If you notice a new or changing mole, getting an accurate assessment as early as possible can make an important difference to your care. Artificial intelligence is increasingly being studied as a way to help your dermatologist analyse suspicious skin lesions, prioritise concerns and support decisions about whether you need further investigation.
Artificial intelligence is being investigated as a way to support melanoma detection by analysing clinical and dermoscopic images. Published research can help you understand how these technologies may assist your dermatologist, where their limitations remain and why professional clinical assessment is still essential.
Why Does Early Melanoma Detection Matter to You?
Melanoma can spread beyond your skin if it is not recognised and treated promptly. When melanoma is identified earlier, your treatment may be less extensive and your outlook is generally more favourable than when disease is diagnosed at a more advanced stage.
You should therefore arrange professional assessment if you notice a new mole or a lesion that changes in size, shape, colour or behaviour. AI may eventually support this pathway, but it should not delay your access to an experienced clinician when you have a concerning skin change.
How Can Artificial Intelligence Support Melanoma Detection?
AI systems can be trained to recognise visual patterns in clinical photographs and dermoscopic images. When your skin lesion is analysed, an algorithm may estimate whether its features are more consistent with a benign lesion or whether your dermatologist should consider it suspicious.
For you, the potential value is not that a computer replaces your dermatologist, but that it provides another source of information. Your clinician can combine the AI output with your history, examination, dermoscopic findings and clinical judgement before deciding what should happen next.
How Does AI Learn to Recognise Suspicious Lesions?
Many image-based AI systems are developed using large collections of labelled skin-lesion images. The algorithm learns associations between visual features in those images and the diagnoses attached to them, which can help it recognise similar patterns when your lesion is assessed.
The quality of that learning depends heavily on the data used. If the training images do not adequately represent your skin tone, lesion type, age group or clinical setting, the system may perform less reliably for you than headline accuracy figures suggest.
What Is Dermoscopy and Why Does It Matter?
Dermoscopy allows your dermatologist to examine structures within a skin lesion using magnification and specialised lighting. It can reveal pigment networks, vascular patterns and other features that are difficult for you or your clinician to assess with the naked eye alone.
Because dermoscopic images contain detailed visual information, they are widely used in melanoma-AI research. An AI system may analyse these images quickly, but your dermatologist still needs to interpret the result in the context of your whole clinical picture.
Why Should AI Support Rather Than Replace Your Dermatologist?
An image does not contain everything your dermatologist needs to know. Your medical history, symptoms, lesion evolution, medications, previous skin cancers, family history and the appearance of the rest of your skin can all influence how a particular lesion is interpreted.
AI can therefore be useful as decision support, triage or an additional assessment tool, but your care should not depend on an algorithm alone. If an AI result conflicts with your dermatologist’s concern or with a lesion that is clearly changing, professional assessment takes priority.
How Is AI Performance in Melanoma Detection Measured?
Researchers use several measures to describe how well an AI system performs. You may see terms such as sensitivity, specificity, positive predictive value and negative predictive value, but none of these numbers should be interpreted in isolation when judging whether a system is safe for your care.
The table below explains the main measures in patient-friendly terms. Your dermatologist also needs evidence showing that an AI system performs well outside the dataset on which it was originally developed.
| Performance Measure | What It Means | Why It Matters to You |
| Sensitivity | How many melanomas are correctly identified as suspicious | Higher sensitivity reduces the chance that your melanoma is missed |
| Specificity | How many non-melanoma lesions are correctly identified as non-suspicious | Higher specificity can reduce unnecessary referrals or procedures |
| False negative | A melanoma is incorrectly classified as low risk | This could falsely reassure you and delay assessment |
| False positive | A benign lesion is classified as suspicious | You may undergo additional appointments or biopsy |
| Positive predictive value | How often a positive result truly represents the target condition | It shows how meaningful a suspicious result may be in your clinical population |
| Negative predictive value | How often a negative result truly represents absence of the target condition | It helps show how reassuring your negative result may be |
| External validation | Testing on patients or images not used to build the model | It shows whether performance may generalise beyond the development dataset |
| Prospective validation | Testing the system as people move through real clinical care | It provides more useful evidence about real-world safety and performance |
Why Do Sensitivity and Specificity Matter to You?
A melanoma-detection system can be tuned to identify more suspicious lesions, but increasing sensitivity may also increase false-positive results. This means you could be referred or biopsied even when your lesion is ultimately benign.
The opposite trade-off also matters. A system that avoids too many false positives may miss lesions that deserve further assessment, so your dermatologist needs to consider the balance between sensitivity and specificity rather than rely on one impressive headline number.
Can AI Reduce Unnecessary Biopsies?
AI may help your healthcare team distinguish some lower-risk lesions from those needing specialist review, which could reduce unnecessary referrals or procedures in certain clinical pathways. Whether this happens depends on the algorithm, the threshold used and where the technology sits within your pathway.
You should not assume that every high-performing AI system reduces biopsies. A tool designed to maximise melanoma detection may deliberately flag more lesions, which could increase the number of benign lesions that your dermatologist investigates.
Why Does Real-World Clinical Validation Matter?
An algorithm can perform very well on a carefully selected image dataset and still behave differently in routine practice. Your clinic may use different cameras, include different skin tones and see a broader mix of lesions than the dataset used to develop the system.
Prospective clinical studies are therefore particularly valuable because they test what happens when AI is used in real patient pathways. Published studies have shown that AI can support melanoma assessment, but your dermatologist still needs to understand which technology was tested and in what population before applying those findings to you.
Research Insight
Prospective melanoma-AI research provides stronger clinical evidence than image-only retrospective testing because it assesses how systems behave in real or multicentre healthcare settings. These studies suggest that AI can support lesion classification in selected pathways, but the findings belong to the particular systems and populations that were evaluated.
You should be cautious about applying one algorithm’s performance figures to another AI system. Results depend on the technology studied, the patient population, the type of images used and the clinical setting, so your dermatologist should consider evidence that is relevant to the particular system being used.
Why Does Image Quality Affect Your AI Result?
AI depends on the information contained in the image it receives. If your photograph is blurred, poorly lit, cropped incorrectly or taken at an unsuitable distance, important lesion features may not be captured clearly enough for reliable analysis.
High-quality dermoscopic imaging can provide more consistent detail, but quality control remains important. You should not rely on a reassuring result from a poor-quality image when your lesion is changing or your dermatologist remains concerned.
Why Does Skin-Tone Diversity Matter?
Your skin tone can influence how lesions appear in clinical photographs, and under-representation in training datasets can create performance gaps. Research has shown that dermatology AI can perform differently across patient groups when datasets are not sufficiently diverse.
For you, this means an overall accuracy figure may conceal weaker performance in particular skin tones, lesion types or anatomical sites. Your dermatologist should therefore consider whether the evidence for a technology includes people who are sufficiently representative of the population in which it will be used.
Can AI Support Remote Skin Assessments?
AI may be incorporated into teledermatology pathways where images of your lesion are captured remotely and reviewed before or alongside specialist assessment. This can help healthcare teams prioritise referrals and may allow you to receive a faster decision about the next stage of your care.
Remote assessment still has limitations because your clinician may not be able to palpate the lesion, examine your entire skin surface or obtain the same information available during a full consultation. If your images are inadequate or your symptoms remain concerning, you may still need an in-person examination.
What Are the Current Limitations of Melanoma AI?
AI performance can change according to image quality, patient population, lesion type, anatomical site, equipment and the prevalence of melanoma in the setting where the technology is used. A strong research result therefore does not guarantee identical performance when the same system is introduced into your local service.
An AI tool also cannot independently integrate every aspect of your health in the way an experienced clinician can. Your dermatologist may need to override an algorithm, arrange biopsy or recommend follow-up when your history or examination raises concern despite a lower-risk AI classification.
When Should You Seek Professional Assessment?
You should arrange professional review if you notice a new mole, a lesion that is changing, an irregular border, several colours within one lesion, persistent itching or pain, bleeding, crusting, ulceration or a mark that looks different from your other moles. A changing lesion beneath your nail or a sore that does not heal also deserves assessment.
You should not wait for an AI application or use a reassuring consumer-app result as a reason to postpone care. If your dermatologist considers a lesion suspicious, removal and histopathological examination may be needed to establish the diagnosis and guide your treatment.
Why Can a False AI Result Matter?
A false-negative AI result could reassure you when melanoma is actually present, while a false-positive result could expose you to anxiety and additional procedures. Safe use therefore depends on clear escalation rules, appropriate clinical oversight and a pathway for your lesion to be reviewed when concern persists.
You should also remember that AI classification is not the same as histopathological confirmation. When your dermatologist removes a suspicious lesion, laboratory examination of the tissue provides diagnostic information that an image-based algorithm cannot replace.
How Is Skin-Lesion AI Being Used in the UK?
NICE published early value assessment guidance in 2025 on AI technologies for assessing and triaging skin lesions referred to the urgent suspected skin cancer pathway. DERM can be used in defined teledermatology services while further evidence is generated, subject to specified conditions and clinical safeguards.
This does not mean every melanoma-AI tool has been approved for your care. Your healthcare provider needs to consider the evidence, intended use, regulatory status, local pathway and safety arrangements for the particular technology being used.
UK Guidance Note
Current UK guidance shows that AI can have a role in skin-lesion triage, but it is introduced within a controlled clinical pathway rather than as an unrestricted replacement for specialist assessment. If AI is involved in your referral, you should still have access to appropriate clinical review when the technology cannot confidently classify your lesion or when concern remains.
UK policy also recognises the risk that medical devices can perform unequally across different patient groups. Developers and healthcare organisations need to consider representativeness, bias, monitoring and real-world performance so that technology used in your care does not create avoidable inequity.
What About Privacy and Your Skin Images?
Clinical and dermoscopic photographs contain health information, so their collection, storage and use require appropriate governance. If your images are used to train, evaluate or operate an AI system, the healthcare organisation should handle them according to applicable privacy, security and data-protection requirements.
You can ask how your images will be used, whether they are stored, who can access them and whether they contribute to research or model development. Clear information helps you understand how your data supports your care and what safeguards apply.
Clinical Tip
If AI is used during your skin assessment, ask your dermatologist what role the technology is playing. It may be helping with triage, providing an additional assessment or supporting referral decisions, and understanding that role can help you interpret the result appropriately.
You should also ask what happens if the AI result and clinical assessment disagree. A safe pathway should make it clear how your lesion will be escalated, reviewed or biopsied when your dermatologist remains concerned.
How Is Artificial Intelligence Being Studied for Melanoma Detection?
Researchers are investigating how artificial intelligence can support melanoma detection through the analysis of clinical and dermoscopic images. For you, the potential benefit is that AI may help your dermatologist identify suspicious lesions and decide which changes in your skin require closer assessment.
Published research has evaluated different AI systems in melanoma assessment, but results belong to the particular technology, population and clinical setting studied. Your care should therefore be based on validated evidence rather than assuming that findings from one AI system apply to every melanoma-detection tool.
What Could Future AI Research Mean for You?
Future studies may improve the ability of AI to work across different skin tones, lesion types, cameras and healthcare settings. Better external and prospective validation could help your dermatologist understand more accurately when an algorithm adds useful information to your assessment.
Research may also explore how AI fits into whole-body imaging, teledermatology and longitudinal monitoring of changing lesions. For you, the most valuable advances will be those that improve access and detection without sacrificing clinical judgement, equity or safety.
Evidence Note
Published evidence supports continued investigation of AI for melanoma detection, including prospective clinical studies in dermatology pathways. The strongest conclusions should be linked to the exact algorithm, dataset and clinical setting studied rather than applied broadly to every AI system.
Current published evidence supports AI-assisted melanoma assessment generally, but results should not be assumed to apply equally to every AI system. You should therefore interpret performance claims in the context of the specific technology, patient population and clinical setting in which they were evaluated.
Myth vs Fact
| Myth | Fact |
| AI can diagnose your melanoma from any photograph | Image analysis can support risk assessment, but your diagnosis may require clinical examination and histopathology |
| A high accuracy score means the system works equally well everywhere | Performance can change with your population, equipment and clinical setting |
| Higher sensitivity always means fewer unnecessary procedures | Higher sensitivity can increase false-positive results |
| A benign AI result means you can ignore a changing mole | You should seek professional assessment if your lesion changes or remains concerning |
| More training images automatically remove bias | Your dataset must also be representative, accurately labelled and clinically relevant |
| AI works equally well across every skin tone | Performance differences can occur when your group is under-represented in development data |
| AI will replace your dermatologist | Current clinical use is generally designed to support or triage care, not remove clinical responsibility |
| Remote photographs provide the same information as a full skin examination | Your clinician may need history, palpation, dermoscopy and examination of other lesions |
| Every AI system continuously learns from your images | Many deployed systems use controlled model versions, with updates requiring evaluation |
| All melanoma-detection AI systems have the same proven performance. | Performance varies between algorithms, patient groups, image types and clinical settings. |
Key Takeaways
- AI may help your dermatologist analyse suspicious skin lesions, but it should support rather than replace professional assessment.
- You should not allow a reassuring AI result to delay review of a new, changing, bleeding or otherwise concerning lesion.
- Your AI result is only as useful as the evidence behind the specific system, including real-world validation and performance across diverse patient groups.
- AI performance should be judged using evidence from the specific system and clinical setting in which it has been tested.
Frequently Asked Questions
1. Can AI tell whether your mole is melanoma?
AI can estimate how suspicious your lesion appears based on patterns learned from images, but it cannot safely replace a full professional assessment. Your dermatologist may recommend dermoscopy, follow-up or removal for histopathology depending on your lesion and clinical history.
2. Why might your dermatologist use AI?
Your dermatologist may use AI to support triage or lesion assessment, depending on the technology and clinical pathway. The aim is to provide additional information that can help your clinician decide which lesions need closer attention.
3. Does a low-risk AI result mean your lesion is definitely benign?
No. No image-based system can guarantee that your lesion is harmless. If your mole changes, bleeds, becomes symptomatic or continues to worry you, you should seek professional assessment even if an AI tool previously classified it as lower risk.
4. What is dermoscopy?
Dermoscopy is a non-invasive examination that gives your dermatologist a magnified, illuminated view of structures within your skin lesion. These detailed images are also commonly used to develop and evaluate melanoma-AI systems.
5. Can AI reduce the number of biopsies you need?
Possibly, depending on the algorithm and clinical pathway. Some systems may help identify lower-risk lesions, but a high-sensitivity approach can also increase false-positive results, so your biopsy decision should remain clinically guided.
6. Does AI work equally well for every skin tone?
Not necessarily. Performance can vary when your skin tone or lesion type is under-represented in training and validation datasets, which is why diversity and subgroup testing are important parts of safe AI development.
7. Can you use a smartphone AI app instead of seeing a dermatologist?
You should not use a consumer app as a substitute for professional assessment of a changing or suspicious lesion. Your dermatologist can assess features, history and other clinical information that may not be captured in a smartphone photograph.
8. Is AI already being used for skin lesions in the NHS?
Yes, selected AI technology can be used within defined NHS teledermatology pathways under NICE guidance while further evidence is generated. This does not mean every AI tool is suitable or approved for use in your care.
9. Why does your image quality matter?
An AI system can only analyse the visual information available in your photograph. Poor lighting, blur, framing or image resolution can hide important features, so an inadequate image may reduce the reliability of your assessment.
10. What does the HEROS study mean for your care?
The topic highlights how artificial intelligence may support melanoma assessment and help your dermatologist evaluate suspicious skin lesions. For your care, the most reliable approach combines validated AI technology, published clinical evidence and professional dermatological assessment.
Final Thoughts: What Does the HEROS Study Mean for You?
Artificial intelligence has genuine potential to support earlier and more efficient assessment of suspicious skin lesions, and prospective studies are helping clinicians understand where these tools may add value. For you, the safest approach is still one in which AI complements expert assessment, appropriate follow-up and histopathology when a lesion requires removal.
If you’d like to book a consultation with a dermatologist in London, you can contact us at the London Dermatology Centre.
References:
- Heinlein, L., Maron, R.C., Hekler, A. et al. (2024) ‘Prospective multicenter study using artificial intelligence to improve dermoscopic melanoma diagnosis in patient care’, Communications Medicine. Available at: https://pubmed.ncbi.nlm.nih.gov/39256516/
- Papachristou, P., Söderholm, M., Pallon, J. et al. (2024) ‘Evaluation of an artificial intelligence-based decision support for the detection of cutaneous melanoma in primary care: a prospective real-life clinical trial’, British Journal of Dermatology, 191(1), pp. 125–133. Available at: https://pubmed.ncbi.nlm.nih.gov/38234043/
- Daneshjou, R., Vodrahalli, K., Novoa, R.A. et al. (2022) ‘Disparities in dermatology AI performance on a diverse, curated clinical image set’, Science Advances, 8(32), eabq6147. Available at: https://pmc.ncbi.nlm.nih.gov/articles/PMC9374341/
- NICE (2025) ‘Artificial intelligence (AI) technologies for assessing and triaging skin lesions referred to the urgent suspected skin cancer pathway: early value assessment (HTG746)’. Available at: https://www.nice.org.uk/guidance/htg746
- NICE (2022) ‘Melanoma: assessment and management (NG14)’. Available at: https://www.nice.org.uk/guidance/ng14/chapter/recommendations
- Department of Health and Social Care (2024) ‘Government response to the report of the equity in medical devices independent review’. Available at: https://www.gov.uk/government/publications/government-response-to-the-report-of-the-equity-in-medical-devices-independent-review/government-response-to-the-report-of-the-equity-in-medical-devices-independent-review
- Ang, X.L. and Oh, C.C. (2025) ‘The use of artificial intelligence for skin cancer detection in Asia: a systematic review’, Diagnostics, 15(7), 939. Available at: https://pubmed.ncbi.nlm.nih.gov/40218289/