Review Dengan Metode Prisma
Febrianti, S. (2024) · Manajemen : Jurnal Ekonomi · doi:10.36985/manajemen.v6i1.1151 · Cited by 15 · cited 1×
“With these features, researchers can present their data in graphs, diagrams, or other visual representations that are easier to understand and interpret [26].”
BackgroundJaklitsch, E. L., Thames, T., Silva, T. D. C., Coll, P. P., Oliviero, M., & Ferris, L. K. (2023) · Journal of Primary Care & Community Health · doi:10.1177/21501319231205979 · Cited by 15 · cited 1× · see citing works
“Overall, AI enhances the diagnostic process by providing objective data that supports clinical judgment, thereby improving the overall accuracy of skin cancer detection in primary care settings [33].”
BackgroundThe State of Artificial Intelligence in Skin Cancer Publications
Joly-Chevrier, M., Nguyen, A. X., Liang, L., Lesko-Krleza, M., & Lefrançois, P. (2024) · Journal of Cutaneous Medicine and Surgery · doi:10.1177/12034754241229361 · Cited by 11 · cited 1× · see citing works
“These systems analyze different imaging methods, with dermoscopy and clinical images being the most prevalent [15].”
BackgroundIslam, M., Akash, S., Rauf, A., et al. (2023) · Annals of Medicine and Surgery · doi:10.1097/ms9.0000000000000662 · Cited by 7
Karakteristik pasien kanker kulit non-melanoma di RSUP Sanglah pada periode tahun 2014 - 2018
Yogiswara, I. G. A. I., Saputra, H., & Ekawati, N. (2021) · Intisari Sains Medis · doi:10.15562/ism.v12i2.1080 · Cited by 6
Oktora, M., Haiga, Y., & Liana, N. (2023) · Jurnal Pengabdian Masyarakat Kesehatan · doi:10.56260/jurabdikes.v1i2.105 · Cited by 5
A Review of Skin Cancer Detection: Traditional and Deep Learning-Based Techniques
Hussien, M. A., & Alasadi, A. H. H. (2023) · JOURNAL OF UNIVERSITY OF BABYLON for Pure and Applied Sciences · doi:10.29196/jubpas.v31i2.4682 · Cited by 3 · see citing works
Skin cancer diagnosis using the deep learning advancements: a technical review
Pandey, S., & Shankhdhar, G. K. (2024) · Bulletin of Electrical Engineering and Informatics · doi:10.11591/eei.v13i3.5925 · Cited by 3 · see citing works
Artificial intelligence in skin cancer diagnosis: a literature review
Citrashanty, I., & Wardiana, M. (2023) · Bali Dermatology Venereology and Aesthetic Journal · doi:10.51559/g554gs55 · cited 1×
“Even though AI cannot fully replace doctors in integrating patient history with physical examination findings [21].”
BackgroundSkin Cancer Detection using Image Processing
Jadhav, P. R. (2023) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2023.52666
Flagged first, then by citations
Research works printed but not linked: 1
- V. Mallikarjunaradhya and J. Mistry, 'The Optimized Analysis For Early Detection Of Skin Cancer Using Artificial Intelligence', vol. 11, no. 7, 2023.
Cited by
Sorted by citations
Küper, A., Lodde, G. C., Livingstone, E., Schadendorf, D., & Krämer, N. (2025) · Journal of Medical Internet Research · doi:10.2196/58660 · Cited by 31 · see citing works
“AI systems excel at processing large datasets, identifying subtle patterns, and providing consistent and rapid analysis, which is particularly valuable for diagnostic support [34].”
Integrating AI and IoT for the Implementation of Skin Cancer Detection in Hospital 4.0
Mrabet, A. E., Alihamidi, I., Benaly, M., et al. (2025) · International Conference on Circuit, Systems and Communication (ICCSC) · doi:10.1109/iccsc66714.2025.11134977 · Cited by 3 · see citing works
“Deep learning algorithms based on AI have attained dermatologist-level performance in the classification of skin lesions through training convolutional neural networks (CNNs) with large-scale image datasets for automatic diagnosis [2].”
Skin AI - Virtual Dermatologist
Jyothsna, K., Reddy, V. S. V., Lokesh, D. L., Chaitanya, D. K., Sivakrishna, K., & Priyanka, L. (2025) · International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI) · doi:10.1109/icoici65217.2025.11253360
Current challenges in skin cancer diagnosis and treatment
Bele, T., Gawad, J., Patil, S. D., Jadhao, M., Maru, S., & Beldar, V. G. (2026) · Advances in cancer research · doi:10.1016/bs.acr.2026.01.010
- Authors
- Andromeda, S., & Dwijaksara, N. L. B.
- Affiliations
- University of Singaperbangsa Karawang (ID)
- Venue
- TIERS Information Technology Journal
- Publisher
- Universitas Pendidikan Nasional
- Published
- 2024-06-25
- Volume
- 5
- Issue
- 1
- Pages
- 41-51
- ISSN
- 2723-4533 (ISSN-L)
- 2723-4541
- OpenAlex
- W4402145562
- Cited by
- 4
- References
- 33
- Citations by year
- 2026: 1 · 2025: 3
- FWCI
- 0.22
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- TIERS Information Technology Journal (publishedVersion, cc-by-sa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- skin cancer, artificial intelligence, skin cancer detection, computer-aided diagnosis, early detection, mobile application, self-examination, primary care, explainable AI, clinical validation of AI
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
