Optimizing Skin Quality via AI-Enhanced Physical Activity

Abstract

Genetic predisposition, environmental factors, lifestyle choices, and physical activity influence skin quality. Regular exercise has well-documented benefits for skin physiology, including enhanced microcirculation, improved collagen synthesis, oxidative stress reduction, and modulation of inflammatory pathways. However, individual responses to physical activity vary significantly, depending on skin type, age, fitness level, and environmental exposures. Recent advances in artificial intelligence (AI) offer new opportunities for tailoring exercise programs to meet individual skin health needs. Wearable sensors and smart fitness devices provide real-time data on physiological responses (e.g., heart rate, sweat rate, and oxidative stress) and environmental parameters (e.g., UV exposure and pollution levels). AI algorithms process this data to create dynamic, adaptive exercise routines designed to maximize skin benefits while minimizing potential harm (e.g., exercise-induced oxidative stress in sensitive skin types). This review synthesizes the current evidence on the skin benefits of exercise while exploring the emerging role of AI-driven personalized physical activity as a novel tool in cosmetic dermatology. Integrating AI into fitness planning, personalized, non-invasive skincare strategies may complement traditional topical and procedural approaches, representing a step forward in precision dermatology.

1. Introduction

Skin quality is influenced by a complex interplay of internal and external factors, including genetic predispositions, exposure to ultraviolet (UV) radiation, environmental conditions such as pollution and climate, and nutritional habits. Among these, the incorporation of regular physical activity into daily life is a lifestyle choice that significantly impacts overall skin health and appearance [1].

The wide-ranging benefits of physical activity on human health are extensively documented in the scientific literature. Regular exercise has been linked to improved cardiovascular performance, enhanced mental well-being, and better metabolic control. More recently, research has begun to emphasize the positive influence of physical activity on skin physiology. These effects include improved circulation, which promotes the efficient delivery of oxygen and essential nutrients to skin cells, as well as the more effective removal of metabolic waste products from the skin [1]. Furthermore, physical activity triggers various biochemical and hormonal responses—such as increased collagen production, enhanced antioxidant activity, and overall improvements in skin structure—which together contribute to a delay in the visible signs of aging [2]. In addition to these anti-aging effects, recent studies suggest that regular exercise may also alleviate symptoms in individuals with chronic skin conditions like psoriasis and eczema by helping to reduce systemic inflammation. However, the extent of these benefits is not uniform across all individuals and can vary significantly based on factors such as skin type, age, environmental influences, and the intensity and duration of physical activity [1].

Despite the clearly demonstrated advantages of exercise for skin wellness, the outcomes are highly individualized, largely due to the diverse nature of physical activity and personal physiological differences. As a result, personalized fitness plans that consider variables such as an individual’s skin characteristics, physical condition, and age may lead to more effective and targeted improvements in skin health. In this context, artificial intelligence (AI) emerges as a powerful tool with transformative potential in the fields of wellness, fitness, and skincare. AI-powered skin analysis technologies are capable of assessing skin features such as texture, moisture content, elasticity, and pigmentation, enabling the delivery of customized solutions tailored to each person’s unique needs [3]. Moreover, AI-driven fitness platforms can generate personalized workout plans by adjusting exercise intensity, frequency, and duration. These platforms can also track important metrics such as hydration status, environmental exposure (e.g., UV levels or humidity), and physical exertion, all of which have a direct impact on the skin’s condition and resilience [4]. Real-time analysis of biometric data is another key capability of AI systems, allowing for precise control of exercise load, personalized recovery recommendations, and maximization of health- and skin-related outcomes [3,4,5].

When combined with wearable sensors and smart devices, AI facilitates continuous monitoring and interpretation of physical activity data [6]. The integration of sensor data and intelligent algorithms enables real-time feedback and adaptive adjustments, supporting more effective and personalized health interventions. In the context of skincare, AI-guided workouts can monitor changes in skin hydration and temperature during physical activity [2,7]. This information can be used to modify workout intensity or duration and adjust skincare routines accordingly, helping to preserve the skin barrier and maintain overall skin homeostasis [4,8]. AI also plays a pivotal role in managing oxidative stress caused by intense or prolonged physical activity, which is one of the leading contributors to premature aging. By fine-tuning exercise regimens to maintain a balance between enhanced blood circulation and controlled production of free radicals, AI helps mitigate the negative impacts of oxidative stress on the skin [1]. The use of artificial intelligence in personalized fitness and skincare programs represents an innovative and rapidly evolving field, with significant applications in both cosmetic treatment and clinical dermatology [3,9].

This study aims to explore the connection between physical activity and skin wellness in depth. It also seeks to assess the role of artificial intelligence in enhancing exercise planning and to evaluate how AI-assisted fitness technologies may offer a non-invasive, tech-enabled approach to improving skin health, appearance, and longevity. By integrating knowledge from the domains of sports science, dermatology, and digital health, this research examines the potential of AI-enhanced physical activity to promote healthier, more youthful skin and to delay the onset of age-related skin changes.

2. Materials and Methods

An integrative narrative review methodology was adopted to explore the emerging intersection between artificial intelligence (AI), physical exercise, and dermatological health. This approach combined a structured literature search with thematic synthesis of findings across the fields of dermatology, exercise physiology, and AI-driven personalization systems.

To identify relevant sources, an extensive search was conducted across multiple electronic databases, including PubMed, Scopus, IEEE Xplore, Google Scholar, and ResearchGate. The search targeted publications from 2010 to 2024, with particular focus on recent advances published between 2019 and 2024 to ensure up-to-date coverage of technological developments.

Articles were selected based on their contribution to one or more of the following thematic areas:

• The physiological impact of physical activity on skin health, including mechanisms such as improved microcirculation, collagen synthesis, reduced oxidative stress, and modulation of inflammation.
• The application of artificial intelligence in designing, adapting, and monitoring personalized exercise protocols using real-time biometric and environmental data.
• The integration of AI technologies in dermatological diagnostics, monitoring, or wellness optimization.

Only peer-reviewed publications—including original research, systematic reviews, and meta-analyses—were included. Editorials, commentaries, and non-peer-reviewed sources were excluded. Articles were assessed for relevance, methodological quality, and applicability to the research aims. A total of 92 publications were identified, of which 61 met the inclusion criteria.

Although no clinical trials currently validate AI-personalized exercise specifically for dermatological outcomes, this review synthesizes the most relevant evidence from adjacent disciplines to propose conceptual frameworks for future empirical exploration. Proposed exercise personalization approaches are based on correlational data—such as heart rate variability, hydration, and environmental stressors—that may indirectly relate to skin physiology. However, these associations have not yet been clinically confirmed using dermatological outcome measures such as transepidermal water loss (TEWL), skin elasticity, or inflammatory biomarkers. Future research should include randomized controlled trials and molecular endpoint assessments to determine the causal effects of AI-driven physical activity on dermatological health.

3. Results

3.1. Effects of Exercise on Skin Health

The beneficial effects of regular physical activity on skin health are extensive, well-documented, and supported by a growing body of scientific evidence. These benefits arise from a complex interplay of cellular, metabolic, and physiological processes that are positively influenced by exercise. Physical activity contributes significantly to enhanced microcirculation, improved transport of oxygen and nutrients throughout the body, regulation of oxidative stress, reduction of chronic inflammation, and stimulation of collagen synthesis—all of which are essential for maintaining healthy, youthful-looking skin.

In particular, Kruk’s research [10] emphasized the crucial role of exercise in mitigating oxidative stress and reducing inflammatory mediators, both of which are known contributors to premature skin aging. Additional studies have confirmed that consistent physical activity strengthens the body’s antioxidant defense systems, thereby decreasing inflammatory processes and exerting a positive influence on overall skin health and resilience [11].

When examining the skin’s structural integrity and functional performance, researchers such as McLoughlin et al. [12] and Oizumi et al. [13] demonstrated the positive impact of exercise on skin elasticity, collagen biosynthesis, and the function of the epidermal barrier. Moreover, the meta-analysis conducted by Lanting et al. [14] confirmed that regular physical activity significantly enhances microvascular reactivity and vasodilation, leading to improved delivery of oxygen and nutrients to skin cells, which are critical for repair and regeneration. These effects are further detailed in

Table 1. Effects of Exercise on Skin Health.

Authors	Study Focus	Key Findings
Kruk J. 2007 [10]	Exercise and chronic disease	Reduces oxidative stress and inflammation
Cho C, et al., 2004 [11]	Blood flow restriction aerobic exercise	Enhances antioxidant mechanisms and skin health
McLoughlin EC, et al., 2022 [12]	Exercise and skin function	Supports skin elasticity and vascular health
Oizumi R et al., 2024 [13]	Epidermal barrier	Improves skin elasticity and epidermal barrier
Lanting MS et al., 2017 [14]	Exercise and microvascular reactivity	Confirms improved oxygen/nutrient delivery to skin
Palmer JA et al., 2022 [15]	Blood flow post-exercise	Indicates potential for improved skin circulation
Fuertes-Kenneally L, et al., 2023 [16]	HIIT and vascular function	Improves microcirculation and skin perfusion
McIntosh MC, et al., 2024 [17]	Resistance training and vascular function	Increases capillary permeability and skin vascularity
Lee J, et al., 2024 [18]	Collagen synthesis and resistance exercise	Boosts collagen with hydrolyzed collagen intake
Proksch E, et al., 2014 [19]	Collagen peptides and skin	Improves skin hydration and elasticity
Heinemeier KM, et al., 2007 [20]	Exercise and IGF-I	Stimulates regeneration via growth factors
Langton AK, et al., 2010 [21]	Elastic fibers and skin aging	Emphasizes importance of elastic fiber integrity
Tominaga K, et al., 2012 [22]	Astaxanthin and exercise	Combined benefits on skin quality
Yeh CJ, et al., 2022 [23]	Exercise and skin diseases	Improves psoriasis and alopecia via cytokine reduction
Conti P, et al., 2023 [24]	Exercise and immune response	Reduces oxidative stress in skin diseases
El Assar M, et al., 2022 [25]	Exercise and aging	Improves microcirculation and skin regeneration

, which presents a summary of key studies and their respective findings.

The investigation conducted by Palmer et al. [15] provides insights into the rapid increase in cerebral blood flow following moderate-intensity exercise, which suggests a broader enhancement of peripheral circulation, including improved blood flow to the skin. Similarly, the study by Fuertes-Kenneally et al. [16] reveals that high-intensity interval training (HIIT) has a profound impact on vascular function, significantly boosting skin microcirculation and perfusion.

Furthermore, McIntosh et al. [17] found that resistance training contributes to increased capillary permeability, resulting in enhanced skin vascular function and nutrient exchange. This vascular enhancement is especially beneficial for skin cell regeneration and overall dermal health. In terms of skin composition, both the study by Lee et al. [18] and the clinical trial by Proksch et al. [19] confirm that physical activity promotes collagen synthesis. When combined with hydrolyzed collagen supplementation, exercise leads to measurable improvements in skin hydration, firmness, and elasticity.

Additionally, the role of exercise in stimulating the production of growth factors, such as insulin-like growth factor I (IGF-I), has been highlighted by Heinemeier et al. [20]. These growth factors promote tissue regeneration and support skin cell renewal. Langton et al. [21] emphasized the importance of preserving elastic fibers—structures essential for maintaining skin resilience and preventing sagging—which are indirectly protected and supported by regular physical activity.

The synergistic benefits of exercise and antioxidant supplementation were investigated by Tominaga et al. [22], who explored the combined effect of astaxanthin and physical activity on skin quality. Their findings underscore the potential of combining targeted nutrition with exercise for enhanced dermatological outcomes.

In addition to promoting skin health in healthy individuals, research has also demonstrated exercise’s therapeutic potential in managing chronic skin disorders. Yeh et al. [23] reported improvements in inflammatory conditions such as psoriasis and androgenetic alopecia, attributed to reduced levels of pro-inflammatory cytokines. Likewise, Conti and Gallenga [24] found that physical activity can alleviate symptoms of various skin diseases by reducing oxidative stress and inflammation. Finally, El Assar et al. [25] confirmed that exercise enhances microcirculation and supports the skin’s regenerative capacity—both critical factors in maintaining skin vitality during the aging process.

In conclusion, the collective findings from the recent literature reinforce the notion that regular physical activity—whether in the form of aerobic training, resistance workouts, or high-intensity interval sessions—positively influences skin health on multiple levels. By enhancing blood flow, stimulating collagen production, regulating inflammation, and improving the delivery of nutrients to the skin, exercise serves as a highly effective and natural approach to skin rejuvenation. It offers a drug-free, preventive strategy for delaying the visible signs of skin aging and promoting overall dermatological well-being.

3.2. Exercise and Artificial Intelligence

The rapid advancement of artificial intelligence (AI) technologies has brought transformative changes to the fields of physical activity, sports performance, and rehabilitation. AI is increasingly being integrated into the design, monitoring, and implementation of exercise programs, offering new possibilities for personalization, precision, and safety. Through the use of data-driven algorithms and machine learning, AI systems are capable of analyzing complex physiological and environmental variables to optimize exercise outcomes for individuals across different age groups and fitness levels [26,27].

One of the most impactful applications of AI is its ability to prevent and predict physical injuries. This is achieved by analyzing biomechanical data, movement patterns, and historical health records, thereby allowing the creation of targeted exercise programs that not only enhance performance but also reduce the risk of injury. Personalized exercise regimens, tailored to an individual’s needs and conditions, promote safer and more effective training experiences. These programs can also dynamically adapt to real-time data, offering ongoing adjustments that account for fatigue, recovery status, and external factors [26].

In their study, Canzone et al. [5] emphasized the critical role of artificial intelligence in creating highly individualized training programs, highlighting the importance of real-time data acquisition in making informed decisions throughout the training process. The integration of biometric and contextual data allows AI to personalize fitness plans, ensuring that each recommendation aligns with the user’s physical capabilities and goals. Similarly, Xu et al. [27] explored how conversational AI platforms such as ChatGPT-4.0 can be linked with specialized information systems to design personalized training programs. Their findings showed that AI could develop safe and reliable exercise routines by accurately assessing users’ physical condition, performance metrics, and personal preferences. These findings are summarized in

Table 2. Exercise and Artificial Intelligence.

Authors	Study Focus	Key Findings
Nitish N, et al., 2029 [26]	AI guidance in health	Navigation-based personalized health and quality of life improvement
Xu Y, et al., 2024 [27]	ChatGPT and personalized exercise	AI systems can create tailored exercise plans based on user profiles
Canzone A, et al., 2025 [5]	AI in exercise program design	Real-time data enables personalization and decision-making
Fang J, et al., 2024 [28]	Digital health and goal setting	ML improves personalized exercise goal setting
Schoeppe S, et al., 2016 [29]	Apps for physical activity	AI-supported apps improve diet and activity tracking
Zhan C. 2024 [30]	AI in injury rehabilitation	AI creates adaptive rehab plans using video/sensor data
Zou R. 2025 [31]	Injury prevention and rehab	AI predicts injury risk and supports safe return to sport
Kakavas G, et al., 2020 [32]	Sports trauma prediction	AI predicts injuries using athlete history and condition
Bartlett R. 2006 [33]	Biomechanics and AI	AI enhances diagnosis and rehab monitoring
Reis FJJ, et al., 2024 [34]	AI in sports medicine	AI uses data from diagnostic tools
Desa V, et al., 2024 [35]	AI and return to play	AI supports decision-making in rehabilitation
Smaranda AM, et al., 2024 [36]	AI in ECG analysis	AI reshapes ECG analysis for athlete safety
Pareek A, et al., 2025 [37]	AI in Sports	Outlines AI’s current and future roles in sports injury management

.

Artificial intelligence enables continuous feedback and real-time monitoring of exercise sessions, ensuring the user receives timely and accurate insights to enhance performance and reduce risk [28,29]. Mobile applications equipped with AI functionality are capable of tracking every aspect of an athlete’s physical activity, from heart rate and sleep quality to hydration levels and stress markers. These metrics allow the system to adapt exercise plans accordingly, providing a highly personalized approach that can be followed and adjusted day-to-day. The result is a dynamic program that evolves with the user, supporting continuous improvement and injury prevention [29].

Equally significant is the growing role of AI in the rehabilitation of sports-related injuries. The study conducted by Zhan [30] demonstrated that artificial intelligence can create fully customized rehabilitation protocols by processing data from motion sensors, video analysis, and patient history. These adaptive programs are capable of identifying delays in recovery or irregularities in progress, allowing healthcare providers or users to make timely interventions and modifications.

The research by Zou [31] adds further support to the preventive capabilities of AI by showcasing its ability to detect early signs of overtraining or biomechanical inefficiencies that could lead to injury. AI-driven systems not only forecast potential risks but also provide guidelines for a safe return to physical activity. Kakavas et al. [32] underscore this point by illustrating how AI tools assess an athlete’s medical and performance history to accurately predict susceptibility to injuries. These capabilities play a vital role in modern sports science, where the goal is not only to optimize performance but also to ensure longevity in athletic careers.

The study by Bartlett [33] further highlights the revolutionary impact of AI in diagnosing movement-related disorders and monitoring rehabilitation progression. Through motion capture and biomechanical analysis, AI can detect subtle changes in performance that may indicate inefficiencies or potential complications.

In the realm of sports medicine, researchers like Reis et al. [34] and Desa [35] explored how AI incorporates data from diagnostic technologies such as electrocardiograms (ECG) and force measurement tools (dynamometers) to inform clinical decision-making. These tools help clinicians assess readiness for return to sport with greater accuracy and confidence.

Smaranda et al. [36] and Pareek et al. [37] focused on AI’s potential to redefine ECG analysis and detect musculoskeletal vulnerabilities, which is crucial for managing athletes’ health and minimizing the risk of sudden injury. Their findings emphasize the importance of integrating AI in health surveillance and performance optimization.

In summary, the collective findings across numerous studies suggest that artificial intelligence plays an increasingly vital role in the development of effective and safe exercise programs. With its capacity to process large volumes of data, adjust plans in real time, and predict potential risks, AI is poised to become a foundational element in the future of personalized fitness, athletic rehabilitation, and injury prevention. As AI continues to evolve, its contributions to sports science and digital health are expected to grow, offering more precise, data-informed strategies for enhancing both performance and long-term well-being.

3.3. Artificial Intelligence and Dermatology

A comprehensive evaluation of 21 recent studies was conducted to explore the evolving role and growing significance of artificial intelligence (AI) in the field of dermatology. These studies collectively demonstrate how AI technologies are transforming skin diagnostics, treatment planning, and patient care. One of the most notable applications involves the use of image-based machine learning algorithms to detect and classify various skin conditions—including lesions, melanomas, and different types of skin cancer—with remarkable precision. The accuracy of these AI models has been shown to be comparable to, and in some cases even surpass, that of experienced dermatologists, as demonstrated in the influential studies by Esteva et al. and Brinker et al. [38,39]. These findings are summarized in

Table 3. Artificial intelligence and dermatology.

Authors	Study Focus	Key Findings
Esteva A, et al., 2017 [38]	Skin cancer classification	AI matches dermatologist-level accuracy
Brinker TJ, et al., 2029 [39]	Melanoma classification	AI outperforms dermatologists in image classification
Janda M, et al., 2017 [40]	Melanoma diagnosis automation	High sensitivity useful in low-access settings
Han SS, et al., 2028 [41]	Onychomycosis diagnosis	Deep learning matches expert diagnosis
Liu Y, et al., 2020 [42]	Differential diagnosis of skin diseases	AI diagnoses 26 conditions with expert-level accuracy
Tschandl P, et al., 2020 [43]	Human-AI collaboration	Physician + AI improves diagnostic performance
Mohan J, et al., 2025 [44]	Transformer models in dermatology	Enhances accuracy and explainability
Omiye JA, et al., 2023 [45]	Explainable AI	Improves trust and clarity in diagnosis
Malalur Rajegowda G, et al., 2024 [46]	AI skincare in XR	93% accuracy in skincare recommendation
Zhou J, et al., 2023 [47]	SkinGPT-4	Visual LLMs for dermatological diagnostics
Panagoulias DP, et al., 2024 [48]	Tele-dermatology	AI supports decision-making via multi-modal data
Cortes J, et al., 2024 [49]	Physician attitudes on AI	Interest in AI chatbots despite ethical concerns
Liopyris K, et al., 2022 [50]	Challenges in dermatology AI	Discusses biases and regulation needs
Gomolin A, et al., 2020 [51]	AI in dermatology Overview	Evaluates current AI applications
Hogarty DT, et al., 2020 [52]	Future of AI in dermatology	Reviews applications and prospects
De A, et al., 2020 [53]	AI use in Indian dermatology	Highlights AI’s expanding role
Busik V. et al., 2024 [54]	AI and LLMs in dermatology	Reviews current LLM applications
Alwahaibi N, et al., 2025 [55]	Skin biopsy techniques	Discusses AI’s impact on diagnostics
Hirani R, et al., 2024 [56]	AI in healthcare evolution	Historical and futuristic view on AI in care
Li Z, et al., 2022 [57]	Dermatology image analysis	Overview of AI trends and developments

.

The diagnostic accuracy of AI in dermatology has been especially promising in settings with limited access to specialists. For instance, in underserved or rural areas where dermatological expertise may not be readily available, AI can provide high-sensitivity diagnostic tools that support early detection and intervention [38,40]. In the specific case of onychomycosis, Han et al. [41] demonstrated that deep learning networks were able to diagnose nail infections with a level of accuracy comparable to that of expert dermatologists.

Moreover, AI has been employed in differential diagnosis, enabling the accurate classification of 26 distinct dermatological conditions. These AI-driven systems not only match specialist performance but often outperform general practitioners in diagnostic comprehensiveness and precision [42]. Such developments underscore the potential of AI to bridge gaps in expertise, reduce diagnostic delays, and improve patient outcomes.

Importantly, AI is not intended to replace clinical judgment but rather to augment and support it. A pivotal study by Tschandl et al. [43] showed that when AI was used in conjunction with physicians, diagnostic accuracy significantly improved compared to the performance of either party working alone. This synergy reflects a future model of care in which AI acts as a collaborative assistant rather than a substitute.

Advancements in explainable AI (XAI) and transformer models, as reported by Mohan et al. [44] and Omiye et al. [45], further enhance the interpretability of AI outputs. These technologies increase clinician trust by providing transparent reasoning for decisions, thereby making AI not only more accurate but also more acceptable in clinical environments.

The application of AI in personalized skincare has also gained attention. Malalur Rajegowda et al. [46] reported that AI models used in extended reality (XR) environments can deliver skincare recommendations with up to 93% accuracy. This high degree of precision affirms AI’s potential to guide customized skincare regimens, improving efficacy and user satisfaction.

Simultaneously, the emergence of large language models (LLMs), such as SkinGPT-4 [47], has enabled more complex integrations between AI systems and clinical workflows, particularly in teledermatology. These tools enhance remote consultations by synthesizing visual and textual data, enabling better patient follow-up and diagnostic support across digital platforms [48].

The increasing interest in AI among healthcare professionals, especially dermatologists, has also been well-documented. Cortes et al. [49] found that many practitioners are optimistic about the use of AI-based chatbots to enhance patient communication and engagement, although concerns remain regarding algorithmic transparency, ethical use, and data security.

Despite these encouraging developments, challenges still persist. Studies by Liopyris et al. [50] and others [51,52,53,54,55,56,57] have highlighted significant issues related to heterogeneous datasets, algorithmic biases, and the urgent need for ethical guidelines and regulatory oversight. These challenges underscore the importance of standardized training datasets, inclusive algorithm development, and robust clinical validation to ensure equitable outcomes for all patient populations.

In conclusion, artificial intelligence is rapidly becoming a powerful tool in dermatology, revolutionizing how skin conditions are diagnosed, monitored, and managed. While AI shows exceptional promise in terms of accuracy, personalization, and accessibility, its full integration into clinical practice requires ongoing evaluation, transparency, and a commitment to ethical standards. The future of AI in dermatology lies in harmonizing technological innovation with human expertise to provide safer, faster, and more inclusive skin care.

4. Discussion

4.1. AI-Augmented Exercise for Skin Health

Artificial intelligence (AI) has rapidly become a transformative element in healthcare, significantly enhancing diagnostic accuracy, personalized medicine, and real-time monitoring. In dermatology, AI has shown remarkable utility, particularly in the classification of skin conditions via imaging and longitudinal disease monitoring. However, its integration with physical activity to improve skin health is an emerging, underexplored field that combines the physiological benefits of exercise with the adaptive capabilities of AI. Literature from 2010–2015 was included selectively to provide foundational scientific context—particularly regarding the well-documented physiological mechanisms through which exercise benefits skin health (e.g., improved microcirculation, collagen synthesis, reduced inflammation). These early insights remain essential in explaining how physical activity modulates skin function and provide the basis for integrating AI-enhanced interventions.

The dermatological benefits of exercise—such as improved microcirculation, enhanced collagen production, reduced inflammation, increased skin hydration and elasticity, and its role in managing chronic skin conditions like psoriasis, atopic dermatitis, and acne—are well-documented [10,11,12,23]. These findings are summarized in

Table 4. Exercise, Artificial Intelligence, and Skin Health.

Topic	Information
Effects of Exercise on Skin	- Improved microcirculation - Enhanced collagen synthesis - Reduced inflammation - Antioxidant activity - Improved skin elasticity and hydration - Beneficial for chronic skin conditions (psoriasis, atopic dermatitis, acne)
Use of AI in Dermatology	- Image analysis for diagnosis and disease monitoring - Detection of early lesions and signs of aging - Personalized treatment recommendations via systems like Skin GPT-4 and Dermacen Analytica - Use of Explainable AI (XAI) for transparent and trustworthy decision-making
Combining Exercise and AI for Skin Health	- Biometric and physiological data analysis through wearable devices - Customized workout plans based on real-time data (e.g., hydration, skin temperature) - Prevention of irritation and dryness by regulating exercise intensity/duration - Predictive models identifying exercise-related flare-ups (e.g., acne) - Regulation of cortisol (stress hormone) levels through exercise
Collaboration and Future Directions	- Collaboration between doctors, developers, and re searchers - Skin quality as an indicator of overall health - Development of new therapeutic protocols combining AI and exercise - Integration with biosensors and advanced wearable technologies

.

AI platforms enhance these benefits by enabling personalized exercise regimens tailored in real time to individual biometric responses. Supported by wearable technologies, these systems track hydration, heart rate variability (HRV), skin temperature, and more, adjusting exercise intensity and duration dynamically to avoid adverse effects and support skin health [27,58]. AI-guided feedback loops also raise user awareness of the link between physical activity and dermatological outcomes, optimizing the therapeutic potential, especially in chronic skin conditions (Figure 1).

These physiological effects are mediated by systemic mechanisms, including improved endothelial function, enhanced antioxidant capacity, and modulation of the hypothalamic-pituitary-adrenal (HPA) axis [13,24]. AI-based systems strengthen these outcomes by personalizing exercise regimens based on real-time data such as perceived exertion, skin conductivity, and environmental conditions. This personalization may be particularly effective in individuals with reactive skin types or inflammatory disorders, where small deviations in physical load can significantly affect skin homeostasis [10,12,52].

Moreover, AI can assist in early detection of exercise-induced skin responses by analyzing patterns across multimodal datasets. For instance, coupling physiological data with environmental variables, like UV exposure and pollution levels, enables AI to recommend adaptive strategies for protecting the skin during physical activity [27,59]. Such integration promotes not only skin resilience but also long-term dermatological stability, particularly in urban populations where environmental stressors are more prevalent [52,58]. Earlier studies (2010–2015) were selectively included to establish foundational physiological mechanisms of exercise-induced skin benefits, such as improved microcirculation, reduced inflammation, and collagen synthesis, which remain relevant to current research frameworks [10,11,12].

4.2. Technical Limitations and Ethical Considerations of AI-Enhanced Wearables in Dermatology

Despite their potential, wearable devices used in AI-enhanced skin health monitoring face important limitations. Most commercial wearables are not specifically designed for dermatological assessments and rely on indirect markers such as skin temperature, hydration levels, or electrodermal activity to infer skin-related physiological states [27,58]. Common examples include smartwatches, skin patches, hydration sensors, and electrodermal trackers, devices popular in fitness and wellness contexts but lacking dermatology, specific calibration, or clinical accuracy [59].

Environmental variables such as humidity, physical activity, and sweat can further distort sensor readings. Moreover, inter-user variability, based on factors like skin tone, anatomical site, and phototype, exacerbates the challenge of standardization and consistent data interpretation [52]. These devices typically lack integration with high-resolution dermatoscopic imaging, molecular biomarker detection, or real-time photometric analyses, which are essential for clinical dermatology. Therefore, interpretation of wearable-derived data should be approached with caution and, where possible, validated through clinical-grade instruments or laboratory methods. Future research must focus on the development of dermatology-specific biosensors and the establishment of calibration protocols that reflect ethnic and phototypic diversity [50]. In the context of AI-driven fitness personalization for skin-related outcomes, algorithmic bias poses a serious risk to equitable care. The lack of representational diversity in training datasets may lead to flawed adjustments in exercise protocols that are not optimal—or even counterproductive—for individuals with underrepresented skin types. A critical challenge in AI-based skincare is algorithmic bias, particularly when applied to underrepresented populations. Most AI models are trained on datasets lacking sufficient ethnic, phototypic, and dermatological diversity, potentially resulting in misclassification or suboptimal recommendations. To mitigate these risks, future systems must prioritize inclusive training datasets, undergo fairness audits, and be clinically validated across diverse demographic groups. Incorporating XAI frameworks also promotes equitable decision-making and user confidence [50].

Beyond technical shortcomings, the use of AI in continuous biometric monitoring introduces significant ethical and privacy concerns. The collection and real-time processing of sensitive physiological data—including hydration, skin barrier status, and stress markers—requires stringent safeguards. A privacy-by-design framework is critical. This involves transparent informed consent processes, data minimization strategies (collecting only what is necessary), and end-to-end encryption to secure data both at rest and in transit [46,48].

Compliance with data protection regulations such as the General Data Protection Regulation (GDPR) is not only a legal obligation but also an ethical imperative. Users must be informed about the scope and purpose of data use, with the right to opt out or withdraw consent. Moreover, explainable AI (XAI) methodologies should be embedded to clarify how algorithmic decisions are made—especially when recommendations may influence clinical or cosmetic dermatological interventions [44,50].

To build long-term trust, independent ethics committees, regular audits, and user-centered controls should become integral components of AI-supported dermatology platforms. Privacy and algorithmic integrity must evolve alongside technological progress, ensuring that personalization never compromises autonomy or dignity.

4.3. Ethical and Regulatory Roadmap for AI in Dermatology

To responsibly implement AI-driven systems in dermatological contexts, a robust ethical and regulatory foundation is necessary. We propose the adoption of a privacy-by-design framework, which emphasizes informed user consent, data minimization, secure encryption protocols, and strict compliance with international data protection regulations such as the General Data Protection Regulation (GDPR).

In addition, large-scale deployment of biometric monitoring technologies should be accompanied by ethical oversight boards and independent third-party audits. These safeguards aim to ensure user autonomy, data integrity, and public trust in AI-driven dermatological tools.

Finally, the integration of explainable AI (XAI) methodologies can further enhance algorithmic transparency, particularly when AI-generated outputs influence skin-related exercise adjustments or therapeutic decisions. Together, these measures offer a comprehensive ethical and regulatory roadmap for the safe and equitable adoption of AI in personalized skin health interventions.

4.4. Monitoring Oxidative Stress and Stress-Related Skin Conditions

AI platforms offer important preventive potential, particularly in managing flare-ups associated with stress-related dermatological disorders such as acne and eczema. These systems rely on real-time physiological and environmental data—such as heart rate variability (HRV), hydration, perceived exertion, UV exposure, and pollution levels—to infer stress responses and dynamically adjust exercise regimens [10,12,24,52]. Although cortisol is a known biomarker associated with inflammation-related skin flare-ups, as current AI systems do not directly monitor hormonal levels, any impact on cortisol-mediated responses remains hypothetical and is inferred through stress-related surrogate markers such as HRV and perceived exertion. However, as noted by Conti and Gallenga [24], while exercise effectively lowers cortisol, a hormone linked to inflammatory skin conditions, current AI systems do not directly measure oxidative stress biomarkers.

Instead, these systems employ machine learning models trained to recognize patterns from indirect surrogate markers, offering predictive estimates of oxidative stress risk rather than definitive diagnoses. This makes the distinction between adaptive (beneficial) and pathological (harmful) oxidative stress a persistent challenge. Integration of advanced biosensors capable of detecting reactive oxygen species (ROS) or inflammatory biomarkers in sweat, interstitial fluid, or skin would significantly improve diagnostic precision in future iterations.

4.5. Evidence for Adherence and Dermatological Impact

Long-term adherence to therapeutic protocols is critical for sustainable dermatological improvement. AI-driven systems have already shown success in other domains by promoting consistent engagement through personalized feedback, goal setting, and progress tracking. Studies such as those by Liopyris et al. [50] and Alowais et al. [59] emphasize the importance of continuous monitoring for maintaining treatment adherence and optimizing outcomes.

Nevertheless, direct empirical data connecting AI-personalized exercise programs to measurable skin improvements, such as hydration, elasticity, and barrier integrity, remain limited. Future randomized controlled trials (RCTs) will be vital to assess the dermatological effectiveness and behavioral sustainability of such interventions. The psychological benefits mentioned are based on observational associations and theoretical models, not validated psychometric evaluations—an important limitation to address in future studies.

4.6. Innovative Applications and Collaboration Needs

To better understand the technological backbone of these systems, it is helpful to consider the AI models commonly applied in related health optimization contexts. Emerging platforms like SkinGPT-4 and Dermacen Analytica illustrate how visual data combined with physiological inputs can provide early warnings for skin deterioration. SkinGPT-4 integrates visual large language models (LLMs) to assist in dermatological diagnostics [47], while Dermacen Analytica uses multi-modal data and machine learning for teledermatology support [48]. These systems exemplify how AI can extend beyond reactive diagnostics to offer predictive insights based on multi-source inputs.

Although this review does not validate a specific AI model, it builds upon established architectures in adjacent fields. Supervised learning techniques, including decision trees and support vector machines, have been widely implemented in personalized fitness applications, while convolutional neural networks (CNNs) are central to dermatological image classification. Recent work also shows promise in using transformer-based architectures for explainable image analysis in dermatology [44,45].

Future research should explore integrating these models into multimodal frameworks that personalize exercise regimens while simultaneously monitoring and predicting dermatological responses. For instance, AI-enhanced skincare systems in extended reality (XR) environments have achieved up to 93% accuracy in delivering personalized skincare recommendations, reinforcing the feasibility of adaptive, technology-supported protocols [46].

However, while these AI systems can provide daily, scalable, and personalized feedback, they are not a replacement for clinical expertise. Dermatologists offer irreplaceable value in diagnosis, treatment selection, and handling complex or atypical cases. Therefore, a hybrid model combining AI-based personalization with professional oversight may offer the most comprehensive and effective approach—particularly in preventive care or routine skincare management.

Interdisciplinary collaboration among dermatologists, biomedical engineers, data scientists, and ethicists remains essential for the safe, equitable, and clinically meaningful implementation of these AI tools.

4.7. Review Limitations

This article is a narrative review, not a systematic one, and does not follow PRISMA guidelines. Although some studies conceptually associate improved skin appearance with enhanced psychological well-being and self-esteem, such links in the reviewed literature remain theoretical. Validated psychometric tools were not employed in the studies cited, and future research should integrate standardized psychological assessment methods to objectively evaluate this relationship. While it offers a broad interdisciplinary perspective, several statements are based on preliminary evidence, theoretical models, or findings from adjacent fields (e.g., cardiometabolic or rehabilitation AI systems) due to the current lack of clinical trials directly addressing AI-personalized exercise in dermatology. Moreover, most reviewed studies do not systematically control for confounding factors such as diet, skincare product use, stress levels, or environmental pollutant exposure—each of which may independently influence skin health outcomes. These uncontrolled variables limit the ability to isolate exercise as the primary factor contributing to dermatological improvement. Readers are advised to interpret such findings with caution. Future systematic reviews and randomized controlled trials (RCTs) should explicitly control for these confounders to isolate the specific dermatological effects of AI-personalized exercise interventions. Such evidence will be instrumental in establishing evidence-based guidelines and integrating AI-personalized exercise into routine dermatological practice [1,2,3,4,5,6,10,11,12,23,24,27,44,50,59].

5. Conclusions and Future Directions

The convergence of artificial intelligence and physical exercise represents a promising new frontier in dermatological care—one that extends well beyond traditional treatment paradigms. While the skin-enhancing benefits of exercise are already well-established, the integration of AI enables a smarter, more dynamic, and highly personalized approach. By adapting exercise routines in real time based on individual physiological data, AI transforms physical activity from a general health recommendation into a precision-driven therapeutic tool. AI facilitates targeted interventions, continuous biometric monitoring, and individualized feedback—contributing not only to improved skin quality but also to enhanced psychological well-being and long-term quality of life.

While direct empirical studies combining AI-guided exercise and dermatological outcomes are currently lacking, this review proposes a conceptual synthesis based on foundational evidence from each field and outlines a framework for future validation through clinical trials. This narrative review outlines a conceptual framework for integrating AI into dermatological wellness. Future research must focus on clinical validation, algorithm fairness, and long-term adherence to ensure safe, equitable, and effective implementation in practice.