A comprehensive literature search was conducted using the PubMed, Scopus, and Embase databases between August 2024 and October 2024. To identify relevant papers addressing patient perspectives on AI and telemedicine in dermatology, we used the following search terms: ((artificial intelligence) AND (dermatology)) AND (comfort OR perception OR perspective), as well as ((Telemedicine OR Teledermatology) AND (dermatology)) AND (comfort OR perception OR perspective).

The initial search yielded 622 papers, which were imported into Covidence for systematic screening. After removing 236 duplicates, 386 studies underwent independent title and abstract review by three researchers (CM, TDZ, and LDS). Conflicts were resolved through discussion until a consensus was reached. Following the initial screening, 60 papers underwent a full-text review, with a focus on original research and data-supported observations. Ultimately, 38 papers met our inclusion criteria: studies addressing patient perspectives or perceptions of AI and telemedicine in dermatology, published after 2009, in English with full text available, and providing empirical data on patient perspectives (Table 1). We limited inclusion to studies published after 2009 to align with the major advances in AI and telemedicine over the past 15 years, during which AI became increasingly integrated into both health care and broader societal applications, and telemedicine saw widespread adoption across global health systems [5,6]. This timeframe was chosen to capture evolving patient expectations and technological standards that more accurately reflect current experiences. Ten additional papers identified through reference list screening of included studies were incorporated to provide background context, bringing the total to 48 papers. Our search revealed no publications directly comparing patient perspectives of AI-integrated telemedicine in dermatology, highlighting a gap in current literature.

Three reviewers independently extracted predefined attributes from each paper. Disagreements were resolved through discussion until a consensus was reached. Literature primarily focusing on technical aspects of AI or telemedicine implementation, without substantial discussion of patient perspectives, was deemed outside the scope of this review.

While our review applied structured screening and thematic synthesis similar to a scoping review, we selected a narrative review approach to enable conceptual interpretation of patient perspectives across diverse study types. This approach allowed us to synthesize findings not only by outcome themes but also by behavioral drivers and contextual patterns. The narrative format also guided our inclusion criteria, enabling us to incorporate both quantitative and qualitative studies that offered insight into patient perceptions, even when methods or outcome measures were heterogeneous. This review was not preregistered, and no formal checklist such as PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) was used, although we followed structured screening procedures to enhance methodological transparency.

This paper is a narrative review and does not involve primary data collection with human or animal participants. Therefore, institutional review board approval and informed consent were not required. The study adhered to the principles of the Declaration of Helsinki and followed JMIR Publications’ ethical guidelines for secondary research and literature reviews.

The literature revealed several distinct themes regarding patient perspectives on AI and telemedicine in dermatology, also known as “teledermatology.” The following sections examine these themes for each technology, including patient willingness to use, perceived benefits and risks, barriers to implementation, and conditions necessary for successful integration into dermatology. Each section compares perspectives between AI and teledermatology while highlighting how these viewpoints might inform future implementation strategies. Tables 2 and 3, located after the Results section, summarize key themes from patient perspectives on AI and teledermatology, respectively. The column categories of these tables were adapted from Kalkman et al [45]. Figure 1 presents a conceptual framework that synthesizes these findings to guide integrated implementation approaches.

The literature shows that patients exhibit hesitancy toward AI-based diagnoses without dermatologist interventions. In a survey conducted by Wu et al [43], if a dermatologist and an AI model made different diagnoses, the majority of patients (119/141, 84.4%) favored the dermatologist’s diagnosis. They also found that about 14.9% (21/141) of patients expressed “complete unwillingness” to be evaluated by AI alone [43]. In contrast, Lim et al [28] found that patients would be happy for their general practitioner to use AI to make dermatologist referral decisions (235/603, 39%).

In suspected skin cancer cases, patient trust in human expertise remained high, with a significant majority (524/603, 86.9%) of patients strongly preferring a dermatologist’s diagnosis, and only 12.1% (73/603) willing to accept a diagnosis made solely by AI [28]. Despite hesitancy toward standalone AI diagnoses, the Wu et al [43] survey data revealed that most patients (96/141, 68.1%) preferred dermatologists to use an AI model that could provide differential diagnoses based on a photograph at the point of care rather than working alone. This pattern suggests patients prefer AI as a decision-support tool that enhances rather than replaces clinical judgment.

Patients’ acceptance of dermatology-based AI tools showed overall mild concerns for data sharing privacy. While Lim et al [28] found that 87% (508/584) of patients were willing to share their patient images for AI training and to help other patients, Ly et al [30] determined that patient comfort levels declined when more facial or sensitive areas were in question. For example, 81% (820/1010) of patients were comfortable with sharing images of their hands, 70% (710/1010) with images of their face, 58% (326/563) with images of male genitals, and 47% (209/447) with female genitals [30].

The willingness to share sensitive or identifiable images was also dependent on the AI tool’s development, and Horsham et al’s study [23] concluded that patients were more willing to share pictures with university-developed tools than those that were industry-developed. The preference for university-developed AI tools over commercially-developed ones likely reflects broader perceptions of institutional trust. Patients may associate academic institutions with stricter data privacy protections, ethical oversight, and transparency in tool development, whereas commercial tools may raise concerns about profit motives and potential misuse of sensitive health information. These perceptions reiterate the importance of transparent development processes, ethical governance, and academic partnerships in cultivating public trust in AI technologies.

Despite the rising popularity of teledermatology appointments, patient preferences for consultation methods remain mixed (Table 3). Balakrishnan et al [14] identified that older age was associated with an increased likelihood of using telemedicine for follow-up appointments, whereas studies by DeVries et al [17] and Choi et al [15] found a negative association between age and willingness to use teledermatology services for follow-up appointments.

In studies with patients who experienced both face-to-face appointments and teledermatology, preferences widely varied. In Gnanappiragasam et al’s study [3] of patients with a mean age of 53.3 years, 61% (97/156) preferred face-to-face for future consultations, while 39% (60/156) did not have a preference for appointment modality. Meanwhile, Hsueh et al [24] reported that 66% (332/503) of veterans, with a 92% (464/503) male population and a mean age of 71 (SD 17) years, preferred teledermatology over face-to-face. Finally, Hadjieconomou [2] had a 72% (34/48) female demographic, with 91.5% (44/48) younger than 65 years and an 8% (4/48) preference for face-to-face visits.

These contrasting findings related to participant age likely reflect context-specific factors. For instance, the high telemedicine acceptance among older male veterans in Hsueh et al’s study [24] may be shaped by functional limitations (such as mobility impairments), structured support from the Department of Veterans Affairs system, or previous exposure to digital tools in service settings. In contrast, Hadjieconomou’s [2] younger, predominantly female population may have expressed reluctance due to concerns about privacy, self-image on video, or decreased rapport during teledermatology consultations. These findings suggest that age, gender, previous technology exposure, and health status may influence teledermatology acceptance through competing functional and technological factors.

Regarding positive feelings toward telemedicine, the prospective study conducted by Lowe et al [29] reported that patients (98%, 41/42) with telemedicine consultations felt their concerns were addressed during consultations. Ford et al’s [18] randomized controlled trial demonstrated that 70% (210/300) believed telemedicine improved care, 27% (82/300) reported no change, and 2% (6/300) perceived a negative impact.

Adding to these findings about patient preferences, Frühauf et al [19] demonstrated strong patient acceptance of teledermatology among patients with psoriasis, with 90% (n=9/10) reporting they felt “in good hands” with remote care while experiencing a more flexible lifestyle. The same study found that 80% (n=8/10) of patients considered teledermatology a viable alternative to in-person consultations, suggesting high levels of patient confidence in remote care delivery [19]. Finally, a small subset of patients in a Dutch study who used chat or email consultations graded their experience as a 9 out of 10 in satisfaction [42].

However, underlying these positive ratings, Table 3 shows that a few studies reported that patients expressed concerns about the lack of patient-physician connection during initial consultations [3,29,44]. This concern appears to be related to patients’ previous experience with technology, as Qun Oh et al [35] found that patients were more likely to decline telemedicine if they had minimal exposure to video conferencing. This technology-related hesitation highlights the importance of gradually exposing patients to telemedicine platforms to build familiarity and comfort with remote care delivery.

A final interesting observation was that patient preferences for telemedicine varied significantly based on their specific dermatological conditions and needs. Handa et al [22] reported that the highest levels of satisfaction (3419/5229, 65.4%) with telemedicine were seen in patients with infectious dermatologic manifestations. However, for chronic disease management, Ford et al [18] found that 65% (195/300) of patients surveyed preferred in-person follow-ups.

Furthermore, findings from a survey by DeVries et al [17] demonstrate that patients with acne had a strong preference for teledermatologic visits, whereas those with possible malignant lesions strongly preferred an in-person visit. Another study comparing video counseling to in-office counseling for acne isotretinoin initiation found no significant difference in patient satisfaction scores across multiple domains, including comfort starting isotretinoin and concerns about side effects [33]. Ruggiero et al [38] reported specific aspects of teledermatology that patients with acne were highly satisfied with, including dermatologist attention (48/52, 92%), quality of time spent with the dermatologist (45/52, 87%), and the treatment received (37/52, 71%). These variations in willingness to use telemedicine reflect patients’ risk assessment preferences, with higher-stakes conditions driving demand for direct physician contact.

Collectively, patient preferences for both AI and teledermatology are influenced by factors such as demographic characteristics, previous technology experience, the level of clinician involvement, and specific dermatological needs. Understanding these preference patterns helps design patient-centered implementation strategies that maximize patient acceptance and engagement.

Many facets of patient care could be impacted by the use of AI in dermatology, and patients’ motivations for its implementation are optimistic. As shown in Table 2, a qualitative study by Nelson et al [1] found that some of the primary patient values of AI relate to its potential for quicker diagnoses (29/48, 60%), greater ease of health care access (29/48, 60%), and increased triage efficiency (14/48, 29%) [1]. Approximately 35% (17/48) also associated AI with reduced health care costs. However, this survey focused on using AI as a skin cancer screening tool, and a majority (32/48, 67%) of participants had a history of melanoma or other skin cancer. Given this context, future research stratifying patient responses by disease history would be valuable.

Additional research by Sangers et al [39] provided valuable insights into the practical benefits that patients associate with using AI, specifically for skin cancer screening. These included the ability to perform skin cancer screenings from home and monitor lesions over time, giving patients a better sense of involvement in their dermatological health care. As provided in Table 3, patients surveyed by Goessinger et al [21] following AI-assisted skin cancer screening reinforced these positive perspectives, believing that AI enhances diagnostic performance (195/205, 95.5%). These patient perceptions of the potential benefits of AI reflect some of AI’s greatest strengths as it integrates into dermatological health care.

Meanwhile, telemedicine addresses several logistical challenges patients face when accessing dermatological care. Efficiency was a primary benefit noted by patients, and Abeck et al [12] reported that the most frequent reason for using teledermatology was shorter waiting times for appointments (103/166, 62%). This finding was reinforced by Pathoulas et al [36], who found that patients preferred a telemedicine visit with a 2‐to 3-week wait time over an in-office visit with a wait longer than an average of 6.89 months.

Table 3 highlights another significant advantage of telemedicine cited by patients across studies—its reduction in travel and parking time and costs [2,3,11-13,18,27,29], which is particularly important for patients living in remote areas to improve access to care (Table 3). Interestingly, 3 of these studies evaluated patient perceptions during the COVID-19 pandemic [11,13,29]. It is possible that many positive opinions were driven by limited in-person care options, further supported by Choi et al [15] finding that telemedicine support increased during the pandemic and then decreased after movement restrictions eased. The advantage of reduced travel time is complemented by telemedicine’s potential to minimize absences from work or school [2]. Patients also appreciate teledermatology’s flexibility and the ability to send dermatological concerns at any time [40]. Uniquely, Hadjieconomou [2] found that 71% (34/48) of patients valued its reduction in the risk of infection exposure, and 55% (26/48) appreciated more feasible family involvement during the telemedicine consultation.

Improved treatment outcomes also emerged as a noteworthy patient-reported benefit of teledermatology. A randomized controlled trial by Ford et al [18] found that telemedicine facilitated better psoriasis management, as patients could submit photos and receive real-time updates to their treatment plans based on disease progression. Similarly, participants from a Swiss questionnaire expressed their positive perceptions toward telemedicine for minor skin problems [31]. A German survey also found that patients believed telemedicine represented a useful and underused screening tool for cosmetic dermatology before physician evaluation [37]. These results demonstrate that, from the patient perspective, teledermatology serves as an effective initial management and screening tool for chronic, minor, and cosmetic dermatological concerns, which may evolve to additional disease contexts as patients build trust with the platform.

Overall, patients report similar perceived benefits in the usage of AI and telemedicine services in dermatologic practice, ranging from improved accessibility to enhanced care outcomes.

Patients perceived the greatest risks of integrating AI into dermatologic practice to be its limited communication abilities and inherent constraints as an algorithmic tool. Table 2 highlights that patients’ primary concerns about AI center on its limited communication abilities, with 40% (19/48) noting the absence of nonverbal communication as key risks [1]. Loss of social interaction (18/48, 38%) was similarly identified as a risk, as patients doubted AI’s ability to respond appropriately to emotional distress [1]. In terms of verbal communication, patients emphasized AI’s limited capacity to provide education or answer follow-up questions [1]. As AI advances in its real-time interaction capabilities, this particular concern may gradually diminish.

Table 2 highlights patients' expressed concerns about potential misdiagnoses by AI, including false negatives and positives, limited training datasets, lack of physical examination, and operator error [1]. As mentioned in the “Willingness to Use” section, patients exhibited hesitancy toward AI’s use as an independent diagnostic tool [28]. These concerns for AI as a diagnostic tool greatly contrast with patient perceived risks of AI as a screening tool, which primarily focused on AI’s lack of empathy [1]. As AI begins playing a larger role in dermatologic care, clearly communicating the intended purpose of the technology—whether for screening or diagnosis—may help alleviate patient hesitancy.

Data security emerged as another potential risk, with patients in the Nelson et al study [1] highlighting concerns about loss of privacy (14/603, 29%) and nefarious use of AI (11/603, 23%). These hesitations are valid and highlight the need for dermatologists to adopt transparent AI tools and proactively communicate their limitations, privacy safeguards, and intended roles in care to build trust and mitigate patient concerns.

In response to teledermatology, patients perceived substandard physical examinations as the greatest risk, and numerous studies reported patient concerns regarding the quality of teledermatology complete skin examinations, especially for those being monitored for skin cancer [3,4,11,27,29,34,41] (Table 3). Respondents to an Alabama survey of 150 patients expressed concerns that doctors might miss critical details in a teledermatology setting (150/235; 63.8%). They also noted that an in-person check might still be needed after teledermatology care (32/235, 13.5%) [40].

Hsueh et al [24] reported similar apprehensions, as shown in Table 3, finding that patients were concerned about improper treatment recommendations due to lower diagnostic accuracy during telemedicine interactions. Qualitative interviews by Chow et al [16] also revealed that camera quality was a key reason why patients were concerned with diagnostic accuracy. Given that some studies describe patient beliefs that AI can improve diagnostic accuracy, reliability, and efficiency [1,25,28], integrating AI with teledermatology could help mitigate concerns about the limitations of remote physical examinations. However, some telemedicine limitations remain beyond AI’s scope, as surveys for parents of patients with pediatric conditions identified that several required laboratory tests and procedures could only be performed in person [27].

Similar to AI-related risks, limited personal elements also contribute to patients’ concerns about reduced quality of care via virtual telemedicine platforms [3,29] (Table 3). Specifically, patients in a UK survey reported that teledermatology consultations lacked nonverbal cues, which led to worse patient-physician understanding and weakened rapport [11]. Similar sentiments were expressed through qualitative feedback on telemedicine experiences in a study by Yadav et al [44], where patients noted concern for the lack of personal touch during the consultation. In a pediatric cohort, parents of patients reported that telemedicine was less effective in easing their anxiety compared to face-to-face visits, and 52% (60/116) of surveyed participants expressed significant dissatisfaction with the telephone clinic [29].

Overall, patients share similar concerns about both AI and teledermatology centered around diagnostic accuracy limitations and reduced human connections (Figure 1). Thus, integration of these technologies must focus on preserving existing human interaction. Teledermatology faces additional scrutiny of physical examination quality, while AI elicits concerns about emotional responsiveness and data security.

Beyond perceived risks, patients identify several practical and trust-related barriers that may limit the successful integration of AI and teledermatology into dermatologic care. Table 2 shows that patients are concerned with poor training datasets for AI and the necessity for clinicians to still interpret AI results to develop effective treatment plans [25]. Patients in qualitative interviews by Sangers et al [39] specifically mentioned that limited knowledge about the use and functionality of AI was a barrier to its integration.

Patient trust in dermatologic AI services may also be undermined by data security risks. As discussed in “Willingness to Use,” patients would much rather share sensitive or identifiable images with university-developed AI versus private industry-developed systems [23]. Notably, only 15% (6/39) of these respondents answered that they had a “high” level of knowledge about AI, as opposed to 72% (28/39) who selected “low” or “moderate.” Therefore, patient perceptions of AI security and privacy could significantly change with increased familiarity with AI technology and its privacy protections.

Finally, a survey on patient acceptance of AI in skin cancer diagnostic pathways revealed that Fitzpatrick skin type strongly influenced patient agreement scores regarding the use of AI to assist their general practitioner (P=.02) [26]. Patients with darker Fitzpatrick skin types IV–VI reported a median agreement score of 44.60 out of 100, significantly lower than those with Fitzpatrick types I (79.89/100), II (81.39/100), and III (74.77/100) [26]. Therefore, AI training datasets must be representative of all skin types to ensure that AI operates equitably and fosters trust among patients.

Regarding teledermatology, patients mentioned technological limitations as the primary barrier to adoption [3,20,32,34] (Table 3). This barrier contrasts with the main perceived risk relating to substandard physical examinations, suggesting that it may be more difficult to circumvent technological limitations. One survey revealed that patients with lower satisfaction scores were significantly more likely to have experienced technical difficulties or to perceive their teledermatology-based physical examination as unsatisfactory [32]. The literature shows that, for patients with limited digital literacy, nonuser friendly or uninviting teledermatology platforms may exacerbate challenges with teledermatology visits [18,29]. Therefore, clinicians using teledermatology must ensure that digital services are simple to navigate and offer extensive troubleshooting for technology-related problems.

Other logistical barriers emerged for teledermatology use and adoption. Table 3 includes 2 studies noting that patients often struggled to take photos of hard-to-reach areas for teledermatology visit evaluation, occasionally requiring assistance from others [18,40]. This finding expands on previous data that describes patients’ hesitancy to share images of certain body parts [30], further indicating that image location plays an important role in patient comfort. In addition, a retrospective survey on telephone consultations found that teledermatology visits were less preferred by patients due to reduced natural social interactions and the clinicians’ inability to physically examine the patients’ skin [11]. Table 3 also highlights patients’ emphasis on the importance of visually seeing the clinician as a care preference [11].

In summary, barriers to both AI and teledermatology adoption stem from certain technological limitations and trust concerns and are important to address before technological convergence or implementation.

For AI and teledermatology to be successfully integrated into dermatological care, patients have distinct considerations and requirements. Patients emphasize the need for dermatologist oversight for AI system model validation and to ensure that AI would not replace human discussion [1,25,28,43]. Current AI models that use patient images have brought up concerns about the adequacy of existing guidelines and policies around AI [23]. Consequently, patients desire safeguards and transparency of the tools to guarantee clear AI privacy policies, secondary uses of data, and AI’s effectiveness [23,28,46]. As shown in Table 2, patient acceptance of AI is heavily contingent on demonstrated superiority, with patients requiring AI to be a mean of 12.9% (SD 8.1%) more accurate than dermatologists before accepting standalone AI evaluations [43]. Most importantly, patients indicated that endorsement from their dermatologist and government regulatory bodies would promote their acceptance of AI use in dermatological care [39].

To improve the adoption of teledermatology services, patients outlined several practical and operational factors surrounding their appointments. For example, patients expressed that preappointment teledermatology educational materials and adequate follow-up care systems are important considerations for use [4,24]. Technical difficulties were frequently cited as a reason for preferring face-to-face consultations [3]. For this reason, patients noted that high-quality images and video should be required for their visit [3,4].

Like the concerns surrounding AI’s safeguards and transparency, measures for building trust in telemedicine were important considerations for using telemedicine platforms [20]. Finally, patients emphasized the importance of dermatologists addressing accessibility barriers, including the patient’s ability to navigate technology for telemedicine appointments [3,4], to ensure equitable care across diverse populations. This highlights important implications for health equity as teledermatology continues to expand.

Collectively, these findings suggest that patients may embrace AI and teledermatology only with appropriate safeguards, such as transparency about technological limitations, adequate educational resources, clear privacy policies, and, most importantly, continued dermatologist involvement that preserves the human elements of care. Figure 1 illustrates the interconnected drivers of patient receptivity, perceived benefits, and risks and provides a framework for integrated implementation strategies that address patient concerns while leveraging the complementary strengths of both technologies.

This narrative review of 48 studies revealed that patients exhibit distinct perspectives on AI and telemedicine in dermatology, with both technologies showing complementary strengths that could enhance dermatologic care delivery. While both technologies reduce wait times, they achieve this through different mechanisms—AI uses automated diagnostics and data analysis [1,28], whereas telemedicine minimizes logistical barriers, such as travel and appointment times [2,3,13,18,29]. This distinction is significant because it suggests implementation strategies should consider each technology’s unique advantages rather than applying a one-size-fits-all approach.

In addition, the sources of trust differ notably between the technologies. AI trustworthiness depends heavily on professional oversight, as patients strongly prefer AI that includes a dermatologist [28,43], and patients require AI models to be more accurate than dermatologists before they would feel comfortable with AI-only evaluations [43]. In contrast, telemedicine’s trustworthiness stems from patients’ confidence in its diagnostic capabilities for specific conditions, with studies showing high acceptance rates for certain dermatologic issues such as acne and infectious manifestations [17,22,38], while patients consistently preferred in-person evaluations for potentially malignant lesions [17]. Overall, patient acceptance of both technologies depends on perceptions of convenience, accessibility, and care quality, but concerns about privacy, data security, and remote consultation efficacy can impede implementation.

Our review also demonstrates how behavioral and contextual factors play a pivotal role in shaping patient receptivity to these technologies. Patterns of receptivity to AI and telemedicine appear closely tied to patient self-efficacy and contextual factors such as disease type, previous technology exposure, and institutional trust. For instance, patients managing chronic but nonsevere conditions such as acne or psoriasis often reported high satisfaction with teledermatology [18,19,38], which may reflect their familiarity with self-management practices. In contrast, patients with suspected malignancies or limited technology access expressed greater reluctance [15,17,35], likely reflecting both the high-stakes nature of cancer diagnosis that demands maximum clinical certainty and the digital barriers that prevent confident engagement with remote platforms.

These acceptance patterns are further complicated by the intersection of disease severity with demographic factors such as age and gender. Certain dermatologic conditions are more prevalent within specific demographic groups, such as acne more frequently and severely affects adolescent males [38]. These patterns raise the possibility that differences in technology acceptance based on disease severity may be confounded by underlying demographic factors. However, our review highlights the complex relationship between age and willingness to adopt the implementation of AI and telemedicine into dermatologic practice [14,17,30,36,43]. While younger patients who have more familiarity with technology may be more accepting of AI and telemedicine, they may also exhibit more hesitancy due to data privacy concerns [1,16,23,34,39,40]. This ambiguity indicates that technology acceptance is multifaceted and reflects the interplay between numerous clinical and demographic factors.

As the technological revolution expands, the convergence of AI and telemedicine in health care may become inevitable. These technologies may no longer remain distinct but instead function in tandem to provide a more integrated approach to care. Our results support that this integration should be gradual, and that AI and telemedicine should not replace traditional face-to-face services, but rather complement them [1,17,18,28,43].

While this simultaneous integration may exacerbate shared concerns—such as data privacy, loss of human interaction, and diagnostic accuracy—it also offers avenues to mitigate the risks inherent to each technology. For instance, AI tools could address concerns regarding the quality of teledermatology by standardizing image quality assessment and supporting diagnostic accuracy [8,9], while teledermatology’s real-time communication capabilities could mitigate patient concerns about AI’s lack of human interaction [1]. This synergy has the potential to enhance diagnostic accuracy, optimize resource allocation, and prioritize a patient-centered approach that maintains both technological efficiency and essential human touchpoints in health care delivery. As shown in Figure 1, successful integration requires addressing the overlapping concerns while capitalizing on each technology’s unique strengths through targeted implementation strategies.

It should be noted that, as dermatology care increasingly incorporates AI and telemedicine, attention to digital equity is paramount to prevent the widening of existing disparities. Several studies identified disparities in access, literacy, and comfort across demographic groups, and these findings foreshadow the implementation challenges discussed in the 4 paragraphs below, where the very populations that could benefit most from technological access may face the greatest barriers to adoption.

As our results outline, equity concerns surrounding AI center on algorithmic bias and representation, as evidenced by patients with darker Fitzpatrick skin types showing significantly lower acceptance of AI tools [26]. Meanwhile, equity concerns with teledermatology primarily relate to geographic and digital literacy disparities. Generally, patients with longer travel distances to the clinic are more accepting of telemedicine services, though for some patients, this preference may reflect travel or financial barriers rather than true choice [14,40]. Patients living in rural areas may particularly benefit from the increased convenience of web-, photo-, and app-based dermatology appointments but are simultaneously at a heightened risk of experiencing digital literacy and technology-related challenges [3,5,32,34,35].

Across both technologies, the introduction of new digital platforms without adequate support may disproportionately disadvantage individuals with limited digital literacy, including older adults, lower-income populations, and patients with lower education levels [14,17,30,35,36,39,43]. Without deliberate attention to inclusivity, the integration of these technologies risks reinforcing, rather than reducing, gaps in care access and quality. To address these equity concerns and increase patient acceptance across both technologies, implementation strategies must prioritize representative AI training datasets, intuitive telemedicine interfaces, transparent communication about both technologies’ capabilities and limitations, and patient-centered education.

Our review highlights that patients view successful integration of both technologies as requiring dermatologist oversight and the preservation of meaningful patient-provider relationships [3,43]. Therefore, offering ongoing resources and support throughout the integration process may help address patient concerns and maximize comfort with the platforms. Once these technologies are implemented, clinicians can begin by offering patients resources on how to use the AI or telemedicine service during in-person consultations, explaining how the technology works and emphasizing its role as a complementary tool that augments traditional care models [28,47]. Practices could also connect patients with follow-up resources, such as public libraries or help desks, to help train them on basic digital skills for health-oriented patient technology and empower them to engage fully with the services.

Beyond technical training, building trust necessitates transparent communication about security protocols and the protection of personal health information [23,46]. As the literature explains, endorsement from dermatologists alongside these trust-building measures may enhance patient acceptance of these technologies [39]. Ultimately, the success of these technologies in dermatologic care depends on thoughtful implementation that balances technological advancement with patient-centered care delivery. By ensuring that integration strategies align with patient concerns and expectations, these innovations can maximize their potential to improve access, efficiency, and quality in dermatologic care. The integrated framework shown in Figure 1 offers a roadmap for achieving this patient-centered technological convergence.

Although previous research has examined AI and telemedicine as separate modalities, little is known about how patients perceive their integration. Our search identified only 2 studies that addressed both technologies, and neither evaluated patient perspectives on their combined use. This likely reflects the novelty of such tools and the limited availability of integrated, patient-facing deployments during the review period. As these technologies evolve, future research focusing on patient trust and comfort with AI-augmented teledermatology will be important for guiding patient-centered implementation.

In conclusion, patient perspectives surrounding AI and telemedicine in dermatology provide central considerations for clinical implementation. While patients value the benefits of improved access to care and reduced wait times, they continue to have concerns about data privacy, diagnostic accuracy, and maintaining meaningful doctor-patient relationships. These perspectives are especially important for informing health care accessibility in dermatology. As AI and telemedicine potentially converge in dermatologic care, balancing technological advancements with patient-centered care delivery should drive responsible implementation strategies. Research examining how patients experience these modalities together could guide health care systems in harnessing their complementary strengths, while continued investigation will be essential to understand how these technologies can best address patient needs in dermatologic care, both independently and in combination.