The double burden of HIV/AIDS and tuberculosis (TB), coupled with endemic and problematic food insecurity in Africa, can interact to negatively impact health outcomes, creating a syndemic. For people living with HIV/AIDS (PWH), food insecurity is a significant risk factor for acquiring TB due to the strong nutritional influences and co-occurring contextual barriers. We aim to synthesize evidence on the syndemic relationship between HIV/AIDS and TB co-infection and food insecurity in Africa. We conducted a scoping review of studies in Africa that included co-infected adults and children, with evidence of food insecurity, characterized by insufficient to lack of access to macronutrients. We sourced information from major public health databases. Qualitative, narrative analysis was used to synthesize the data. Of 1072 articles screened, 18 articles discussed the syndemic effect of HIV/AIDS and TB co-infection and food insecurity. Reporting of food insecurity was inconsistent, however, five studies estimated it using a validated scale. Food insecure co-infected adults had an average BMI of 16.5–18.5 kg/m². Negative outcomes include death (n = 6 studies), depression (n = 1 study), treatment non-adherence, weight loss, wasting, opportunistic infections, TB-related lung diseases, lethargy. Food insecurity was a precursor to co-infection, especially with the onset/increased incidence of TB in PWH. Economic, social, and facility-level factors influenced the negative impact of food insecurity on the health of co-infected individuals. Nutritional support, economic relief, and psychosocial support minimized the harmful effects of food insecurity in HIV–TB populations. Interventions that tackle one or more components of a syndemic interaction can have beneficial effects on health outcomes and experiences of PWH with TB in Africa.

Background: Recent advancements in artificial intelligence (AI) have significantly impacted dermatology, particularly in diagnosing skin diseases. However, aesthetic dermatology faces unique challenges due to subjective evaluations and the lack of standardized assessment methods. Aims: This review aims to explore the current state of AI in dermatology, evaluate its application in diagnosing skin conditions, and discuss the limitations of traditional evaluation methods in aesthetic dermatology. Additionally, the review proposes strategies for future integration of AI to address existing challenges. Methods: A comprehensive review of AI applications in dermatology was conducted, in both diagnostic and aesthetic fields. Traditional methods such as subjective surveys and hardware devices were analyzed and compared with emerging AI technologies. The limitations of current AI models were evaluated, and the need for standardized evaluation methods and diverse datasets was identified. Results: AI has shown great potential in diagnosing skin diseases, particularly skin cancer. However, in aesthetic dermatology, traditional methods remain subjective and lack standardization, therefore limiting their effectiveness. Emerging AI applications in this field show promise, but they have significant limitations due to biased datasets and inconsistent evaluation methods. Conclusions: To develop the potential of AI in aesthetic dermatology, it is crucial to create standardized evaluation methods, collect diverse datasets reflecting various ethnicities and ages, and educate practitioners on AI's utility and limitations. Addressing these challenges will improve diagnostic accuracy, better patient outcomes, and help integrate AI effectively into clinical practice.