Figure 5 - available via license: Creative Commons Attribution 4.0 International
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The schematic diagram shows the hypothetical use of machine learning algorithms to help dermatologists diagnose lesions to make appropriate clinical decisions. An emerging AI model CNN can help non-expert clinicians narrow the range of differential diagnosis and provide appropriate treatments.
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Background:
Thanks to the rapid development of computer-based systems and deep-learning-based algorithms, artificial intelligence (AI) has long been integrated into the healthcare field. AI is also particularly helpful in image recognition, surgical assistance and basic research. Due to the unique nature of dermatology, AI-aided dermatological dia...
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... has also gradually become a hot topic of discussion in dermatology and dermatopathology. The current health care society and legal framework are more suitable for using AI as a decision aid for dermatologists, especially in terms of assisting the diagnosis ( Figure 5). On account of the rapid development of AI and its already widespread use by patients and doctors, several international and regional survey studies were conducted. ...
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In recent years, deep learning (DL) has shown great potential in the field of dermatological image analysis. However, existing datasets in this domain have significant limitations, including a small number of image samples, limited disease conditions, insufficient annotations, and non-standardized image acquisitions. To address these shortcomings,...
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... AI allows for the augmentation of microsurgical methods as well as real-time, perioperative feedback. According to Li et al., exceptional three-dimensional visual fields and instrumental capabilities as applied to dermatologic surgery can be achieved with the help of AI [11]. In turn, betterment of perioperative approaches with the use of digital intelligence minimizes the rate of postoperative complications. ...
Introduction The field of dermatology is undergoing a transformative shift with the advent of digital intelligence, particularly through the integration of artificial intelligence (AI) and machine learning (ML) technologies. These advancements are poised to revolutionize the prediction and management of skin diseases, offering unprecedented accuracy and personalized treatment strategies. As skin diseases often present complex diagnostic and prognostic challenges, traditional methods of prediction and treatment are increasingly being supplemented and, in many cases, surpassed by AI-driven approaches. This literature review aims to provide a comprehensive overview of the current state of AI applications in dermatology, focusing on their ability to forecast disease progression and treatment outcomes.
... Recognizing the critical importance of accurate skin lesion diagnosis, particularly in the context of urban health challenges, the dermatology field is turning to innovative solutions. Artificial intelligence (AI) emerges as a powerful tool to revolutionize dermatological practices, especially in the era of smart and green cities [5]. ...
Smart and sustainable dermatology takes on a new dimension within Green Smart Cities with the integration of artificial intelligence (AI) into dermatological diagnosis. This study explores the success of deep learning models in accurately recognizing skin lesions, focusing on the use of the HAM10000 dataset. Our comparative analysis highlights the crucial impact of network architecture choices, data augmentation, and preprocessing on model performance. The results reveal that models leveraging transfer learning and fine-tuning on pre-trained networks excel in precision, underscoring their relevance in the context of smart green health. We also address opportunities for improvement in model generalization across diverse datasets and skin types. These findings provide a foundation for the development of more accurate skin lesion recognition models aligned with the principles of Green Smart Health, contributing to faster diagnostics, improved patient care, and ultimately, healthier Green Smart Cities. This work opens avenues for future research, such as exploring of the effectiveness of deep learning techniques in diverse health contexts and the integration of clinical data for more personalized dermatological diagnostics within Green Smart Cities.
... Currently, studies report high accuracy performance, even exceeding dermatologists for the diagnosis of skin lesions [1]. The use of artificial intelligence in dermatology is mainly image-based and is constructed by the deep learning (DL) methodology, which makes connections between inputs such as images and outputs such as diagnoses like BCC or melanoma [2]. As a result, a complex mapping is created with links that bond image patterns and prediction of diagnosis. ...
This survey represents the first endeavor to assess the clarity of the dermoscopic language by a chatbot, unveiling insights into the interplay between dermatologists and AI systems within the complexity of the dermoscopic language. Given the complex, descriptive, and metaphorical aspects of the dermoscopic language, subjective interpretations often emerge. The survey evaluated the completeness and diagnostic efficacy of chatbot-generated reports, focusing on their role in facilitating accurate diagnoses and educational opportunities for novice dermatologists. A total of 30 participants were presented with hypothetical dermoscopic descriptions of skin lesions, including dermoscopic descriptions of skin cancers such as BCC, SCC, and melanoma, skin cancer mimickers such as actinic and seborrheic keratosis, dermatofibroma, and atypical nevus, and inflammatory dermatosis such as psoriasis and alopecia areata. Each description was accompanied by specific clinical information, and the participants were tasked with assessing the differential diagnosis list generated by the AI chatbot in its initial response. In each scenario, the chatbot generated an extensive list of potential differential diagnoses, exhibiting lower performance in cases of SCC and inflammatory dermatoses, albeit without statistical significance, suggesting that the participants were equally satisfied with the responses provided. Scores decreased notably when practical descriptions of dermoscopic signs were provided. Answers to BCC scenario scores in the diagnosis category (2.9 ± 0.4) were higher than those with SCC (2.6 ± 0.66, p = 0.005) and inflammatory dermatoses (2.6 ± 0.67, p = 0). Similarly, in the teaching tool usefulness category, BCC-based chatbot differential diagnosis received higher scores (2.9 ± 0.4) compared to SCC (2.6 ± 0.67, p = 0.001) and inflammatory dermatoses (2.4 ± 0.81, p = 0). The abovementioned results underscore dermatologists’ familiarity with BCC dermoscopic images while highlighting the challenges associated with interpreting rigorous dermoscopic images. Moreover, by incorporating patient characteristics such as age, phototype, or immune state, the differential diagnosis list in each case was customized to include lesion types appropriate for each category, illustrating the AI’s flexibility in evaluating diagnoses and highlighting its value as a resource for dermatologists.
... Visual question-answering efforts have mainly targeted radiology images, overlooking the crucial context provided by clinical text (Abacha et al., 2019a). While recent advancements in deep learning have shown promise in lesion classification for dermatology (Li et al., 2022), these approaches often focus on specific image types and cannot integrate textual information, essential for a holistic understanding of a patient's condition. While some research explores combining clinical text and images for specific dermatology tasks, such as melanoma risk assessment, they haven't addressed open-ended question answering (Groh et al., 2022;Lin et al., 2023). ...
The MEDIQA-M3G 2024 challenge necessitates novel solutions for Multilingual & Multi-modal Medical Answer Generation in dermatology (wai Yim et al., 2024a). This paper addresses the limitations of traditional methods by proposing a weakly supervised learning approach for open-ended medical question-answering (QA). Our system leverages readily available MEDIQA-M3G images via a VGG16-CNN-SVM model, enabling multilingual (En-glish, Chinese, Spanish) learning of informative skin condition representations. Using pre-trained QA models, we further bridge the gap between visual and textual information through multimodal fusion. This approach tackles complex , open-ended questions even without pre-defined answer choices. We empower the generation of comprehensive answers by feeding the ViT-CLIP model with multiple responses alongside images. This work advances medical QA research, paving the way for clinical decision support systems and ultimately improving healthcare delivery.
... AI algorithms, particularly deep learning models, have demonstrated remarkable accuracy and efficiency in analyzing dermatological images to detect and classify various skin conditions, ranging from common disorders like acne and eczema to more complex diseases such as melanoma and basal cell carcinoma [12]. These AI-powered systems aid dermatologists in making more accurate and timely diagnoses, leading to improved patient outcomes and enhanced clinical workflow efficiency [13]. Moreover, AI-driven decision-support tools provide valuable insights and recommendations for personalized treatment plans, helping clinicians optimize therapeutic interventions based on individual patient characteristics and disease profiles [13]. ...
... These AI-powered systems aid dermatologists in making more accurate and timely diagnoses, leading to improved patient outcomes and enhanced clinical workflow efficiency [13]. Moreover, AI-driven decision-support tools provide valuable insights and recommendations for personalized treatment plans, helping clinicians optimize therapeutic interventions based on individual patient characteristics and disease profiles [13]. Additionally, AI-based predictive models leverage patient data and clinical parameters to forecast disease progression, treatment response, and potential adverse outcomes, enabling proactive management strategies and preventive interventions [13]. ...
... Moreover, AI-driven decision-support tools provide valuable insights and recommendations for personalized treatment plans, helping clinicians optimize therapeutic interventions based on individual patient characteristics and disease profiles [13]. Additionally, AI-based predictive models leverage patient data and clinical parameters to forecast disease progression, treatment response, and potential adverse outcomes, enabling proactive management strategies and preventive interventions [13]. Overall, the integration of AI technologies into dermatological practice holds tremendous promise for advancing the field, facilitating precision medicine approaches, and ultimately improving patient care delivery in dermatology. ...
... The presence of such errors, artifacts, or the lack of proper data preprocessing algorithms adds another layer of complexity to raw dermatological data, which must be addressed through comprehensive data preprocessing strategies to enhance the reliability of the model outputs. 84 One major challenge is addressing bias and limitations in dataset representation, which are evident in the diversity in skin types, conditions, and demographic factors. These aspects may not have been adequately emphasized in training datasets, leading to biases in model outcomes. ...
In any ailment, the identification of the symptoms, detection, and diagnosis plays a pivotal role in treatment and therapy. However, certain diseases share similar symptoms, lacking signature key indicators, which can lead to fallacious or incorrect inferences. Skin disorders, such as pruritus, dermatitis, eczema, psoriasis, and ichthyosis, all present similar symptoms, which confound clinicians. One such commonly misunderstood condition is atopic dermatitis (AD), a chronic inflammatory skin condition characterized by its relapsing nature, which heightens the importance of diagnosis and disease monitoring for effective management. Recent strides in artificial intelligence (AI) have opened avenues for precise diagnosis and continuous monitoring of AD. This review explores and evaluates current applications of AI in the diagnosis and monitoring of individuals with AD emphasizing the need to address challenges and collaborate across intra-, inter-, trans-, and multi-disciplinary domains to maximize the benefits of AI in enhancing the precision of AD diagnosis, ultimately leading to improved patient care and satisfaction through technologically-driven biomedical tools in customized healthcare.
... AI-driven diagnostic technologies, such dermatology and radiology image analysis, increase diagnosis speed and accuracy, especially in underserved or distant areas where access to qualified healthcare providers is limited [40]. ...
Healthcare and the biomedical sciences are going through a revolutionary period due to the introduction of Artificial Intelligence (AI) in medical research. This chapter examines the critical role artificial intelligence (AI) has played in transforming modern medicine and highlights the several ways in which AI has transformed the industry. It is clear from a thorough analysis of the complex interactions that exist between AI and medical research that AI is more than just a tool-rather, it is a driving force behind innovation in healthcare. Artificial Intelligence will undoubtedly have a significant impact on healthcare in the future. It will enable precise disease diagnosis, quicker medication development, individualized treatment regimens, predictive analytics, and early disease outbreak identification. This chapter explores AI's amazing potential, including its ability to provide accurate diagnosis, speed up drug discovery, and enable tailored medicine-all of which can improve patient outcomes and save expenses associated with healthcare. Additionally, the potential of AI to improve medical imaging, anticipate and mitigate epidemics, and increase the effectiveness of healthcare delivery is investigated. But this revolutionary potential also brings up moral and legal issues that need to be carefully considered. This chapter presents a fair analysis of the advantages and difficulties presented by artificial intelligence (AI) in medical research, offering insightful information about how the field of healthcare is changing as AI develops.
... 46 It is based on "machine learning", namely through the data used to train the AI algorithm (training datasets) to make decisions and predictions, and with experience the algorithm automatically improves. 47 In turn, Machine Learning contains the deep-learning sub-type, based on artificial neural networks (ANNs), a mathematical model inspired by the way neural networks work and whose performance depends on the number of ANNs and the training datasets. 47 The popularity of AI has increased rapidly in recent years and one of the questions raised is how much the physician's practice can gain from it. ...
... 47 In turn, Machine Learning contains the deep-learning sub-type, based on artificial neural networks (ANNs), a mathematical model inspired by the way neural networks work and whose performance depends on the number of ANNs and the training datasets. 47 The popularity of AI has increased rapidly in recent years and one of the questions raised is how much the physician's practice can gain from it. Several studies have been conducted among the different specialities, but it is those dependent on imaging that have the greatest benefits, such as radiology, ophthalmology and dermatology. ...
Teledermatology may be defined as the application of telemedicine to dermatology. According to published data, teledermatology is more widespread in Europe and North America, probably where resources for health care are greater than in other areas of the world. Indeed, teledermatology requires advanced technology to be efficient, as high image quality is necessary to allow the dermatologist to make correct diagnoses. Thanks to the recent advances in this field, teledermatology is become routinary in daily clinical practice. However, its use has been improved over time, overcoming several challenges. The aim of this narrative review is to retrace the almost 30-year history of teledermatology, to address the new challenges posed by advancing technologies such as artificial intelligence and the implications it may have on healthcare.
... This study explores the techniques for diagnosing skin lesions, focusing on dermatoscopy, the importance of early detection, and the role of artificial intelligence in improving diagnosis accuracy. The importance of combining the features of two CNN models for analyzing dermoscopy images in the early detection of skin cancer and distinguishing it from other types of skin lesions lies in its potential to enhance accuracy, robustness, and diagnostic capabilities significantly [12]. This approach capitalizes on the strengths of each individual CNN model while compensating for its limitations, thereby improving overall performance and enabling more reliable and effective diagnosis. ...
Skin cancer is one of the most fatal skin lesions, capable of leading to fatality if not detected in its early stages. The characteristics of skin lesions are similar in many of the early stages of skin lesions. The AI in categorizing diverse types of skin lesions significantly contributes to and helps dermatologists to preserve patients’ lives. This study introduces a novel approach that capitalizes on the strengths of hybrid systems of Convolutional Neural Network (CNN) models to extract intricate features from dermoscopy images with Random Forest (Rf) and Feed Forward Neural Networks (FFNN) networks, leading to the development of hybrid systems that have superior capabilities early detection of all types of skin lesions. By integrating multiple CNN features, the proposed methods aim to improve the robustness and discriminatory capabilities of the AI system. The dermoscopy images were optimized for the ISIC2019 dataset. Then, the area of the lesions was segmented and isolated from the rest of the image by a Gradient Vector Flow (GVF) algorithm. The first strategy for dermoscopy image analysis for early diagnosis of skin lesions is by the CNN-RF and CNN-FFNN hybrid models. CNN models (DenseNet121, MobileNet, and VGG19) receive a region of interest (skin lesions) and produce highly representative feature maps for each lesion. The second strategy to analyze the area of skin lesions and diagnose their type by means of CNN-RF and CNN-FFNN hybrid models based on the features of the combined CNN models. Hybrid models based on combined CNN features have achieved promising results for diagnosing dermoscopy images of the ISIC 2019 dataset and distinguishing skin cancers from other skin lesions. The Dense-Net121-MobileNet-RF hybrid model achieved an AUC of 95.7%, an accuracy of 97.7%, a precision of 93.65%, a sensitivity of 91.93%, and a specificity of 99.49%.
... Acquiring high-quality images of skin lesions can be difficult because of factors such as their location on the body and the presence of skin artifacts like colored markings and tattoos [3]. AI-generated clinical images offer a solution to these challenges while also eliminating privacy concerns associated with using actual patient photos. ...
