The interplay between nutrition and skin health provides a crucial lens for understanding, diagnosing, and managing eating disorders (EDs) such as anorexia nervosa (AN), bulimia nervosa (BN), and binge-eating disorder (BED). This review explores the dermatological manifestations resulting from the nutritional deficiencies commonly associated with EDs, including conditions like hair loss, xerosis, and brittle nails. These changes in the skin and its appendages often reflect deeper systemic dysfunctions, such as deficiencies in essential micronutrients (zinc, iron, and vitamins A and C), hormonal imbalances, and electrolyte disturbances. Recognizing these dermatological signs as diagnostic tools is vital for the early identification and intervention of EDs. By integrating dermatological observations with psychiatric and nutritional care, a holistic, multidisciplinary approach can be developed to address both the physical and psychological complexities of EDs. This review highlights the critical role of these skin-related markers in promoting timely diagnosis and effective treatment. To examine the relationship between specific nutrients and dermatological manifestations in EDs, a systematic review of three electronic databases—PubMed, Google Scholar, and ResearchGate—was conducted. The findings underline the importance of early recognition of these skin symptoms for effective management. Collaborative care involving dermatologists, psychiatrists, and nutritionists is essential for diagnosing and treating EDs. Such integrated efforts ensure a comprehensive approach to these multifaceted conditions, ultimately improving patient outcomes and enhancing overall care.

Chronic inflammatory skin diseases and neurodegenerative disorders share overlapping genetic, immunologic, and metabolic pathways that may predispose individuals to cognitive decline. This review synthesizes current human genomic, transcriptomic, and bioinformatic evidence linking psoriasis, rosacea, atopic dermatitis, and bullous pemphigoid with Alzheimer’s and Parkinson’s disease. Literature from PubMed, IEEE Xplore, and Google Scholar was examined, prioritizing studies integrating genomic, transcriptomic, and proteomic analyses. Among inflammatory dermatoses, psoriasis exhibits the strongest overlap with dementia genetics, with shared susceptibility loci including APOE, IL12B, and HLA-DRB5, and transcriptional regulators such as ZNF384 that converge on IL-17/TNF signaling. Rare-variant and pleiotropy analyses further implicate SETD1A and BC070367 in psoriasis–Parkinson’s comorbidity. Rosacea demonstrates upregulation of neurodegeneration-related proteins SNCA, GSK3B, and HSPA8, together with shared regulatory hubs (PPARG, STAT4, RORA) driving NF-κB/IL-17/TNF-dependent inflammation. In atopic dermatitis, rare FLG variants interacting with BACE1 suggest a mechanistic bridge between barrier dysfunction and amyloidogenic processing. Bullous pemphigoid reveals an HLA-DQB1*03:01-mediated immunogenetic link hypothesis and cross-reactive autoantibodies targeting BP180 (collagen XVII) and BP230, highlighting an autoimmune route of neurocutaneous interaction. Other inflammatory and neurodegenerative diseases with currently weak or limited genetic evidence are also discussed, as they may represent emerging biological pathways or potential therapeutic targets within the skin–brain connection in the future. The aim of this work is to help clarify these genetic links and to advocate for the routine cognitive assessment of affected patients, enabling early detection, improved long-term quality of life, and the potential for timely therapeutic intervention.

Neurofibromatosis type 1 (NF1) is an autosomal dominant disorder caused by mutations in the NF1 gene, typically diagnosed during early childhood and characterized by significant phenotypic heterogeneity. Despite advancements in next-generation sequencing (NGS), the diagnostic process remains challenging due to the gene’s complexity, high mutational burden, and frequent identification of variants of uncertain significance (VUS). This review explores the emerging role of artificial intelligence (AI) in enhancing NF1 variant detection, classification, and interpretation. A systematic literature search was conducted across PubMed, IEEE Xplore, Google Scholar, and ResearchGate to identify recent studies applying AI technologies to NF1 genetic analysis, focusing on variant interpretation, structural modeling, tumor classification, and therapeutic prediction. The review highlights the application of AI-based tools such as VEST3, REVEL, ClinPred, and NF1-specific models like DITTO and RENOVO-NF1, which have demonstrated improved accuracy in classifying missense variants and reclassifying VUS. Structural modeling platforms like AlphaFold contribute further insights into the impact of NF1 mutations on neurofibromin structure and function. In addition, deep learning models, such as LTC neural networks, support tumor classification and therapeutic outcome prediction, particularly in NF1-associated complications like congenital pseudarthrosis of the tibia (CPT). The integration of AI methodologies offers substantial potential to improve diagnostic precision, enable early intervention, and support personalized medicine approaches. However, key challenges remain, including algorithmic bias, limited data diversity, and the need for functional validation. Ongoing refinement and clinical validation of these tools are essential to ensure their effective implementation and equitable use in NF1 diagnostics.

As the global population ages, the rising prevalence of neurodegenerative diseases, characterized by abnormal protein aggregates, presents significant challenges for early diagnosis and disease monitoring. Identifying accessible tissue biomarkers is crucial for advancing our ability to detect and track the progression of these diseases. Among the most promising biomarkers is the skin, which shares a common embryological origin with the brain and central nervous system (CNS). This biological connection positions the skin as a potential reflection of CNS pathology. Over the past decades, gene expression studies have demonstrated that key genes involved in neurodegenerative diseases are also expressed in skin tissues. Genes such as APP, PSEN1, PPA2, PINK1, LRRK2, PLCB4, MAPT, SPAST, and SPG7 are prominent in this regard. Beyond gene expression, proteins related to neurodegenerative diseases—such as α-synuclein, TAU, PARKIN, and prion protein (PrP)—have been isolated from the skin of affected individuals, underscoring the skin’s capacity to mirror neural degeneration. This non-invasive window into neurodegenerative processes is further enhanced by advances in stem cell technology, which have allowed for the generation of human-induced pluripotent stem cells (iPSCs) from patient-derived fibroblasts. These iPSCs offer a valuable model for studying disease mechanisms and developing therapeutic approaches. This review conducts a comprehensive analysis of the literature from databases such as PubMed, Google Scholar, and ResearchGate, emphasizing the unique potential of the skin as a non-invasive biomarker for neurodegenerative diseases. It explores how the skin serves as a bridge between gene expression and disease pathology in both the skin and the CNS. By leveraging this biological connection, the skin emerges as a promising model for enhancing our understanding of neurodegenerative disorders and developing innovative strategies for early detection and treatment. However, significant limitations remain, requiring further validation to establish the specificity and sensitivity of these biomarkers.

Sexually transmitted infections (STIs) remain a major global public health challenge, while stigma, privacy concerns, and barriers to healthcare access continue to delay diagnosis and treatment. This narrative review summarizes the current evidence on the use of artificial intelligence (AI) to support STI self-assessment and digital sexual healthcare while critically discussing its current applications, challenges, and limitations, based on a PubMed literature search. Existing studies demonstrate the potential of machine-learning algorithms for individualized HIV/STI risk prediction, symptom-based assessment, automated evaluation of genital lesions, and differentiation of sexually transmitted from non-sexually transmitted conditions, with reported diagnostic performance ranging from AUCs of approximately 0.75–0.95 for symptom assessment models up to 0.893 for multimodal image-based lesion classification and validation accuracies of 94.4% for real-world image analysis platforms. Advances in computer vision and radiomics further highlight the ability of image-based AI systems to identify diagnostically relevant lesion characteristics while improving model interpretability. In parallel, generative AI chatbots are increasingly being explored as tools for sexual health education, personalized risk assessment, behavioural support, triage, and linkage to care. These technologies also influence psychological aspects of sexual health by providing private and accessible support, facilitating risk appraisal, and addressing anxiety associated with STI concerns. However, insufficient demographic diversity and the reliance on retrospective training datasets, privacy and data security concerns, limited prospective validation, and uncertainty regarding real-world clinical effectiveness remain important barriers to the widespread adoption of these technologies. To address some of these limitations, future developments are expected to focus on multimodal and explainable AI systems integrated within digital sexual health ecosystems, with clinicians remaining central to the validation, interpretation, and contextualization of AI-generated assessments and the delivery of patient-centred care.