¹California Health Sciences University College of Osteopathic Medicine, Clovis, CA, USA

²Des Moines University College of Osteopathic Medicine, West Des Moines, IA, USA

³Albert Einstein College of Medicine, Bronx, NY, USA

⁴Rowan-Virtua School of Osteopathic Medicine, Stratford, NJ, USA

⁵Nova Southeastern University Dr. Kiran Patel College of Osteopathic Medicine, Fort Lauderdale, FL, USA

⁶Mercer University School of Medicine, Macon, GA, USA

⁷Department of Dermatology, Northwell Health, New Hyde Park, NY, USA

*Corresponding author: Kelly Frasier, DO, MS, Department of Dermatology, Northwell Health, New Hyde Park, NY, USA, Phone: 3105956882, Email: [email protected]

Received Date: February 09, 2025

Publication Date: February 19, 2025

Citation: Flores J, et al. (2025). Artificial Intelligence and Machine Learning Transforming Dermatopathology with Diagnosis and Predictive Analytics. Dermis. 5(1):29.

Copyright: Flores J, et al. © (2025).

ABSTRACT

The integration of artificial intelligence (AI) and machine learning (ML) into dermatopathology is revolutionizing the field by enhancing diagnostic precision, enabling advanced predictive analytics, and optimizing workflows. Dermatopathology traditionally relies on subjective interpretations of histopathological features, which can be prone to variability among pathologists; however, AI and ML algorithms, particularly those utilizing deep learning techniques and convolutional neural networks (CNNs), demonstrate exceptional capabilities in pattern recognition and data integration. Many algorithms, trained on large annotated datasets such as HAM10000 and ISIC, have achieved diagnostic accuracies surpassing 95% for conditions such as melanoma, basal cell carcinoma, and inflammatory dermatoses, with the added benefit of standardizing diagnostic practices by minimizing interobserver variability. Beyond diagnostics, AI-driven predictive analytics is emerging as a transformative tool, enabling prognostic assessments and personalized patient stratification by integrating histopathological, molecular, and clinical data. For example, ML models have been employed to correlate histopathological patterns with molecular markers, allowing risk stratification and identification of therapeutic targets, particularly for aggressive conditions like cutaneous melanoma. Additionally, AI-powered digital pathology platforms are streamlining workflows by automating routine tasks such as mitotic figure counting, margin assessment, and cellular quantification, which reduces diagnostic turnaround times and allows dermatopathologists to focus on complex cases, addressing growing demands in underserved regions. Despite these advancements, challenges remain in the form of algorithmic bias due to insufficiently diverse training datasets, regulatory barriers, and ethical concerns regarding data privacy and model interpretability. Addressing these challenges requires the development of comprehensive, explainable AI systems and the establishment of transparent frameworks for clinical integration. The transformative potential of AI and ML in dermatopathology is evident, with these technologies poised to redefine the field by delivering precision diagnostics, personalized care, and enhanced efficiency, ultimately advancing dermatopathology into a new era of evidence-based, patient-centered medicine.

Keywords: Artificial Intelligence, Machine Learning, Dermatopathology, Skin Cancer

INTRODUCTION

Dermatopathology, the microscopic study of skin diseases, serves as the foundation of dermatological diagnostics, offering crucial insights into the pathogenesis, prognosis, and management of complex conditions. Historically, the field has relied on traditional microscopy and manual processes, which, while foundational, are inherently subjective and labor-intensive. These conventional practices are constrained by variability among pathologists and dermatologists, impacting diagnostic precision and workflow efficiency. These challenges are compounded by increasing workloads and a global shortage of trained specialists, particularly in underserved regions.

Integrating artificial intelligence (AI) and machine learning (ML) into dermatopathology is rapidly reshaping the field by addressing these critical gaps. AI refers to the simulation of human intelligence by machines, and ML, a subset of AI, involves algorithms that learn from and make predictions based on data. Leveraging techniques like supervised learning and deep learning models, including convolutional neural networks (CNNs), these technologies excel in recognizing complex patterns within large datasets. Diagnostic accuracies exceeding 95% for conditions like melanoma and basal cell carcinoma highlight their transformative potential, reducing interobserver variability and improving diagnostic standardization [1,2]. Their ability to surpass traditional diagnostic approaches positions AI as a vital tool in modern dermatopathology.

Beyond diagnostics, AI-driven predictive analytics are enabling a shift toward personalized medicine. By integrating histopathological, molecular, and clinical data, machine learning models facilitate risk stratification and identification of therapeutic targets, particularly in aggressive diseases such as cutaneous melanoma. Additionally, AI-powered platforms streamline workflows by automating routine tasks, such as mitotic figure counting, margin assessment, and cellular quantification. As highlighted by Wells et al., these advancements reduce diagnostic turnaround times and enable dermatopathologists to allocate more time to complex cases, ultimately addressing operational inefficiencies and growing demands in resource-limited settings [3]. Furthermore, the continuous refinement of AI-driven models, combined with advancements in digital pathology, is enhancing diagnostic standardization and supporting clinical decision-making. These innovations unveil AI’s growing role as an indispensable tool in modern dermatopathology, bridging advancements with clinical expertise to improve outcomes.

This paper explores the transformative potential of AI and ML in dermatopathology, focusing on their applications in diagnostic precision, predictive analytics, and workflow optimization. It also examines challenges such as algorithmic bias, ethical considerations, and regulatory barriers, proposing strategies to integrate explainable AI systems into clinical practice. By bridging technological innovation with dermatopathological expertise, this study highlights how AI and ML are poised to redefine the field, advancing toward precision diagnostics, personalized care, and enhanced operational efficiency.

DIAGNOSTIC ACCURACY AND PRECISION

Technological Overview

Artificial intelligence (AI) enables machines to perform tasks such as learning, reasoning, and problem-solving, with machine learning (ML) as a key driver of AI innovations in dermatopathology. ML algorithms identify patterns in large datasets and improve through iterative training. Supervised learning, unsupervised learning, and ensemble methods underpin ML’s ability to process complex data. A subset of ML, deep learning (DL), employs multi-layered neural networks, particularly convolutional neural networks (CNNs), to analyze histopathological images with exceptional precision [2]. DL enhances diagnostic accuracy by detecting subtle histopathological features that may be overlooked in traditional assessments. Emerging transformer-based models further expand capabilities in skin cancer staging and therapeutic identification [4]. These advancements contribute to the progress of personalized medicine and promote equitable care delivery in dermatopathology.

Furthermore, training on datasets such as HAM10000 and ISIC allows these models to classify a broad spectrum of skin conditions, including melanoma, with remarkable accuracy [5]. This capability facilitates earlier clinical interventions and improves patient outcomes. For instance, CNNs trained on these datasets have successfully detected nuanced features characteristic of naevoid melanoma, illustrating AI’s potential in dermatopathology [6]. Beyond classification, AI applications extend to malignancy detection, assisting dermatologists in complex diagnoses. While Gomolin et al. emphasize AI’s ability to improve accuracy and streamline workflows, they also highlight challenges such as dataset biases and the need for rigorous validation across diverse populations [7]. Addressing these challenges is crucial for ensuring the reliability and inclusivity of AI-driven tools in dermatopathology.

Figure 1. Comparison of AI, Machine Learning, and Deep Learning [8].

Performance Metrics in AI Diagnostics

Artificial intelligence (AI) systems have consistently achieved diagnostic accuracy rates exceeding 95% for conditions such as melanoma, basal cell carcinoma, and inflammatory dermatoses, as highlighted by Wells et al. [3]. This high precision reduces interrater variability, a critical benchmark for achieving consistent and reliable outcomes in. For instance, Duschner et al. demonstrated that AI models can detect cases of basal cell carcinoma with accuracy levels that significantly minimize diagnostic delays, directly improving patient care timelines [2]. These advancements feature AI’s growing role in dermatological diagnostics, particularly in enhancing early detection strategies. Building on this advancement, AI-powered dermoscopy devices facilitate early malignancy detection, with Witkowski et al. emphasizing their capability to enhance diagnostic efficiency by identifying skin cancer with unparalleled speed and accuracy [9]. As these technologies continue to evolve, they hold the potential to further optimize patient outcomes by enabling faster and more accurate skin cancer diagnoses.

Integrating large, heterogeneous datasets further demonstrates AI’s adaptability across diverse environments. Young et al. reported that advanced architectures, such as convolutional neural networks, achieve robust accuracy by addressing diagnostic biases and maintaining reliability across populations [1]. This ability to enhance diagnostic consistency is further complemented by AI’s capacity to identify intricate pathological features beyond human perception. Additionally, AI systems detect subtle histopathological patterns that human observers often miss, as demonstrated by Cazzato et al., showcasing their superior diagnostic sensitivity [6]. Together, these performance metrics accentuate AI's transformative impact on dermatopathology, setting new benchmarks for accuracy, speed, and patient outcomes while establishing its role as an indispensable tool in clinical practice.

Additional Examples of AI-Driven Diagnostics

AI is revolutionizing dermatopathology by enhancing diagnostic accuracy and efficiency. CNNs, as highlighted by Cazzato and Rongioletti, reduce interobserver variability by identifying intricate histopathological patterns, ensuring more consistent evaluations of conditions like melanoma [10]. Beyond improving diagnostic consistency, AI is also enhancing whole-slide imaging analysis. Cazzato et al. demonstrated AI’s ability to accurately distinguish malignant from benign lesions, enabling earlier interventions and reducing diagnostic delays [11]. These advancements provide clinicians with reliable tools for comprehensive assessments and informed decision-making. Machine learning (ML) models, including segmentation algorithms, further refine diagnostic precision by analyzing tumor microenvironments and offering valuable insights for treatment planning [12]. The integration of AI in dermatopathology bridges the gap between diagnostics and therapeutics, achieving precision comparable to expert pathologists. By detecting subtle histopathological features and supporting therapeutic decisions, AI plays a multifaceted role in advancing dermatological care. As these technologies continue to advance, they promise to redefine diagnostic standards and foster a more patient-centered approach.

PREDICTIVE ANALYTICS

Integration of Multimodal Data with case applications

The integration of histopathological, molecular, and clinical data through artificial intelligence (AI) is fundamentally reshaping dermatopathology by enabling predictive analytics that were previously beyond the realm of possibility. Traditional diagnostic methods rely on visual examination of histological slides, genetic testing, and clinical evaluations in isolation, often missing subtle yet critical correlations across these modalities. AI, however, has the unique capability to synthesize these diverse datasets, constructing a more comprehensive and nuanced understanding of patient health [13]. This holistic approach allows for the detection of intricate relationships between histological patterns, genetic mutations, and clinical presentations that might otherwise go unnoticed. For instance, AI-driven analysis of whole-slide images in conjunction with genomic data and electronic health records enables a level of precision in disease detection and prognosis that surpasses human capability. By leveraging deep learning models, AI can discern minute histopathological features that correlate with specific genetic alterations, facilitating early disease identification and improved therapeutic decision-making [14-16]. As a result, clinicians can tailor treatment plans more effectively, ensuring that high-risk patients receive aggressive intervention while sparing low-risk individuals from unnecessary treatments. This multimodal approach represents a paradigm shift toward precision medicine, where AI functions as an indispensable tool in optimizing patient outcomes.

For instance, in cutaneous melanoma, AI has demonstrated the ability to correlate histopathological characteristics such as tumor thickness, ulceration, and mitotic rate with key molecular markers like BRAF, NRAS, and KIT mutations [17]. These molecular markers not only guide the prognosis but also determine therapeutic responsiveness, particularly in the context of targeted therapy and immunotherapy [18]. AI models, trained on vast datasets, can assess these relationships with a level of precision that surpasses conventional diagnostic methods, enhancing risk stratification and enabling the early identification of aggressive melanoma subtypes. This multimodal approach ensures that patients with high-risk profiles receive timely and tailored interventions while minimizing overtreatment for those with less aggressive disease.

Beyond improving diagnostics, AI is revolutionizing patient management by predicting treatment responses and optimizing personalized therapy strategies. By integrating histopathological features, molecular signatures, and clinical variables, AI algorithms can forecast patient outcomes with remarkable accuracy. Machine learning models have been shown to predict the likelihood of recurrence, metastatic potential, and overall survival rates, providing clinicians with valuable insights for treatment planning. For example, CNNs have been utilized to analyze dermoscopic images and histopathological slides, achieving performance levels comparable to expert dermatopathologists in classifying malignant and benign lesions [19]. Furthermore, AI-based predictive analytics can assist in selecting the most effective therapeutic approach for each patient, whether through checkpoint inhibitors, BRAF/MEK inhibitors, or combination therapies, ultimately improving long-term survival and quality of life [20]​. As AI continues to evolve, its role in dermatopathology will extend beyond diagnostics to proactive disease management, enabling early intervention and personalized treatment strategies tailored to each patient's unique molecular and histopathological profile. This paradigm shifts underscores the need for ongoing research, ethical AI integration, and collaboration between dermatologists, pathologists, and data scientists to refine and enhance AI-driven predictive models for melanoma and other dermatological conditions.

WORKFLOW OPTIMIZATION

Automated Tools

As AI continues to refine diagnostic precision through the correlation of histopathological patterns with molecular markers, its impact extends beyond accuracy, revolutionizing workflow efficiency and streamlining dermatopathology operations through automation. Traditional histopathological assessments, such as mitotic figure counting, margin evaluation, and cellular quantification, are time-consuming that require specialized expertise while also being susceptible to interobserver variability. AI-powered automation provides a scalable and highly precise solution to these challenges, enabling more consistent and reproducible diagnoses [21]. They play an important role in enhancing pathologists’ efficiency, particularly in key areas such as mitotic figure detection, tumor margin assessment, and cellular quantification [22-24]. By employing advanced image recognition algorithms, AI can accurately identify and quantify mitotic figures, a crucial step in grading malignancies and predicting tumor aggressiveness. Additionally, AI-powered tumor margin assessment aids in determining clear surgical boundaries, helping to minimize recurrence risks and guide re-excision decisions. Moreover, AI can perform automated cellular quantification and feature extraction, allowing for the precise identification of dysplastic or malignant cells, improving reproducibility in both clinical and research settings.

Beyond improving diagnostic precision, AI-driven automation streamlines dermatopathology workflows, addressing bottlenecks and expediting case reviews. Faster turnaround times are achieved through AI-assisted image analysis, which accelerates histopathological review by automating repetitive, labor-intensive tasks, enabling pathologists to focus on complex cases [3]. This efficiency also helps alleviate workload-related fatigue and burnout, a growing concern as pathology case volumes continue to rise. Additionally, AI enhances diagnostic consistency by reducing interobserver variability, ensuring standardized evaluations that improve risk stratification and treatment planning, and extending to melanoma classification. Seamless integration of AI into digital pathology platforms allows for remote consultations, second-opinion reviews, and real-time data sharing, fostering improved collaboration across institutions. As AI-driven automation continues to evolve, its role in dermatopathology will expand beyond diagnostics, shaping a future where skin cancer and other dermatological diseases are managed proactively with faster, data-driven interventions, ultimately improving patient outcomes.

ADDRESSING HEALTHCARE INEQUITIES

The global shortage of dermatopathologists disproportionately impacts underserved regions, leading to diagnostic delays and suboptimal patient outcomes. AI-driven solutions offer the potential to address this inequity by extending diagnostic capabilities to areas with limited access to trained professionals. For instance, implementing AI algorithms for specific tasks such as cellular quantification and tissue margin evaluation can significantly reduce the burden on available dermatopathologists, expediting the diagnostic process and enabling them to focus on more complex cases [25]. This increased efficiency is particularly valuable in regions facing a shortage of specialists, where AI can serve as a critical support system in maintaining diagnostic accuracy and timely patient care. Furthermore, AI-powered mobile applications and cloud-based services facilitate remote diagnostics by connecting healthcare providers in rural or resource-limited areas to centralized AI systems for real-time analysis [26]. This democratization of dermatopathology ensures consistent diagnostic quality regardless of geographic location.

AI technologies can further bridge healthcare disparities by enhancing the efficiency of dermatopathology workflows. For example, convolutional neural networks (CNNs) trained on annotated datasets like ISIC have demonstrated high accuracy in identifying histopathological patterns, reducing the reliance on in-person consultations [5]. These tools can also integrate seamlessly into existing healthcare infrastructures, enabling faster turnaround times for diagnoses and promoting early clinical intervention. Importantly, the deployment of AI-driven tools not only improves access but also fosters capacity building by supporting healthcare providers in underserved areas with decision-making aids, ultimately elevating the standard of care across populations.

CHALLENGES AND LIMITATIONS

Algorithmic Bias

The performance of AI models in dermatopathology heavily depends on the diversity of their training datasets. Unfortunately, many of these datasets, such as HAM10000 and ISIC, predominantly feature images from light-skinned individuals, leading to biased algorithms that perform suboptimally for patients with darker skin tones [27]. This lack of representation can exacerbate existing health disparities by increasing misdiagnoses or missed diagnoses in underrepresented populations. For instance, AI models trained on predominantly Caucasian datasets exhibit poor sensitivity for detecting melanoma in darker skin types [28]. This can have severe implications for disease prognosis. To mitigate these biases, researchers must prioritize the creation of global datasets that include diverse skin types and conditions. Strategies such as collaborating with international institutions and leveraging federated learning models can improve data diversity without compromising patient privacy. Additionally, the development of explainable AI systems can help identify biases during deployment, allowing for iterative corrections and more equitable applications in clinical settings.

Regulatory Barriers

Regulatory frameworks such as those established by the FDA and EMA are essential for ensuring the safety and efficacy of AI systems in dermatopathology [29]. However, these frameworks often lag behind the rapid advancements in AI technology, creating barriers to clinical implementation. For instance, the opaque nature of many AI algorithms, often referred to as “black box” models, poses challenges for regulatory approval, as these systems fail to provide interpretable justifications for their outputs. To address these challenges, regulatory bodies need to develop standardized guidelines tailored to AI applications in dermatopathology. This includes incorporating metrics for algorithm explainability and establishing pathways for continuous post-market surveillance to ensure real-world efficacy. Collaborative efforts among developers, clinicians, and regulators can streamline the approval process, fostering the safe and effective integration of AI tools into routine practice [30]. By prioritizing transparency and continuous monitoring, these efforts can ensure that AI systems meet clinical and ethical standards, paving the way for their acceptance and widespread adoption in healthcare.

Ethical Concerns

The integration of artificial intelligence (AI) in dermatopathology raises significant ethical challenges, particularly around data privacy and security. Large datasets are essential for training accurate diagnostic algorithms, but their use reveals the importance of safeguarding sensitive patient information. Robust data governance frameworks are critical to achieving this balance, as they ensure compliance with ethical standards and help build trust in AI systems. For example, McKay et al. highlight how such frameworks can address privacy concerns while supporting the effective use of digital pathology tools [31]. In parallel, the importance of high-quality standardized data in advancing AI capabilities is demonstrated by datasets like HAM10000, widely utilized in dermatology. These datasets are instrumental in advancing AI by providing the foundation for more precise diagnostic algorithms [5]. However, the inconsistent implementation of privacy-preserving measures across healthcare systems creates significant gaps, requiring collaborative solutions. By bringing together clinicians, developers, and policymakers, it is possible to develop scalable strategies that protect patient data while enabling ethical innovation in dermatopathology.

Explainability and Interpretability

The success of artificial intelligence (AI) in dermatopathology depends on developing models that are both accurate and interpretable, fostering trust and accountability in clinical practice. Explainable AI enables clinicians to understand the reasoning behind algorithmic outputs, reducing risks of diagnostic errors and enhancing confidence in its use. Transparency is particularly critical in high-stakes fields like dermatopathology, where diagnostic precision directly affects patient outcomes [32]. Training clinicians to work effectively with AI systems is equally essential, as tools with limited interpretability may hinder adoption and foster skepticism. Addressing ethical concerns, such as algorithmic bias and fairness, is vital to maintaining trust [31]. Collaboration between clinicians and developers is necessary to integrate explainable AI into workflows, tailoring systems to clinical needs and reducing barriers to use. Strategies like robust validation frameworks and continuous clinician education can mitigate challenges such as algorithm variability and bias [33]. By prioritizing interpretability, transparency, and collaboration, explainable AI can improve diagnostic accuracy while adhering to ethical standards, supporting its sustainable adoption in dermatopathology.

FUTURE DIRECTIONS

Development of Explainable AI

The integration of artificial intelligence (AI) into healthcare, mainly through innovations like Explainable AI (XAI), has the potential to transform clinical practice. AI promises to enhance diagnostic accuracy, streamline workflows, and improve patient outcomes, especially in specialties like dermatopathology. However, successful adoption of AI requires attention to interpretability, transparency, standardization, and collaboration. These elements are essential for ensuring AI systems are reliable, safe, and well-integrated into clinical practice [34,35]. Addressing these factors will not only drive innovation but also overcome current barriers to AI adoption in clinical settings, ensuring that AI solutions contribute meaningfully to patient care.

The development of XAI is crucial for regulatory approval and clinical adoption. Interpretable models, such as decision trees and linear regression, provide transparency by making AI’s decision-making process understandable to clinicians and regulators. This transparency is particularly important in high-risk environments, like intensive care units (ICUs), where clear decision-making is essential for patient safety [34]. Interpretability not only fosters trust in AI but also enables clinicians to use AI as a decision-support tool confidently. Additionally, regulatory frameworks, such as the Artificial Intelligence Act, emphasize the need for interpretability, though they currently lack specific standards for explainability, creating ambiguity in implementation. This gap underscores the need for robust interpretability frameworks to ensure safe and effective integration of AI into healthcare [36]. Thus, further development in regulatory clarity is essential for advancing the broader adoption of AI in clinical practice.

Emerging techniques like Local Rule-Based Explanations (LORE) are advancing AI transparency. LORE generates localized, interpretable rules that explain individual predictions, offering a granular understanding of AI outputs. This is particularly valuable in clinical settings like dermatopathology, where understanding the rationale behind specific outcomes can directly impact patient care. By addressing the limitations of traditional global explainability methods, LORE offers a more transparent approach, enhancing clinician confidence in AI systems and promoting smoother integration into clinical workflows [35]. This shift towards more detailed, interpretable models signals a move toward more reliable AI tools in healthcare, making AI more accessible and actionable in everyday clinical practice. As a result, the widespread use of such tools could lead to significant improvements in diagnostic precision.

Standardizing AI in Dermatopathology

In dermatopathology, standardization is essential for ensuring the reliability and consistency of AI systems. Challenges such as biological variations in tissue samples and differences in imaging equipment can significantly impact AI model performance. Huo et al. proposes a framework to address these issues, including automated image quality checks and equipment standardization, which can improve the reliability of AI in digital pathology [37]. Such measures are crucial for ensuring accurate and consistent diagnostic results, fostering trust in AI-driven diagnoses. However, achieving high accuracy is not enough—AI models must also account for sensitivity and specificity to ensure clinical relevance. Models that perform well on common conditions but fail to detect rare but critical diseases could undermine clinical decision-making. Therefore, frameworks that emphasize sensitivity and specificity are essential for ensuring that AI tools support effective patient care [36]. This approach not only improves diagnostic performance but also enhances patient outcomes by ensuring rare but critical conditions are not missed.

Collaboration among regulatory bodies, AI developers, and clinicians is critical for overcoming the challenges of AI adoption in dermatopathology. Effective regulation can mitigate risks related to safety, security, ethical biases, and accountability while promoting fairness and encouraging sustainable AI practices [38]. Regulatory bodies should prioritize patient welfare over commercial interests, ensuring that AI tools are safe and effective. Dermatologists also play a key role in validating and standardizing AI solutions, ensuring they are clinically relevant and align with patient care needs. By fostering collaboration, we can address these challenges and facilitate the widespread adoption of AI technologies in clinical practice, improving both diagnostic accuracy and patient outcomes. This collaborative approach will ultimately lead to the responsible integration of AI into clinical workflows.

Global Health and Equity

AI has the potential to significantly enhance dermatopathology services in low-resource settings by improving diagnostic accuracy and efficiency. Newer deep learning (DL) models like “Derma Care,” aim to overcome limitations of previous work by using a large and diverse dataset of skin images, a CNN that can handle image-based data, and the ability to detect multiple skin diseases simultaneously [39]. It has shown a 96.2% accuracy rate, demonstrating the effectiveness of AI in teledermatology and remote care, especially for managing eczema and psoriasis. Such programs also integrate clinical and imaging data for enhanced reliability, reaching 97.5% accuracy in resource-limited settings, offering a vital advantage in underserved regions. Other innovative tools like Juyal et al.’s proposed “Intelligent Skin Monitoring Device” provide smart vans with low-cost, real-time diagnostics for conditions such as acne and lupus erythematosus [40]. It utilizes Internet of Things (IoT), which allows a network of physical devices that are connected to the internet to collect and share data. This device not only facilitates preventive care but also maps disease prevalence based on environmental factors. It also offers reusability of medical services, inside the van, and cost-effectiveness in terms of the physical infrastructure needed for hospitals [41]. These advancements emphasize the potential of AI and IoT technologies to bridge the gap in dermatological care in underserved areas. By leveraging AI for diagnostic accuracy and integrating IoT for continuous monitoring and data-sharing, these tools could revolutionize dermatology, making specialized care more accessible and sustainable.

Furthermore, AI can aid in the digitization and analysis of histology slides, facilitating remote consultations and education. Innovative workflows have been proposed to create whole-slide images from low-quality videos captured using inexpensive microscopes, making digital pathology more accessible in resource-limited environments [42]. By integrating AI into dermatopathology, low-resource settings can overcome barriers related to the scarcity of specialists and infrastructure, leading to more timely and accurate diagnoses. However, it's crucial to address challenges such as algorithmic bias, data privacy, and the need for local validation to ensure these technologies are effective and equitable [41]. Advancements in DL and AI underscore the transformative potential of AI in ratifying dermatopathology services, particularly in underserved regions where specialist care is limited. By integrating innovative technologies and data-driven models, AI offers a scalable solution to address diagnostic disparities, ultimately improving patient outcomes and advancing global health equity. This convergence of innovation offers a promising pathway toward equitable healthcare delivery, especially in regions with limited access to trained specialists and resources.

Emerging Technologies

In recent years, there has been a growing interest in artificial intelligence in all fields of medicine. Utilizing AI algorithms for diagnosis could transform the world of medicine by improving patient outcomes with more effective and efficient treatment plans. There are a few emerging technologies that could have potential applications in dermatopathology and beyond. One of these technologies is generative AI, designed to produce synthetic data from patterns of already existing data [43]. In the world of dermatology, this could prove beneficial for physician training, offering analysis of skin images and, based on algorithms, identifying skin lesions and abnormalities in patients with high efficiency. Another emerging artificial intelligence approach is federated learning (FL), which uses multiple sites to gather data, but does not exchange the data with those sites, and then collaboratively trains a shared model [44]. This approach proves beneficial in solving the privacy issue that comes with gathering data for research use.

The impact that generative AI and federated learning could have on dermatopathology is revolutionary. In the world of dermatopathology, generative AI is beneficial in enhancing diagnostic precision and accuracy. It can generate high-quality images of skin lesions that may be uncommon to benefit trainees such as medical students, residents, and pathologists. Furthermore, generative AI can detect skin cancers at an early stage to improve patient outcomes. However, limitations exist such as dataset bias, limited population diversity in data, and privacy risks [41]. Federated learning could prove beneficial in fixing these privacy issues that generative AI may have. Since in FL the data is directly added to the model without any data sharing, knowledge can be pooled while minimizing privacy concerns. According to Haggenmuller et al, the FL approach is a viable approach for the classification of invasive melanomas and nevi [45]. This supports the notion that further research needs to be done on other skin conditions to determine the effectiveness of FL in diagnosing them. All in all, the integration of generative AI and federated learning has great promise in the world of dermatopathology. It offers effective and efficient diagnostic capabilities, fosters collaboration, and offers the potential for enhanced patient outcomes.

CONCLUSION

The application of artificial intelligence (AI) and machine learning (ML) dermatopathology is transforming the specialty by enhancing diagnostic accuracy, improving predictive analysis, and optimizing workflows. AI-driven models, particularly models such as convolutional neural networks, have demonstrated efficacy in diagnosing skin pathologies and reducing interobserver variability, thereby standardizing diagnostic practices. Additionally, AI’s integration of histopathological, clinical, and molecular data enables personalized patient management and prognostic assessments, particularly for conditions such as melanoma. Beyond diagnostics, AI-enhanced automation is streamlining routine tasks and allowing dermatopathologists to prioritize complex cases and improving turnaround times. However, several challenges remain, including algorithm bias due to limited training data diversity, regulatory hurdles, and concerns regarding model transparency and data privacy. To truly understand AI’s potential in dermatopathology, interdisciplinary collaboration among dermatopathologists, data scientists, and regulatory bodies is crucial for developing explainable AI systems and establishing clinical guidelines. As AI technology continues to evolve, it will not only enhance diagnostic precision but also reshape personalized treatment strategies and improve accessibility to dermatopathologic expertise worldwide.

ACKNOWLEDGEMENTS

None.

CONFLICTS OF INTEREST

The authors declare that there are no conflicts of interest.

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