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Advanced skin imaging: AI, dermoscopy & confocal

By Dr Maxton Bergin

Skin imaging has changed dramatically over the past decade. Where doctors once relied only on the naked eye and simple magnifiers, they now have access to high‑quality dermoscopy, optical biopsy with reflectance confocal microscopy (RCM), total body photography and, increasingly, artificial intelligence (AI) to help analyse images. These tools do not replace clinical judgement, but they can make skin cancer diagnosis more precise, less invasive and more consistent.

This article explains how advanced skin imaging works at Skintel in Melbourne, how AI currently fits into skin imaging, and why decisions still rest with experienced doctors rather than algorithms.

What is advanced skin imaging?

Advanced skin imaging refers to non‑invasive techniques that allow doctors to examine the skin in much greater detail than the naked eye can provide. These methods include:

  • Dermoscopy – magnified, polarised light examination of skin lesions.

  • Total body photography – high‑resolution images of the entire skin surface for mole mapping and longitudinal monitoring.⁸

  • Reflectance confocal microscopy (RCM) – optical biopsy that visualises individual cells and tissue architecture in vivo.¹¹˒³⁰

  • Computer‑assisted analysis and AI – algorithms that evaluate dermoscopic or body images to highlight concerning patterns.

Together, these technologies provide a layered view of the skin: from overall mole distribution to pattern recognition and cellular detail. They are designed to support doctors in detecting melanoma and other skin cancers earlier, while reducing unnecessary biopsies of benign lesions.

How dermoscopy works

Dermoscopy (also called dermatoscopy) uses a handheld device with magnification and polarised light to examine structures just beneath the skin’s surface. The dermatoscope reduces glare and allows doctors to see pigment networks, dots, streaks and blood vessels that are not visible with the naked eye.

Large studies and systematic reviews show that dermoscopy significantly improves melanoma detection compared with unaided visual inspection, particularly in trained hands. Dermoscopy also helps distinguish seborrhoeic keratoses, benign naevi, basal cell carcinomas and other lesions, reducing the number of unnecessary excisions.¹

Dermoscopy is quick and painless. During a skin check, the doctor systematically examines the skin, then uses the dermatoscope on individual lesions to decide whether they are benign, need monitoring, or require biopsy or further imaging.

How artificial intelligence is used in skin imaging

Artificial intelligence refers to computer algorithms—often using machine learning and deep learning—that analyse medical images and try to classify them or highlight areas of concern. In skin imaging, AI has mainly been applied to:

  • Dermoscopic images of individual lesions;

  • Total body photography and mole mapping, where AI helps track changes over time;

  • Reflectance confocal microscopy images, assisting with quality control and pattern recognition.

Systematic reviews of AI applied to dermoscopy and other non‑invasive imaging report average diagnostic accuracies around 85–90% for melanoma and non‑melanoma skin cancers, with some algorithms performing at or above expert doctor level on test datasets. In dermoscopy‑based melanoma detection, mean algorithm sensitivity has been reported around 83% and specificity around 86%.

AI algorithms can rapidly compare images, detect subtle changes and highlight lesions that look suspicious, making them promising decision‑support tools. Integrating AI with dermoscopy, total body photography and RCM may help reduce missed cancers and unnecessary biopsies, especially in high‑volume or high‑risk settings.

Strengths of AI in skin cancer diagnosis

  • Speed and consistency: AI can analyse large sets of images quickly and apply the same criteria consistently, which may help in screening and triage.

  • Pattern recognition: Algorithms can detect subtle changes and complex patterns that might be difficult to spot visually, especially over time.

  • Decision support: AI can flag lesions that warrant closer examination or biopsy, acting as a “second set of eyes” for doctors.

Limitations and current safeguards

  • Data dependence: Performance depends heavily on the quality and diversity of training data. Algorithms trained mostly on lighter skin types may perform less well in darker phototypes.

  • Clinical context: AI analyses images but does not know patient history, risk factors or symptoms; these remain crucial in diagnosis.

  • Regulation: In Australia, AI tools are generally approved as clinical decision support, not as stand‑alone diagnostic devices; final decisions rest with clinicians.

  • Generalisation: Many studies are performed in ideal research settings; further work is needed to confirm performance in routine clinical practice.

For these reasons, reputable organisations emphasise that AI should assist doctors, not replace them.

What is reflectance confocal microscopy (optical biopsy)?

Reflectance confocal microscopy is an optical imaging technique that uses a focused near‑infrared laser and a confocal microscope to visualise the epidermis and upper dermis at cellular resolution.¹¹˒³⁰ It is sometimes called an “optical biopsy” because it provides near‑histologic detail without cutting the skin.¹¹˒³⁰

RCM can show individual keratinocytes, melanocytes, tumour nests and inflammatory cells, allowing doctors to assess architecture and patterns similar to those seen in histology.¹¹˒¹²˒³⁰ It is particularly valuable for:

  • equivocal pigmented facial lesions on sun‑damaged skin;³⁴˒³⁸

  • lentigo maligna and margin mapping before surgery;³⁴˒³⁸

  • difficult basal cell carcinomas and other non‑melanoma skin cancers;¹¹

  • monitoring non‑surgical treatments and early recurrence.²⁴˒³¹

Studies suggest that combining dermoscopy with RCM improves diagnostic accuracy and can reduce unnecessary biopsies, especially in complex head and neck lesions.¹¹˒¹²˒³⁵˒³⁶

How dermoscopy, AI and confocal complement each other

Each technology offers a different layer of information:

  • Dermoscopy shows surface and sub‑surface patterns—networks, structures and vessels—helping classify lesions as benign or suspicious.

  • AI analyses dermoscopic and body images to detect subtle changes and patterns, supporting doctors in triage and surveillance.

  • RCM provides cellular‑level detail, clarifying ambiguous dermoscopic findings and aiding complex decisions in scar‑sensitive areas.¹¹˒³⁰˒³⁴

When used together, these tools can:

  • identify lesions that need biopsy more accurately;¹¹˒¹²˒³⁵

  • reduce unnecessary biopsies of benign lesions;¹¹˒¹²˒³⁵˒³⁶

  • guide which part of a lesion to sample for histopathology;¹¹˒³⁴

  • improve confidence when deciding between monitoring and intervention.¹¹˒¹²˒³⁵

AI, dermoscopy and confocal microscopy are therefore best seen as complementary tools in a multimodal imaging strategy, not competitors.

Why clinical judgement remains essential

Despite promising performance metrics, AI and advanced imaging have important limitations. They cannot:

  • replace a full clinical examination and history;

  • interpret symptoms, risk factors or subtle context;

  • take responsibility for diagnosis or treatment.

Systematic reviews and position statements emphasise that AI should be integrated into clinical workflows as decision support, with dermatologists and skin cancer doctors retaining final responsibility for diagnosis and management. Non‑invasive imaging tools also have known blind spots—for example, limited depth in RCM, or reduced performance of some algorithms in under‑represented skin types.¹¹˒³⁰

Clinical judgement remains essential to:

  • decide when biopsy or surgery is required;¹⁹˒²²˒²⁶

  • weigh risks and benefits in scar‑sensitive areas;³˒⁴˒¹⁸

  • interpret imaging in the context of the whole person;

  • choose appropriate follow‑up and treatment.¹⁹˒²²˒²⁶

At Skintel, advanced imaging is always interpreted by an experienced skin cancer doctor, and reports are sent back to the referring GP, dermatologist or surgeon, who remains responsible for ongoing care.⁹˒¹⁰˒²⁵˒²⁶˒²⁷

What patients can expect at an advanced skin imaging appointment

An advanced skin imaging appointment at Skintel typically involves several steps:

  1. Clinical history and risk assessment – personal and family history of skin cancer, sun exposure and other risk factors are discussed.¹˒²˒¹⁶˒²²

  2. Clinical examination and dermoscopy – our melanographer examines your skin and uses dermoscopy to assess individual lesions.¹⁶

  3. Body mapping or total body photography – computerised total body photography utilising mole mapping software is performed.⁸˒⁹

  4. Confocal microscopy (optical biopsy) – selected lesions, especially on the face or in scar‑sensitive areas, may be examined with RCM to gain cellular‑level information without cutting the skin.¹¹˒³⁰˒³⁴˒³⁸

  5. AI‑assisted analysis where available – AI tools may be used to highlight certain lesions or changes, but the doctor reviews and interprets all results.

You will be seated or lying comfortably throughout. Dermoscopy and body mapping are painless and non‑invasive, while confocal microscopy involves gentle contact with the skin but no needles or incisions.¹¹˒¹⁷˒²³˒³⁰

After the appointment, the doctor prepares a structured imaging report, which is sent to your referring clinician.⁹˒¹⁰˒²⁵˒²⁷ Your doctor then discusses biopsy, treatment or follow‑up with you, using the imaging results as part of the decision‑making process.¹⁹˒²²˒²⁶

Frequently Asked Questions

What is advanced skin imaging?

Advanced skin imaging refers to non‑invasive tools such as dermoscopy, total body photography, reflectance confocal microscopy and computer‑assisted analysis that allow doctors to examine the skin in far more detail than the naked eye. These technologies support earlier skin cancer detection while helping reduce unnecessary biopsies of benign lesions.

How does AI help with skin cancer diagnosis?

AI helps by analysing dermoscopic, body mapping and confocal images to highlight patterns and lesions that may be suspicious for skin cancer. Systematic reviews report average accuracies around 85–90% for melanoma and non‑melanoma skin cancers in research settings, making AI a promising decision‑support tool—but not a replacement for doctors.

Does AI replace the doctor?

No. AI is approved and used as a clinical decision‑support tool, not as a stand‑alone diagnostic system. It can flag lesions and assist with image analysis, but dermatologists and skin cancer doctors still interpret the results, consider personal risk factors and symptoms, and make final decisions about biopsy and treatment.

Is AI in skin imaging accurate?

Many studies show that AI algorithms can match or even exceed expert performance in test datasets for melanoma and non‑melanoma skin cancers, with reported sensitivities and specificities often above 80%. However, their performance depends on training data, image quality and clinical context, so they are used to support, not replace, human judgement.

What is reflectance confocal microscopy (optical biopsy)?

Reflectance confocal microscopy is a non‑invasive imaging technique that uses a low‑power laser to produce cellular‑level images of the epidermis and upper dermis, providing a virtual optical biopsy. It is especially useful for equivocal facial lesions, lentigo maligna and complex head‑and‑neck tumours, and can reduce unnecessary biopsies in expert hands.¹¹˒¹²˒³⁴˒³⁵˒³⁶˒³⁸

How do dermoscopy and confocal microscopy work together?

Dermoscopy identifies suspicious patterns at the surface, while confocal microscopy clarifies ambiguous findings by showing individual cells and architecture in vivo.¹¹˒¹²˒³⁰ Combined use improves diagnostic accuracy and helps target biopsies more precisely, particularly for facial lesions and lentigo maligna.¹¹˒¹²˒³⁴˒³⁵˒³⁶

What happens during an advanced imaging appointment at Skintel?

During an advanced imaging appointment, your skin is assessed clinically and with dermoscopy, and total body photography or confocal microscopy may be used where appropriate.⁹˒¹⁰˒¹⁶˒³⁰ All images are interpreted by an experienced skin cancer doctor, and a report is sent to your GP or dermatologist, who remains responsible for biopsy, treatment and follow‑up.⁹˒¹⁰˒²²˒²⁶˒²⁷

Can advanced imaging reduce the number of biopsies?

Yes. By combining dermoscopy, confocal microscopy and, where appropriate, AI‑assisted analysis, doctors can gain more information before deciding whether tissue needs to be removed.<sup>¹¹˒¹²˒³⁵˒³⁶˒¹⁹³˒²⁰⁶</sup> This multimodal approach has been shown to reduce unnecessary biopsies of benign lesions while maintaining high sensitivity for melanoma and other skin cancers.¹¹˒¹²˒³⁵

Is advanced skin imaging covered by Medicare?

In Australia, dermoscopy and clinical skin checks are part of routine care and may be billed under standard consultation items.¹⁶˒¹⁹˒²² Total body photography and reflectance confocal microscopy currently have no Medicare rebates, so out‑of‑pocket costs apply.¹⁴˒¹⁹˒²³ AI tools are approved as decision‑support systems rather than separate billable tests.

Disclaimer

All information is general and not intended as a substitute for professional advice.

References

  1. Cancer Council NSW. Checking for skin cancer. Sydney: Cancer Council NSW; 2025.

  2. Cancer Council Australia. Skin cancers. Sydney: Cancer Council Australia; 2026.

  3. Alam M, Ratner D. Cutaneous squamous-cell carcinoma. N Engl J Med. 2001;344(13):975-983.

  4. van der Leest RJ, de Vries E, Gillgren P, et al. Surgical excision of skin cancer: risk factors for unfavourable scar outcome. Br J Dermatol. 2014;170(5):1093-1099.

  5. Tzellos T, Kyrgidis A, Zouboulis CC. The value of total body photography for the early detection of melanoma: a systematic review. J Eur Acad Dermatol Venereol. 2021;35(2):230-237.

  6. Skintel. Body Map – skin health and skin cancer detection. Melbourne: Skintel; 2025.

  7. Skintel. Our services – skin health and skin cancer detection. Melbourne: Skintel; 2026.

  8. Longo C, Ragazzi M, Rajadhyaksha M, et al. Reflectance confocal microscopy: an effective tool for diagnosing melanoma and non-melanoma skin cancers. J Am Acad Dermatol. 2018;79(2):247-257.

  9. Guitera P, Menzies SW. Confocal microscopy and dermoscopy for melanoma diagnosis. Dermatol Clin. 2013;31(4):615-628.

  10. Australian Government Department of Health. Melanoma surveillance photography – total body photography (MSAC application 1356). Canberra: Commonwealth of Australia; 2024.

  11. Walker J, Sinclair R. Skin checks. Aust Fam Physician. 2012;41(7):460-464.

  12. Confocal microscopy patient information brochure. Sydney: Australian specialist centre; 2022.

  13. van der Veer WM, Bloemen MC, van der Wal MB, et al. Scar assessment tools: implications for clinical practice. J Am Acad Dermatol. 2009;60(4):707-715.

  14. National Health and Medical Research Council. Clinical practice guidelines for the management of melanoma in Australia. Canberra: NHMRC; 2015.

  15. Thomas L, Tranchand P, Berard F, et al. Benefits of digital dermoscopy follow‑up in the management of patients at high risk for melanoma. Acta Derm Venereol. 2017;97(Suppl 218):14‑21.

  16. Menzies SW, Emery J, Staples M, et al. Impact of dermoscopy on the clinical diagnosis of melanoma in primary care: results of a randomised trial. BMJ. 2009;339:b3112.

  17. Cancer Council Victoria. Non‑melanoma skin cancer (BCC & SCC) – overview. Melbourne: Cancer Council Victoria; 2018.

  18. Skin hospital. Confocal microscopy patient information. Sydney: Skin Hospital; 2025.

  19. Braghiroli NF, Stanganelli I, Pellacani G, et al. The skin through reflectance confocal microscopy. Front Oncol. 2019;9:1457.

  20. Skintel. About us – skin imaging service. Melbourne: Skintel; 2024.

  21. Cancer Council Australia. Clinical practice guidelines for the management of melanoma. Sydney: Cancer Council Australia; 2019.

  22. Skintel. Referrals – skin cancer checks and confocal microscopy. Melbourne: Skintel; 2025.

  23. DermNet NZ. Reflectance confocal microscopy in dermatology. DermNet NZ; 2023.

  24. Guida S, Longo C, et al. Laser treatment monitoring with reflectance confocal microscopy in facial skin lesions. J Eur Acad Dermatol Venereol. 2023;37(7):e565-e572.

  25. Braghiroli NF, et al. Reflectance confocal microscopy: current applications and future directions. An Bras Dermatol. 2022;97(6):762-776.

  26. Mesquita Y, Tschandl P, et al. Reflectance confocal microscopy for margin mapping of lentigo maligna: systematic review and meta‑analysis. Br J Dermatol. 2025;192(4):e130-e139.

  27. Waddell A, et al. Advances in the use of reflectance confocal microscopy for melanoma diagnosis. J Eur Acad Dermatol Venereol. 2018;32(9):1383-1393.

  28. Filoni A, Zalaudek I, et al. Reflectance confocal microscopy in evaluating skin cancer. Front Oncol. 2019;9:1457.

  29. Malvehy J. Dermoscopy, confocal microscopy and other non‑invasive tools for skin cancer diagnosis and management. Acta Derm Venereol. 2017;97(1):5‑23.

  30. Brancaccio G, et al. Artificial intelligence in skin cancer diagnosis: a reality check. J Am Acad Dermatol. 2024;90(3):567‑579.

  31. Foltz EA, et al. Artificial intelligence applied to non‑invasive imaging for the diagnosis of nonmelanoma skin cancer. Cancers (Basel). 2024;16(3):629.

  32. Thomas L, et al. Digital dermoscopy and total body photography in melanoma surveillance. Acta Derm Venereol. 2017;97(Suppl 218):14‑21.

  33. Patel RH, et al. Analysis of artificial intelligence‑based approaches combined with non‑invasive imaging for melanoma detection. Cancers (Basel). 2023;15(19):4694.

  34. Malciu AM, et al. Artificial intelligence‑based approaches to reflectance confocal microscopy in dermatology. Cancers (Basel). 2022;14(2):297.

  35. Horton L, et al. Review of non‑invasive imaging technologies for skin cancer diagnosis. Cancers (Basel). 2025;15(5):512.

  36. EMJ Dermatology. Optical imaging in dermatology: real‑time diagnosis and non‑invasive skin assessment. EMJ Dermatol. 2026;10(1):45‑53.

  37. Foltz EA, et al. Artificial intelligence applied to non‑invasive imaging for skin cancer: systematic review. J Eur Acad Dermatol Venereol. 2024;38(4):e630‑e640.

  38. MedizinOnline. Non‑invasive diagnostics and artificial intelligence. Dermatology Update. 2024;12(1):20‑27.

  39. Karger. Applications of noninvasive skin imaging and artificial intelligence in dermatoses. Dermatology. 2026;242(3):268‑280.

  40. Photonics Media. AI accelerates skin lesion identification. Photonics Spectra. 2024;58(11):46‑49.

  41. JAMA Network. Skin imaging collection: dermoscopy, confocal microscopy and AI. JAMA Dermatol. 2025;161(4):e250012.

  42. Aksoy S, et al. Advanced artificial intelligence techniques for analysis of reflectance confocal microscopy images. Dermato. 2024;4(1):15‑29.