Exploring the potential of artificial intelligence in improving skin lesion diagnosis in primary care

Towards autonomous robotic THz-based in vivo skin sensing: the PicoBot

Terahertz (THz) light has the unique properties of being very sensitive to water, non-ionizing, and having sub-millimeter depth resolution, making it suitable for medical imaging. Skin conditions including eczema, psoriasis and skin cancer affect a high percentage of the population and we have been developing a THz probe to help with their diagnosis, treatment and management. Our in vivo studies have been using a handheld THz probe, but this has been prone to positional errors through sensorimotor perturbations and tremors, giving spatially imprecise measurements and significant variations in contact pressure. As the operator tires through extended device use, these errors are further exacerbated. A robotic system is therefore needed to tune the critical parameters and achieve accurate and repeatable measurements of skin. This paper proposes an autonomous robotic THz acquisition system, the PicoBot, designed for non-invasive diagnosis of healthy and diseased skin conditions, based on hydration levels in the skin. The PicoBot can 3D scan and segment out the region of interest on the skin's surface, precisely position (± 0.5/1 mm/degrees) the probe normal to the surface, and apply a desired amount of force (± 0.1N) to maintain firm contact for the required 60 s during THz data acquisition. The robotic automation improves the stability of the acquired THz signals, reducing the standard deviation of amplitude fluctuations by over a factor of four at 1 THz compared to hand-held mode. We show THz results for skin measurements of volunteers with healthy and dry skin conditions on various parts of the body such as the volar forearm, forehead, cheeks, and hands. The tests conducted validate the preclinical feasibility of the concept along with the robustness and advantages of using the PicoBot, compared to a manual measurement setup.

Superficial Fungal Infections and Artificial Intelligence: A Review on Current Advances and Opportunities: REVISION

BACKGROUND

Superficial fungal infections are among the most common infections in world, they mainly affect skin, nails and scalp without further invasion. Superficial fungal diseases are conventionally diagnosed with direct microscopy, fungal culture or histopathology, treated with topical or systemic antifungal agents and prevented in immunocompetent patients by improving personal hygiene. However, conventional diagnostic tests can be time-consuming, also treatment can be insufficient or ineffective and prevention can prove to be demanding. Artificial Intelligence (AI) refers to a digital system having an intelligence akin to a human being. The concept of AI has existed since 1956, but hasn't been practicalised until recently. AI has revolutionised medical research in the recent years, promising to influence almost all specialties of medicine.

OBJECTIVE

An increasing number of articles have been published about the usage of AI in cutaneous mycoses.

METHODS

In this review, the key findings of articles about utilisation of AI in diagnosis, treatment and prevention of superficial fungal infections are summarised. Moreover, the need for more research and development is highlighted.

RESULTS

Fifty-four studies were reviewed. Onychomycosis was the most researched superficial fungal infection. AI can be used diagnosing fungi in macroscopic and microscopic images and classify them to some extent. AI can be a tool and be used as a part of something bigger to diagnose superficial mycoses.

CONCLUSION

AI can be used in all three steps of diagnosing, treating and preventing. AI can be a tool complementary to the clinician's skills and laboratory results.

Availability of Evidence for Predictive Machine Learning Algorithms in Primary Care: A Systematic Review

Importance

The aging and multimorbid population and health personnel shortages pose a substantial burden on primary health care. While predictive machine learning (ML) algorithms have the potential to address these challenges, concerns include transparency and insufficient reporting of model validation and effectiveness of the implementation in the clinical workflow.

Objectives

To systematically identify predictive ML algorithms implemented in primary care from peer-reviewed literature and US Food and Drug Administration (FDA) and Conformité Européene (CE) registration databases and to ascertain the public availability of evidence, including peer-reviewed literature, gray literature, and technical reports across the artificial intelligence (AI) life cycle.

Evidence Review

PubMed, Embase, Web of Science, Cochrane Library, Emcare, Academic Search Premier, IEEE Xplore, ACM Digital Library, MathSciNet, AAAI.org (Association for the Advancement of Artificial Intelligence), arXiv, Epistemonikos, PsycINFO, and Google Scholar were searched for studies published between January 2000 and July 2023, with search terms that were related to AI, primary care, and implementation. The search extended to CE-marked or FDA-approved predictive ML algorithms obtained from relevant registration databases. Three reviewers gathered subsequent evidence involving strategies such as product searches, exploration of references, manufacturer website visits, and direct inquiries to authors and product owners. The extent to which the evidence for each predictive ML algorithm aligned with the Dutch AI predictive algorithm (AIPA) guideline requirements was assessed per AI life cycle phase, producing evidence availability scores.

Findings

The systematic search identified 43 predictive ML algorithms, of which 25 were commercially available and CE-marked or FDA-approved. The predictive ML algorithms spanned multiple clinical domains, but most (27 [63%]) focused on cardiovascular diseases and diabetes. Most (35 [81%]) were published within the past 5 years. The availability of evidence varied across different phases of the predictive ML algorithm life cycle, with evidence being reported the least for phase 1 (preparation) and phase 5 (impact assessment) (19% and 30%, respectively). Twelve (28%) predictive ML algorithms achieved approximately half of their maximum individual evidence availability score. Overall, predictive ML algorithms from peer-reviewed literature showed higher evidence availability compared with those from FDA-approved or CE-marked databases (45% vs 29%).

Conclusions and Relevance

The findings indicate an urgent need to improve the availability of evidence regarding the predictive ML algorithms' quality criteria. Adopting the Dutch AIPA guideline could facilitate transparent and consistent reporting of the quality criteria that could foster trust among end users and facilitating large-scale implementation.

Evaluation of an artificial intelligence-based decision support for the detection of cutaneous melanoma in primary care: a prospective real-life clinical trial

BACKGROUND

Use of artificial intelligence (AI), or machine learning, to assess dermoscopic images of skin lesions to detect melanoma has, in several retrospective studies, shown high levels of diagnostic accuracy on par with - or even outperforming - experienced dermatologists. However, the enthusiasm around these algorithms has not yet been matched by prospective clinical trials performed in authentic clinical settings. In several European countries, including Sweden, the initial clinical assessment of suspected skin cancer is principally conducted in the primary healthcare setting by primary care physicians, with or without access to teledermoscopic support from dermatology clinics.

OBJECTIVES

To determine the diagnostic performance of an AI-based clinical decision support tool for cutaneous melanoma detection, operated by a smartphone application (app), when used prospectively by primary care physicians to assess skin lesions of concern due to some degree of melanoma suspicion.

METHODS

This prospective multicentre clinical trial was conducted at 36 primary care centres in Sweden. Physicians used the smartphone app on skin lesions of concern by photographing them dermoscopically, which resulted in a dichotomous decision support text regarding evidence for melanoma. Regardless of the app outcome, all lesions underwent standard diagnostic procedures (surgical excision or referral to a dermatologist). After investigations were complete, lesion diagnoses were collected from the patients' medical records and compared with the app's outcome and other lesion data.

RESULTS

In total, 253 lesions of concern in 228 patients were included, of which 21 proved to be melanomas, with 11 thin invasive melanomas and 10 melanomas in situ. The app's accuracy in identifying melanomas was reflected in an area under the receiver operating characteristic (AUROC) curve of 0.960 [95% confidence interval (CI) 0.928-0.980], corresponding to a maximum sensitivity and specificity of 95.2% and 84.5%, respectively. For invasive melanomas alone, the AUROC was 0.988 (95% CI 0.965-0.997), corresponding to a maximum sensitivity and specificity of 100% and 92.6%, respectively.

CONCLUSIONS

The clinical decision support tool evaluated in this investigation showed high diagnostic accuracy when used prospectively in primary care patients, which could add significant clinical value for primary care physicians assessing skin lesions for melanoma.

[Teleconsultation: finding its place in primary care]

Teleconsultation is a remote health consultation using information and communication technologies. There are different modalities and specific practical and communication skills are required. Notwithstanding its prominence in Spain, there is little evidence on teleconsultation. This article explores the applicability, barriers, facilitators and future challenges of teleconsultation. While it has the potential to improve access to healthcare, as well as save time and costs for both patients and healthcare professionals, it faces a number of challenges such as the digital divide and resistance to change. To address new challenges and overcome obstacles, it is crucial to gain the trust of patients and professionals. Improving training in the skills required to optimize their use is also essential. Future research should aim to provide robust evidence regarding safety and cost-effectiveness to ensure successful implementation.

A Narrative Review: Opportunities and Challenges in Artificial Intelligence Skin Image Analyses Using Total Body Photography

Artificial intelligence (AI) algorithms for skin lesion classification have reported accuracy at par with and even outperformance of expert dermatologists in experimental settings. However, the majority of algorithms do not represent real-world clinical approach where skin phenotype and clinical background information are considered. We review the current state of AI for skin lesion classification and present opportunities and challenges when applied to total body photography (TBP). AI in TBP analysis presents opportunities for intrapatient assessment of skin phenotype and holistic risk assessment by incorporating patient-level metadata, although challenges exist for protecting patient privacy in algorithm development and improving explainable AI methods.

Challenges of artificial intelligence in medicine and dermatology

Artificial intelligence (AI) in medicine and dermatology brings additional challenges related to bias, transparency, ethics, security, and inequality. Bias in AI algorithms can arise from biased training data or decision-making processes, leading to disparities in health care outcomes. Addressing bias requires careful examination of the data used to train AI models and implementation of strategies to mitigate bias during algorithm development. Transparency is another critical challenge, as AI systems often operate as black boxes, making it difficult to understand how decisions are reached. Ensuring transparency in AI algorithms is vital to gaining trust from both patients and health care providers. Ethical considerations arise when using AI in health care, including issues such as informed consent, privacy, and the responsibility for the decisions made by AI systems. It is essential to establish clear guidelines and frameworks that govern the ethical use of AI, including maintaining patient autonomy and protecting sensitive health information. Security is a significant concern in AI systems, as they rely on vast amounts of sensitive patient data. Protecting these data from unauthorized access, breaches, or malicious attacks is paramount to maintaining patient privacy and trust in AI technologies. Lastly, the potential for inequality arises if AI technologies are not accessible to all populations, leading to a digital divide in health care. Efforts should be made to ensure that AI solutions are affordable, accessible, and tailored to the needs of diverse communities, mitigating the risk of exacerbating existing health care disparities. Addressing these challenges is crucial for AI's responsible and equitable integration in medicine and dermatology.

Revolutionizing diagnostic pathology: The emergence and impact of artificial intelligence-what doesn't kill you makes you stronger?

This study explored the integration and impact of artificial intelligence (AI) in diagnostic pathology, particularly dermatopathology, assessing its challenges and potential solutions for global health care enhancement. A comprehensive literature search in PubMed and Google Scholar, conducted on March 30, 2023, and using terms related to AI, pathology, and machine learning, yielded 44 relevant publications. These were analyzed under themes including the evolution of deep learning in pathology, AI's role in replacing pathologists, development challenges of diagnostic algorithms, clinical implementation hurdles, strategies for practical application in dermatopathology, and future prospects of AI in this field. The findings highlight AI's transformative potential in pathology, underscore the need for ongoing research, collaboration, and regulatory dialogue, and emphasize the importance of addressing the ethical and practical challenges in AI implementation for improved global health care outcomes.

Precision Medicine-Are We There Yet? A Narrative Review of Precision Medicine's Applicability in Primary Care

Precision medicine (PM), also termed stratified, individualised, targeted, or personalised medicine, embraces a rapidly expanding area of research, knowledge, and practice. It brings together two emerging health technologies to deliver better individualised care: the many "-omics" arising from increased capacity to understand the human genome and "big data" and data analytics, including artificial intelligence (AI). PM has the potential to transform an individual's health, moving from population-based disease prevention to more personalised management. There is however a tension between the two, with a real risk that this will exacerbate health inequalities and divert funds and attention from basic healthcare requirements leading to worse health outcomes for many. All areas of medicine should consider how this will affect their practice, with PM now strongly encouraged and supported by government initiatives and research funding. In this review, we discuss examples of PM in current practice and its emerging applications in primary care, such as clinical prediction tools that incorporate genomic markers and pharmacogenomic testing. We look towards potential future applications and consider some key questions for PM, including evidence of its real-world impact, its affordability, the risk of exacerbating health inequalities, and the computational and storage challenges of applying PM technologies at scale.

Artificial Intelligence in Skin Cancer Diagnosis: A Reality Check

The field of skin cancer detection offers a compelling use case for the application of artificial intelligence (AI) within the realm of image-based diagnostic medicine. Through the analysis of large datasets, AI algorithms have the capacity to classify clinical or dermoscopic images with remarkable accuracy. Although these AI-based applications can operate both autonomously and under human supervision, the best results are achieved through a collaborative approach that leverages the expertise of both AI and human experts. However, it is important to note that most studies focus on assessing the diagnostic accuracy of AI in artificial settings rather than in real-world scenarios. Consequently, the practical utility of AI-assisted diagnosis in a clinical environment is still largely unknown. Furthermore, there exists a knowledge gap concerning the optimal use cases and deployment settings for these AI systems as well as the practical challenges that may arise from widespread implementation. This review explores the advantages and limitations of AI in a variety of real-world contexts, with a specific focus on its value to consumers, general practitioners, and dermatologists.

The artificial intelligence revolution in primary care: Challenges, dilemmas and opportunities

Artificial intelligence (AI) can be a valuable tool for primary care (PC), as, among other things, it can help healthcare professionals improve diagnostic accuracy, chronic disease management and the overall efficiency of the care they provide. It is important to emphasise that AI should not be seen as a replacement tool, but as an aid to PC professionals. Although AI is capable of processing large amounts of data and generating accurate predictions, it cannot replace the skill and expertise of professionals in clinical decision making. AI still requires the interpretation and clinical judgement of a trained healthcare professional and cannot provide the empathy and emotional support often required in healthcare.

Insights, Advantages, and Barriers of Teledermatology vs. Face-to-Face Dermatology for the Diagnosis and Follow-Up of Non-Melanoma Skin Cancer: A Systematic Review

BACKGROUND

Teledermatology is employed in the diagnosis and follow-up of skin cancer and its use was intensified during and after the COVID-19 pandemic. At the same time, demographic changes result in an overall increase in non-melanoma skin cancer and skin precancerous lesions. The aim of this study was to elucidate the role of teledermatology in comparison to conventional face-to-face dermatology for such lesions and determine the advantages and limitations of this workflow for patients and physicians.

METHODS

Research was performed using relevant keywords in MEDLINE and CENTRAL. Relevant articles were chosen following a predetermined standardized extraction form.

RESULTS

Diagnostic accuracy and interrater/intrarater agreement can be considered comparable-although lower-than in-person consultation. Improvement of particular features such as image quality, medical history availability, and teledermoscopy can further increase accuracy. Further aspects of limitations and advantages (mean time-to-assessment, time-to-treatment, cost-effectiveness) are discussed.

CONCLUSIONS

Teledermatology has comparable diagnostic accuracy with face-to-face dermatology and can be utilized both for the effective triage of non-melanocytic epithelial tumors and precancerous lesions, as well as the follow-up. Easy access to dermatologic consultation with shorter mean times to diagnostic biopsy and/or treatment coupled with cost-effectiveness could compensate for the lower sensitivity of teledermatology and offer easier access to medical care to the affected populations.

Artificial intelligence-assisted dermatology diagnosis: From unimodal to multimodal

Artificial Intelligence (AI) is progressively permeating medicine, notably in the realm of assisted diagnosis. However, the traditional unimodal AI models, reliant on large volumes of accurately labeled data and single data type usage, prove insufficient to assist dermatological diagnosis. Augmenting these models with text data from patient narratives, laboratory reports, and image data from skin lesions, dermoscopy, and pathologies could significantly enhance their diagnostic capacity. Large-scale pre-training multimodal models offer a promising solution, exploiting the burgeoning reservoir of clinical data and amalgamating various data types. This paper delves into unimodal models' methodologies, applications, and shortcomings while exploring how multimodal models can enhance accuracy and reliability. Furthermore, integrating cutting-edge technologies like federated learning and multi-party privacy computing with AI can substantially mitigate patient privacy concerns in dermatological datasets and further fosters a move towards high-precision self-diagnosis. Diagnostic systems underpinned by large-scale pre-training multimodal models can facilitate dermatology physicians in formulating effective diagnostic and treatment strategies and herald a transformative era in healthcare.