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The current revolution of digital health technology and machine learning offers enormous potential to improve patient care. Nevertheless, it is essential to recognize that dermatology requires an approach different from those of other specialties. For many dermatological conditions, there is a lack of standardized methodology for quantitatively tracking disease progression and treatment response (clinimetrics). Furthermore, dermatological diseases impact patients in complex ways, some of which can be measured only through patient reports (psychometrics). New tools using digital health technology (e.g., smartphone applications, wearable devices) can aid in capturing both clinimetric and psychometric variables over time. With these data, machine learning can inform efforts to improve health care by, for example, the identification of high-risk patient groups, optimization of treatment strategies, and prediction of disease outcomes. We use the term personalized, data-driven dermatology to refer to the use of comprehensive data to inform individual patient care and improve patient outcomes. In this paper, we provide a framework that includes data from multiple sources, leverages digital health technology, and uses machine learning. Although this framework is applicable broadly to dermatological conditions, we use the example of a serious inflammatory skin condition, chronic cutaneous graft-versus-host disease, to illustrate personalized, data-driven dermatology.

The development of multiomic profiling tools has rapidly expanded in recent years, along with their use in profiling skin tissues in various contexts, including dermatologic diseases. Among these tools, single-cell RNA-sequencing (scRNA-seq) and spatial transcriptomics (ST) have emerged as widely adopted and powerful assays for elucidating key cellular components and their spatial arrangement within skin disease. In this paper, we review the recent biological insights gained from the use of scRNA-seq and ST and the advantages of combining both for profiling skin diseases, including aberrant wound healing, inflammatory skin diseases, and cancer. We discuss the role of scRNA-seq and ST in improving skin disease treatments and moving toward the goal of achieving precision medicine in dermatology, whereby patients can be optimally matched to treatments that maximize therapeutic response.

Melanoma tumor syndromes (MTS) represent an important minority of familial melanoma cases. In these patients, the accumulation of sequence alterations in essential genes may prelude the risk of internal malignancies, in addition to melanoma. Although several host and environmental factors have been implicated in familial melanoma, the exact mechanisms of cancer predisposition—particularly in the context of mixed cancer syndromes—still remain unclear. In this paper, we review new insights into MTS and elucidate recent efforts that guide individualized prognostication and treatment for these diseases in the past quarter century.

The current revolution of digital health technology and machine learning offers enormous potential to improve patient care. Nevertheless, it is essential to recognize that dermatology requires an approach different from those of other specialties. For many dermatological conditions, there is a lack of standardized methodology for quantitatively tracking disease progression and treatment response (clinimetrics). Furthermore, dermatological diseases impact patients in complex ways, some of which can be measured only through patient reports (psychometrics). New tools using digital health technology (e.g., smartphone applications, wearable devices) can aid in capturing both clinimetric and psychometric variables over time. With these data, machine learning can inform efforts to improve health care by, for example, the identification of high-risk patient groups, optimization of treatment strategies, and prediction of disease outcomes. We use the term personalized, data-driven dermatology to refer to the use of comprehensive data to inform individual patient care and improve patient outcomes. In this paper, we provide a framework that includes data from multiple sources, leverages digital health technology, and uses machine learning. Although this framework is applicable broadly to dermatological conditions, we use the example of a serious inflammatory skin condition, chronic cutaneous graft-versus-host disease, to illustrate personalized, data-driven dermatology.

Lichen planopilaris (LPP) and frontal fibrosing alopecia (FFA) are primary, lymphocytic cicatricial hair loss disorders. These model epithelial stem cell (SC) diseases are thought to result from a CD8+ T-cell‒dominated immune attack on the hair follicle (HF) SC niche (bulge) after the latter has lost its immune privilege (IP) for as yet unknown reasons. This induces both apoptosis and pathological epithelial‒mesenchymal transition in epithelial SCs, thus depletes the bulge, causes fibrosis, and ultimately abrogates the HFs’ capacity to regenerate. In this paper, we synthesize recent progress in LPP and FFA pathobiology research, integrate our limited current understanding of the roles that genetic, hormonal, environmental, and other factors may play, and define major open questions. We propose that LPP and FFA share a common initial pathobiology, which then bifurcates into two distinct clinical phenotypes, with macrophages possibly playing a key role in phenotype determination. As particularly promising translational research avenues toward direly needed progress in the management of these disfiguring, deeply distressful cicatricial alopecia variants, we advocate to focus on the development of bulge IP and epithelial SC protectants such as, for example, topically effective, HF‒penetrating and immunoinhibitory preparations that contain tacrolimus, peroxisome proliferator–activated receptor-γ, and/or CB1 agonists.

Physician‒scientists have made countless discoveries, and their dwindling numbers are a significant concern. Although dermatology has become an increasingly popular destination for physician‒scientist trainees, the proportion of trainees who pursue scientific research careers after training is among the lowest of all medical specialties. To investigate this problem, we surveyed a national cohort of dermatology educators, physician‒scientist track program directors, and National Institute of Arthritis and Musculoskeletal and Skin Diseases T32 directors for opinions regarding physician‒scientist training in dermatology. On the basis of these findings and to help address the issue, we propose a training practicum and provide a resource for funding opportunities to help guide trainees and institutions interested in supporting investigative dermatologists. We also discuss the important roles of department chairs and institutions in fashioning an environment conducive to physician‒scientist training. The information and recommendations provided in this paper may help to improve the recruitment, training, development, and retention of investigative dermatologists and future leaders in this field.