Jeanelle Jacobs

Recent data suggest that approximately 20% to 40% of patients treated with isotretinoin have recurrence of acne. How should dermatologists interpret these findings?

DR. BARBIERI: While isotretinoin is highly effective and capable of delivering long-term remission, we should be careful to avoid describing it as a “cure” when counseling patients. Importantly, when acne does recur, it is often milder, and about half of those who have acne recurrence can be managed with topicals alone. For those who do require a subsequent course of isotretinoin, we should view this as an outcome that can be expected to happen in about 1 in 10 treated with isotretinoin rather than a treatment failure.

How important is cumulative dose in preventing relapse, and should we be rethinking traditional dosing targets?

DR. BARBIERI: Cumulative dose is one of the most important factors in preventing recurrence. Multiple studies support that higher cumulative dose is a strong predictor of long-term clearance. In contrast, daily dose does not seem to be as important a factor. However, higher cumulative dose also means longer courses and more potential for adverse effects, including long-term skin and eye dryness. For this reason, I prefer to treat to clinical endpoints of clear skin for 2 to 3 months and at least 120 to 150 mg/kg cumulative dose to balance achieving high cumulative doses with potential adverse effects and risks. For those with fewer adverse effects or who prioritize long-term clearance, we might go a little longer and for those with more adverse effects, we might use a shorter course and accept a higher risk for recurrence. By taking this approach, we can individualize our dosing approach to each patient.

What factors most strongly predict relapse after a completed isotretinoin course?

DR. BARBIERI: Some demographic factors that have been associated with higher rates of recurrence include greater baseline severity and younger age at treatment. Women with a strong hormonal component to their acne, such as those with polyendocrine metabolic ovarian syndrome (formerly polycystic ovary syndrome), also may be more likely to have recurrence. With respect to clinical factors, increasing cumulative dose has been associated with reduced risk for recurrence in multiple studies, and treating until a clinical endpoint of clear skin for 2 to 3 months also may be predictive of long-term clearance.

When a patient relapses, how do you decide between topical therapy, hormonal treatment, or a second isotretinoin course?

DR. BARBIERI: It depends on relapse severity and patient goals. Mild recurrence often responds well to topical therapies such as retinoids, benzoyl peroxide, antibiotics, and clascoterone. About half of those with recurrence will be able to manage it with topical therapies alone. For those with more severe acne requiring systemic therapy, about half will decide on a repeat course of isotretinoin, which I often find works faster and better than the first course. For second courses, I will typically try to use micronized isotretinoin due to the more consistent pharmacokinetics. For women—especially those with signs of hyperandrogenism such as hirsutism, irregular periods, or flaring with menstrual cycle—hormonal therapy such as combined oral contraceptives or spironolactone can be a great option. Oral antibiotics also can be a consideration for those with recurrence, though we need to be thoughtful about antimicrobial stewardship and risks of long-term antibiotic use.

Are low-dose or shorter-course regimens contributing to higher relapse rates?

DR. BARBIERI: While there is some evidence that higher daily doses may be associated with lower risk for recurrence, when you control for cumulative dose, it doesn’t seem like daily dose has much influence. In contrast, cumulative dose has a large effect on frequency of long-term clearance. While I don’t think low-dose regimens are inherently problematic, if they result in shorter cumulative dose courses, that could increase the risk for recurrence.

How does hormonal acne influence long-term outcomes after isotretinoin?

DR. BARBIERI: While all acne is “hormonal,” those with a stronger hormonal pathogenesis, such as women with polyendocrine metabolic ovarian syndrome or other signs of hyperandrogenism, may have a higher likelihood of recurrence after treatment. In these patients, I often find hormonal therapy such as combined oral contraceptives or spironolactone to be highly effective, even if they haven't worked before.

Should maintenance therapy be routine after isotretinoin, and if so, what strategies are most effective?

DR. BARBIERI: Since many patients have a goal of long-term clearance after isotretinoin, I do not routinely recommend maintenance therapy, as this seems antithetical to this goal. However, for those who are very concerned about recurrence or who would like to be on a topical retinoid for other reasons, I will sometimes start a topical retinoid after treatment with isotretinoin.

How should dermatologists counsel patients about expectations with respect to relapse before starting isotretinoin?

DR. BARBIERI: We should be careful to set appropriate expectations with isotretinoin. I counsel patients that isotretinoin is an incredibly effective therapy for severe acne, with a high likelihood of long-term remission, but not a guaranteed permanent cure. Setting this expectation upfront reduces disappointment if acne does recur and improves shared decision-making.

Recent data suggest that approximately 20% to 40% of patients treated with isotretinoin have recurrence of acne. How should dermatologists interpret these findings?

DR. BARBIERI: While isotretinoin is highly effective and capable of delivering long-term remission, we should be careful to avoid describing it as a “cure” when counseling patients. Importantly, when acne does recur, it is often milder, and about half of those who have acne recurrence can be managed with topicals alone. For those who do require a subsequent course of isotretinoin, we should view this as an outcome that can be expected to happen in about 1 in 10 treated with isotretinoin rather than a treatment failure.

How important is cumulative dose in preventing relapse, and should we be rethinking traditional dosing targets?

DR. BARBIERI: Cumulative dose is one of the most important factors in preventing recurrence. Multiple studies support that higher cumulative dose is a strong predictor of long-term clearance. In contrast, daily dose does not seem to be as important a factor. However, higher cumulative dose also means longer courses and more potential for adverse effects, including long-term skin and eye dryness. For this reason, I prefer to treat to clinical endpoints of clear skin for 2 to 3 months and at least 120 to 150 mg/kg cumulative dose to balance achieving high cumulative doses with potential adverse effects and risks. For those with fewer adverse effects or who prioritize long-term clearance, we might go a little longer and for those with more adverse effects, we might use a shorter course and accept a higher risk for recurrence. By taking this approach, we can individualize our dosing approach to each patient.

What factors most strongly predict relapse after a completed isotretinoin course?

DR. BARBIERI: Some demographic factors that have been associated with higher rates of recurrence include greater baseline severity and younger age at treatment. Women with a strong hormonal component to their acne, such as those with polyendocrine metabolic ovarian syndrome (formerly polycystic ovary syndrome), also may be more likely to have recurrence. With respect to clinical factors, increasing cumulative dose has been associated with reduced risk for recurrence in multiple studies, and treating until a clinical endpoint of clear skin for 2 to 3 months also may be predictive of long-term clearance.

When a patient relapses, how do you decide between topical therapy, hormonal treatment, or a second isotretinoin course?

DR. BARBIERI: It depends on relapse severity and patient goals. Mild recurrence often responds well to topical therapies such as retinoids, benzoyl peroxide, antibiotics, and clascoterone. About half of those with recurrence will be able to manage it with topical therapies alone. For those with more severe acne requiring systemic therapy, about half will decide on a repeat course of isotretinoin, which I often find works faster and better than the first course. For second courses, I will typically try to use micronized isotretinoin due to the more consistent pharmacokinetics. For women—especially those with signs of hyperandrogenism such as hirsutism, irregular periods, or flaring with menstrual cycle—hormonal therapy such as combined oral contraceptives or spironolactone can be a great option. Oral antibiotics also can be a consideration for those with recurrence, though we need to be thoughtful about antimicrobial stewardship and risks of long-term antibiotic use.

Are low-dose or shorter-course regimens contributing to higher relapse rates?

DR. BARBIERI: While there is some evidence that higher daily doses may be associated with lower risk for recurrence, when you control for cumulative dose, it doesn’t seem like daily dose has much influence. In contrast, cumulative dose has a large effect on frequency of long-term clearance. While I don’t think low-dose regimens are inherently problematic, if they result in shorter cumulative dose courses, that could increase the risk for recurrence.

How does hormonal acne influence long-term outcomes after isotretinoin?

DR. BARBIERI: While all acne is “hormonal,” those with a stronger hormonal pathogenesis, such as women with polyendocrine metabolic ovarian syndrome or other signs of hyperandrogenism, may have a higher likelihood of recurrence after treatment. In these patients, I often find hormonal therapy such as combined oral contraceptives or spironolactone to be highly effective, even if they haven't worked before.

Should maintenance therapy be routine after isotretinoin, and if so, what strategies are most effective?

DR. BARBIERI: Since many patients have a goal of long-term clearance after isotretinoin, I do not routinely recommend maintenance therapy, as this seems antithetical to this goal. However, for those who are very concerned about recurrence or who would like to be on a topical retinoid for other reasons, I will sometimes start a topical retinoid after treatment with isotretinoin.

How should dermatologists counsel patients about expectations with respect to relapse before starting isotretinoin?

DR. BARBIERI: We should be careful to set appropriate expectations with isotretinoin. I counsel patients that isotretinoin is an incredibly effective therapy for severe acne, with a high likelihood of long-term remission, but not a guaranteed permanent cure. Setting this expectation upfront reduces disappointment if acne does recur and improves shared decision-making.

Recent data suggest that approximately 20% to 40% of patients treated with isotretinoin have recurrence of acne. How should dermatologists interpret these findings?

DR. BARBIERI: While isotretinoin is highly effective and capable of delivering long-term remission, we should be careful to avoid describing it as a “cure” when counseling patients. Importantly, when acne does recur, it is often milder, and about half of those who have acne recurrence can be managed with topicals alone. For those who do require a subsequent course of isotretinoin, we should view this as an outcome that can be expected to happen in about 1 in 10 treated with isotretinoin rather than a treatment failure.

How important is cumulative dose in preventing relapse, and should we be rethinking traditional dosing targets?

DR. BARBIERI: Cumulative dose is one of the most important factors in preventing recurrence. Multiple studies support that higher cumulative dose is a strong predictor of long-term clearance. In contrast, daily dose does not seem to be as important a factor. However, higher cumulative dose also means longer courses and more potential for adverse effects, including long-term skin and eye dryness. For this reason, I prefer to treat to clinical endpoints of clear skin for 2 to 3 months and at least 120 to 150 mg/kg cumulative dose to balance achieving high cumulative doses with potential adverse effects and risks. For those with fewer adverse effects or who prioritize long-term clearance, we might go a little longer and for those with more adverse effects, we might use a shorter course and accept a higher risk for recurrence. By taking this approach, we can individualize our dosing approach to each patient.

What factors most strongly predict relapse after a completed isotretinoin course?

DR. BARBIERI: Some demographic factors that have been associated with higher rates of recurrence include greater baseline severity and younger age at treatment. Women with a strong hormonal component to their acne, such as those with polyendocrine metabolic ovarian syndrome (formerly polycystic ovary syndrome), also may be more likely to have recurrence. With respect to clinical factors, increasing cumulative dose has been associated with reduced risk for recurrence in multiple studies, and treating until a clinical endpoint of clear skin for 2 to 3 months also may be predictive of long-term clearance.

When a patient relapses, how do you decide between topical therapy, hormonal treatment, or a second isotretinoin course?

DR. BARBIERI: It depends on relapse severity and patient goals. Mild recurrence often responds well to topical therapies such as retinoids, benzoyl peroxide, antibiotics, and clascoterone. About half of those with recurrence will be able to manage it with topical therapies alone. For those with more severe acne requiring systemic therapy, about half will decide on a repeat course of isotretinoin, which I often find works faster and better than the first course. For second courses, I will typically try to use micronized isotretinoin due to the more consistent pharmacokinetics. For women—especially those with signs of hyperandrogenism such as hirsutism, irregular periods, or flaring with menstrual cycle—hormonal therapy such as combined oral contraceptives or spironolactone can be a great option. Oral antibiotics also can be a consideration for those with recurrence, though we need to be thoughtful about antimicrobial stewardship and risks of long-term antibiotic use.

Are low-dose or shorter-course regimens contributing to higher relapse rates?

DR. BARBIERI: While there is some evidence that higher daily doses may be associated with lower risk for recurrence, when you control for cumulative dose, it doesn’t seem like daily dose has much influence. In contrast, cumulative dose has a large effect on frequency of long-term clearance. While I don’t think low-dose regimens are inherently problematic, if they result in shorter cumulative dose courses, that could increase the risk for recurrence.

How does hormonal acne influence long-term outcomes after isotretinoin?

DR. BARBIERI: While all acne is “hormonal,” those with a stronger hormonal pathogenesis, such as women with polyendocrine metabolic ovarian syndrome or other signs of hyperandrogenism, may have a higher likelihood of recurrence after treatment. In these patients, I often find hormonal therapy such as combined oral contraceptives or spironolactone to be highly effective, even if they haven't worked before.

Should maintenance therapy be routine after isotretinoin, and if so, what strategies are most effective?

DR. BARBIERI: Since many patients have a goal of long-term clearance after isotretinoin, I do not routinely recommend maintenance therapy, as this seems antithetical to this goal. However, for those who are very concerned about recurrence or who would like to be on a topical retinoid for other reasons, I will sometimes start a topical retinoid after treatment with isotretinoin.

How should dermatologists counsel patients about expectations with respect to relapse before starting isotretinoin?

DR. BARBIERI: We should be careful to set appropriate expectations with isotretinoin. I counsel patients that isotretinoin is an incredibly effective therapy for severe acne, with a high likelihood of long-term remission, but not a guaranteed permanent cure. Setting this expectation upfront reduces disappointment if acne does recur and improves shared decision-making.

How might AI enhance the detection of key histologic features in drug eruptions compared to traditional microscopy?

DR. BRIDGES: AI offers the potential to enhance detection of histologic features in drug eruptions by systematically analyzing entire whole-slide images. Convolutional neural networks and attention-based models can identify subtle or focal findings such as scattered dyskeratotic keratinocytes, focal spongiosis, early interface change, rare eosinophils, or microvascular injury, which may be overlooked during routine microscopy due to sampling limitations. This capability is particularly relevant in drug eruptions, where histologic changes often are heterogeneous and patchy.

AI-generated attention heatmaps can highlight diagnostically relevant regions across the slide, improving consistency and completeness of slide reviews. While AI has demonstrated high sensitivity and specificity in broader dermatopathology tasks, particularly neoplastic conditions, drug eruption–specific validation data are currently lacking. As such, the most realistic application at present is AI functioning as a sensitivity-enhancing adjunct or “second reader,” improving consistency and completeness of slide review while preserving expert human interpretation.

Which histologic patterns in drug eruptions are hardest to quantify, and how could AI help standardize their assessment?

DR. BRIDGES: AI-based image analysis can standardize the assessment of histologic patterns through objective reproducible quantification. Deep learning algorithms can segment epidermal and dermal compartments, identify inflammatory cell types, and calculate metrics such as eosinophil density per unit area, percentage of epidermis with vacuolar alteration, or number of affected vessels. Studies in quantitative immunohistochemistry demonstrate high accuracy for tissue segmentation and cell counting, suggesting feasibility for similar applications in inflammatory dermatopathology. While these tools would not replace diagnostic interpretation, they could provide standardized measurements that enhance reproducibility and improve clinicopathologic correlation.

What training challenges must be addressed in AI and drug eruption histology?

DR. BRIDGES: Training AI models for drug eruption histopathology faces several challenges, including the limited availability of high-quality, well-annotated datasets, as most existing AI dermatopathology research focuses on neoplastic conditions. Drug eruptions also exhibit marked histologic heterogeneity, ranging from spongiotic and lichenoid to vasculitic and cytotoxic patterns, often with significant overlap. Accurate labeling, therefore, requires robust clinicopathologic correlation, including medication history, timing, laboratory data, and clinical outcomes—information that is often incomplete or retrospective.

Inaccurate or inconsistent annotations can significantly degrade model performance, and expert disagreement in borderline cases further complicates the creation of reliable ground truth. Additionally, training data may reflect institutional or demographic biases, risking unequal performance across patient populations. Addressing these challenges will require multicenter collaboration, standardized annotation protocols, inclusion of diverse patient cohorts, and careful attention to bias mitigation. At present, these barriers place drug eruption AI firmly in the investigational rather than clinical domain.

How important is AI explainability in the interpretation of diagnostic suggestions?

DR. BRIDGES: Explainability is essential for trust, particularly in the evaluation of drug eruptions, where diagnostic decisions can have serious clinical consequences. Dermatopathologists must understand which histologic features are driving an AI model’s assessment to ensure that conclusions align with morphologic reality and clinicopathologic reasoning. Explainable AI tools (such as attention heatmaps, feature importance rankings, and methods like Shapley Additive Explanations or Local Interpretable Model-Agnostic Explanations) can help clarify which histologic features are driving the AI model’s assessment.

Without transparency, AI systems function as “black boxes,” limiting their utility in high-stakes settings where diagnostic accountability and clinical communication are paramount. Explainability also supports appropriate skepticism, allowing pathologists to recognize when model outputs may be unreliable due to artifacts, atypical patterns, or out-of-distribution cases. In cases of drug eruptions—where diagnosis relies on combining histology, clinical timing, and medication history—explainability is essential for proper use.

How could AI pattern recognition be integrated into your workflow to enhance diagnostic efficiency and accuracy? What safeguards would be required?

DR. BRIDGES: In the near term, AI pattern recognition can be useful as an assistive tool rather than a diagnostic authority. One potential application is pre-screening whole-slide images to flag cases with features such as prominent interface change, increased keratinocyte necrosis, eosinophil-rich infiltrates, or vascular injury, prompting expedited review in clinically concerning scenarios. During sign-out, AI overlays could aid efficiency by highlighting rare but relevant features and providing quantitative summaries that support standardized reporting.

Safeguards are essential. AI systems must be validated across diverse practice settings, staining protocols, and scanning platforms. Human oversight is mandatory, with the dermatopathologist retaining full diagnostic responsibility. AI involvement should be clearly documented for medicolegal transparency, and performance should be continuously monitored to detect algorithmic drift as new drug eruption patterns emerge. Given current limitations, AI is best viewed as a tool to refine and support expert judgment, not replace it.

What data-sharing or privacy challenges must be addressed to develop robust AI models for diverse drug-eruption histopathology?

DR. BRIDGES: Developing robust AI models for drug eruptions requires large diverse datasets, raising significant privacy and governance challenges. Rigorous de-identification protocols, clear informed consent frameworks, and strong institutional oversight are therefore essential. Multicenter collaborations must employ secure data-use agreements and governance structures that clearly define access, ownership, and downstream use of data.

Ensuring equitable representation is equally critical, as underrepresentation of certain populations may lead to biased performance and disparities in care. Standardized data formats and interoperable systems are needed to facilitate collaboration while preserving security. Transparent governance structures, clear rules regarding data use, and trust-building with patients and institutions will ultimately determine willingness to participate. Addressing these challenges is foundational to advancing AI research in drug eruptions responsibly and ethically.

How might AI enhance the detection of key histologic features in drug eruptions compared to traditional microscopy?

DR. BRIDGES: AI offers the potential to enhance detection of histologic features in drug eruptions by systematically analyzing entire whole-slide images. Convolutional neural networks and attention-based models can identify subtle or focal findings such as scattered dyskeratotic keratinocytes, focal spongiosis, early interface change, rare eosinophils, or microvascular injury, which may be overlooked during routine microscopy due to sampling limitations. This capability is particularly relevant in drug eruptions, where histologic changes often are heterogeneous and patchy.

AI-generated attention heatmaps can highlight diagnostically relevant regions across the slide, improving consistency and completeness of slide reviews. While AI has demonstrated high sensitivity and specificity in broader dermatopathology tasks, particularly neoplastic conditions, drug eruption–specific validation data are currently lacking. As such, the most realistic application at present is AI functioning as a sensitivity-enhancing adjunct or “second reader,” improving consistency and completeness of slide review while preserving expert human interpretation.

Which histologic patterns in drug eruptions are hardest to quantify, and how could AI help standardize their assessment?

DR. BRIDGES: AI-based image analysis can standardize the assessment of histologic patterns through objective reproducible quantification. Deep learning algorithms can segment epidermal and dermal compartments, identify inflammatory cell types, and calculate metrics such as eosinophil density per unit area, percentage of epidermis with vacuolar alteration, or number of affected vessels. Studies in quantitative immunohistochemistry demonstrate high accuracy for tissue segmentation and cell counting, suggesting feasibility for similar applications in inflammatory dermatopathology. While these tools would not replace diagnostic interpretation, they could provide standardized measurements that enhance reproducibility and improve clinicopathologic correlation.

What training challenges must be addressed in AI and drug eruption histology?

DR. BRIDGES: Training AI models for drug eruption histopathology faces several challenges, including the limited availability of high-quality, well-annotated datasets, as most existing AI dermatopathology research focuses on neoplastic conditions. Drug eruptions also exhibit marked histologic heterogeneity, ranging from spongiotic and lichenoid to vasculitic and cytotoxic patterns, often with significant overlap. Accurate labeling, therefore, requires robust clinicopathologic correlation, including medication history, timing, laboratory data, and clinical outcomes—information that is often incomplete or retrospective.

Inaccurate or inconsistent annotations can significantly degrade model performance, and expert disagreement in borderline cases further complicates the creation of reliable ground truth. Additionally, training data may reflect institutional or demographic biases, risking unequal performance across patient populations. Addressing these challenges will require multicenter collaboration, standardized annotation protocols, inclusion of diverse patient cohorts, and careful attention to bias mitigation. At present, these barriers place drug eruption AI firmly in the investigational rather than clinical domain.

How important is AI explainability in the interpretation of diagnostic suggestions?

DR. BRIDGES: Explainability is essential for trust, particularly in the evaluation of drug eruptions, where diagnostic decisions can have serious clinical consequences. Dermatopathologists must understand which histologic features are driving an AI model’s assessment to ensure that conclusions align with morphologic reality and clinicopathologic reasoning. Explainable AI tools (such as attention heatmaps, feature importance rankings, and methods like Shapley Additive Explanations or Local Interpretable Model-Agnostic Explanations) can help clarify which histologic features are driving the AI model’s assessment.

Without transparency, AI systems function as “black boxes,” limiting their utility in high-stakes settings where diagnostic accountability and clinical communication are paramount. Explainability also supports appropriate skepticism, allowing pathologists to recognize when model outputs may be unreliable due to artifacts, atypical patterns, or out-of-distribution cases. In cases of drug eruptions—where diagnosis relies on combining histology, clinical timing, and medication history—explainability is essential for proper use.

How could AI pattern recognition be integrated into your workflow to enhance diagnostic efficiency and accuracy? What safeguards would be required?

DR. BRIDGES: In the near term, AI pattern recognition can be useful as an assistive tool rather than a diagnostic authority. One potential application is pre-screening whole-slide images to flag cases with features such as prominent interface change, increased keratinocyte necrosis, eosinophil-rich infiltrates, or vascular injury, prompting expedited review in clinically concerning scenarios. During sign-out, AI overlays could aid efficiency by highlighting rare but relevant features and providing quantitative summaries that support standardized reporting.

Safeguards are essential. AI systems must be validated across diverse practice settings, staining protocols, and scanning platforms. Human oversight is mandatory, with the dermatopathologist retaining full diagnostic responsibility. AI involvement should be clearly documented for medicolegal transparency, and performance should be continuously monitored to detect algorithmic drift as new drug eruption patterns emerge. Given current limitations, AI is best viewed as a tool to refine and support expert judgment, not replace it.

What data-sharing or privacy challenges must be addressed to develop robust AI models for diverse drug-eruption histopathology?

DR. BRIDGES: Developing robust AI models for drug eruptions requires large diverse datasets, raising significant privacy and governance challenges. Rigorous de-identification protocols, clear informed consent frameworks, and strong institutional oversight are therefore essential. Multicenter collaborations must employ secure data-use agreements and governance structures that clearly define access, ownership, and downstream use of data.

Ensuring equitable representation is equally critical, as underrepresentation of certain populations may lead to biased performance and disparities in care. Standardized data formats and interoperable systems are needed to facilitate collaboration while preserving security. Transparent governance structures, clear rules regarding data use, and trust-building with patients and institutions will ultimately determine willingness to participate. Addressing these challenges is foundational to advancing AI research in drug eruptions responsibly and ethically.

How might AI enhance the detection of key histologic features in drug eruptions compared to traditional microscopy?

DR. BRIDGES: AI offers the potential to enhance detection of histologic features in drug eruptions by systematically analyzing entire whole-slide images. Convolutional neural networks and attention-based models can identify subtle or focal findings such as scattered dyskeratotic keratinocytes, focal spongiosis, early interface change, rare eosinophils, or microvascular injury, which may be overlooked during routine microscopy due to sampling limitations. This capability is particularly relevant in drug eruptions, where histologic changes often are heterogeneous and patchy.

AI-generated attention heatmaps can highlight diagnostically relevant regions across the slide, improving consistency and completeness of slide reviews. While AI has demonstrated high sensitivity and specificity in broader dermatopathology tasks, particularly neoplastic conditions, drug eruption–specific validation data are currently lacking. As such, the most realistic application at present is AI functioning as a sensitivity-enhancing adjunct or “second reader,” improving consistency and completeness of slide review while preserving expert human interpretation.

Which histologic patterns in drug eruptions are hardest to quantify, and how could AI help standardize their assessment?

DR. BRIDGES: AI-based image analysis can standardize the assessment of histologic patterns through objective reproducible quantification. Deep learning algorithms can segment epidermal and dermal compartments, identify inflammatory cell types, and calculate metrics such as eosinophil density per unit area, percentage of epidermis with vacuolar alteration, or number of affected vessels. Studies in quantitative immunohistochemistry demonstrate high accuracy for tissue segmentation and cell counting, suggesting feasibility for similar applications in inflammatory dermatopathology. While these tools would not replace diagnostic interpretation, they could provide standardized measurements that enhance reproducibility and improve clinicopathologic correlation.

What training challenges must be addressed in AI and drug eruption histology?

DR. BRIDGES: Training AI models for drug eruption histopathology faces several challenges, including the limited availability of high-quality, well-annotated datasets, as most existing AI dermatopathology research focuses on neoplastic conditions. Drug eruptions also exhibit marked histologic heterogeneity, ranging from spongiotic and lichenoid to vasculitic and cytotoxic patterns, often with significant overlap. Accurate labeling, therefore, requires robust clinicopathologic correlation, including medication history, timing, laboratory data, and clinical outcomes—information that is often incomplete or retrospective.

Inaccurate or inconsistent annotations can significantly degrade model performance, and expert disagreement in borderline cases further complicates the creation of reliable ground truth. Additionally, training data may reflect institutional or demographic biases, risking unequal performance across patient populations. Addressing these challenges will require multicenter collaboration, standardized annotation protocols, inclusion of diverse patient cohorts, and careful attention to bias mitigation. At present, these barriers place drug eruption AI firmly in the investigational rather than clinical domain.

How important is AI explainability in the interpretation of diagnostic suggestions?

DR. BRIDGES: Explainability is essential for trust, particularly in the evaluation of drug eruptions, where diagnostic decisions can have serious clinical consequences. Dermatopathologists must understand which histologic features are driving an AI model’s assessment to ensure that conclusions align with morphologic reality and clinicopathologic reasoning. Explainable AI tools (such as attention heatmaps, feature importance rankings, and methods like Shapley Additive Explanations or Local Interpretable Model-Agnostic Explanations) can help clarify which histologic features are driving the AI model’s assessment.

Without transparency, AI systems function as “black boxes,” limiting their utility in high-stakes settings where diagnostic accountability and clinical communication are paramount. Explainability also supports appropriate skepticism, allowing pathologists to recognize when model outputs may be unreliable due to artifacts, atypical patterns, or out-of-distribution cases. In cases of drug eruptions—where diagnosis relies on combining histology, clinical timing, and medication history—explainability is essential for proper use.

How could AI pattern recognition be integrated into your workflow to enhance diagnostic efficiency and accuracy? What safeguards would be required?

DR. BRIDGES: In the near term, AI pattern recognition can be useful as an assistive tool rather than a diagnostic authority. One potential application is pre-screening whole-slide images to flag cases with features such as prominent interface change, increased keratinocyte necrosis, eosinophil-rich infiltrates, or vascular injury, prompting expedited review in clinically concerning scenarios. During sign-out, AI overlays could aid efficiency by highlighting rare but relevant features and providing quantitative summaries that support standardized reporting.

Safeguards are essential. AI systems must be validated across diverse practice settings, staining protocols, and scanning platforms. Human oversight is mandatory, with the dermatopathologist retaining full diagnostic responsibility. AI involvement should be clearly documented for medicolegal transparency, and performance should be continuously monitored to detect algorithmic drift as new drug eruption patterns emerge. Given current limitations, AI is best viewed as a tool to refine and support expert judgment, not replace it.

What data-sharing or privacy challenges must be addressed to develop robust AI models for diverse drug-eruption histopathology?

DR. BRIDGES: Developing robust AI models for drug eruptions requires large diverse datasets, raising significant privacy and governance challenges. Rigorous de-identification protocols, clear informed consent frameworks, and strong institutional oversight are therefore essential. Multicenter collaborations must employ secure data-use agreements and governance structures that clearly define access, ownership, and downstream use of data.

Ensuring equitable representation is equally critical, as underrepresentation of certain populations may lead to biased performance and disparities in care. Standardized data formats and interoperable systems are needed to facilitate collaboration while preserving security. Transparent governance structures, clear rules regarding data use, and trust-building with patients and institutions will ultimately determine willingness to participate. Addressing these challenges is foundational to advancing AI research in drug eruptions responsibly and ethically.