The era of artificial intelligence (AI) has revolutionized our daily lives and AI has become a powerful force that is gradually transforming the field of medicine. Ophthalmology sits at the forefront of this transformation thanks to the effortless acquisition of an abundance of imaging modalities. There has been tremendous work in the field of AI for retinal diseases, with age-related macular degeneration being at the top of the most studied conditions. The purpose of the current systematic review was to identify and evaluate, in terms of strengths and limitations, the articles that apply AI to optical coherence tomography (OCT) images in order to predict the future evolution of age-related macular degeneration (AMD) during its natural history and after treatment in terms of OCT morphological structure and visual function. After a thorough search through seven databases up to 1 January 2022 using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 1800 records were identified. After screening, 48 articles were selected for full-text retrieval and 19 articles were finally included. From these 19 articles, 4 articles concentrated on predicting the anti-VEGF requirement in neovascular AMD (nAMD), 4 articles focused on predicting anti-VEGF efficacy in nAMD patients, 3 articles predicted the conversion from early or intermediate AMD (iAMD) to nAMD, 1 article predicted the conversion from iAMD to geographic atrophy (GA), 1 article predicted the conversion from iAMD to both nAMD and GA, 3 articles predicted the future growth of GA and 3 articles predicted the future outcome for visual acuity (VA) after anti-VEGF treatment in nAMD patients. Since using AI methods to predict future changes in AMD is only in its initial phase, a systematic review provides the opportunity of setting the context of previous work in this area and can present a starting point for future research.

Background/Objectives: Hybrid breast reconstruction (HBR), combining implant-based breast reconstruction (IBR) with autologous fat grafting (FG), has emerged as a promising solution to improve aesthetic outcomes and reduce complications, especially in irradiated patients. This study aims to systematically review current evidence on the outcomes of HBR with a focus on complication rates and aesthetic satisfaction. Methods: A systematic literature search was performed in March 2023 using PubMed and Embase databases. Studies were selected based on predefined PICOS criteria, including adult female patients undergoing IBR with FG. Sixteen studies met the inclusion criteria. Data on patient demographics, surgical techniques, fat grafting timing, complications, and aesthetic outcomes were extracted and analysed. Results: A total of 730 patients were included, with a mean follow-up of 20.23 months. The overall complication rate was 9%, with fat necrosis being the most common (2.7%). Capsular contracture occurred in 4.5% of cases. Immediate fat grafting during implant placement showed the lowest complication rate (8%) compared to delayed or staged procedures. Aesthetic satisfaction was high, with an average score of 4.4 out of 5 in five studies. Fat grafting improved skin quality and contour, especially in irradiated patients, and enabled the use of smaller implants. No oncological recurrences were reported. Conclusions: HBR is associated with high aesthetic satisfaction and a low rate of complications. Immediate fat grafting during IBR appears to be the safest and most effective approach. These findings support the wider adoption of HBR, though further high-quality studies are needed to validate long-term safety and outcomes.

Background/Objectives: Pyoderma gangrenosum (PG) is a rare, autoimmune skin condition characterized by painful, rapidly progressing ulcers, often associated with autoimmune dysregulation. Managing PG following breast surgery presents unique challenges due to its pathergy phenomenon, which complicates surgical interventions. This article outlines the case of PG in a 48-year-old female post-breast surgery and reviews management strategies through a systematic analysis of the literature. Methods: A systematic literature review from 2018 to 2023 identified 24 relevant articles on PG management post-breast surgery. The studies were analyzed to compare the efficacy and complications of conservative versus combined (conservative and surgical) treatment strategies. Results: Results indicate that while conservative management, primarily with corticosteroids, remains preferred, combined strategies, including systemic therapies, vacuum-assisted closure, and surgery, offer significant benefits in select cases. Conclusions: Our findings suggest that a personalized, multifaceted treatment plan is crucial for managing PG effectively, emphasizing the need for early detection, meticulous planning, and comprehensive care to optimize patient outcomes.

Patient compliance in chronic illnesses is essential for disease management. This also applies to age-related macular degeneration (AMD), a chronic acquired retinal degeneration that needs constant monitoring and patient cooperation. Therefore, patients with AMD can benefit by being properly informed about their disease, regardless of the condition’s stage. Information is essential in keeping them compliant with lifestyle changes, regular monitoring, and treatment. Large language models have shown potential in numerous fields, including medicine, with remarkable use cases. In this paper, we wanted to assess the capacity of two large language models (LLMs), ChatGPT4 and PaLM2, to offer advice to questions frequently asked by patients with AMD. After searching on AMD-patient-dedicated websites for frequently asked questions, we curated and selected a number of 143 questions. The questions were then transformed into scenarios that were answered by ChatGPT4, PaLM2, and three ophthalmologists. Afterwards, the answers provided by the two LLMs to a set of 133 questions were evaluated by two ophthalmologists, who graded each answer on a five-point Likert scale. The models were evaluated based on six qualitative criteria: ($C_1$) reflects clinical and scientific consensus, ($C_2$) likelihood of possible harm, ($C_3$) evidence of correct reasoning, ($C_4$) evidence of correct comprehension, ($C_5$) evidence of correct retrieval, and ($C_6$) missing content. Out of 133 questions, ChatGPT4 received a score of five from both reviewers to 118 questions (88.72%) for $C_1$, to 130 (97.74%) for $C_2$, to 131 (98.50%) for $C_3$, to 133 (100%) for $C_4$, to 132 (99.25%) for $C_5$, and to 122 (91.73%) for $C_6$, while PaLM2 to 81 questions (60.90%) for $C_1$, to 114 (85.71%) for $C_2$, to 115 (86.47%) for $C_3$, to 124 (93.23%) for $C_4$, to 113 (84.97%) for $C_5$, and to 93 (69.92%) for $C_6$. Despite the overall high performance, there were answers that are incomplete or inaccurate, and the paper explores the type of errors produced by these LLMs. Our study reveals that ChatGPT4 and PaLM2 are valuable instruments for patient information and education; however, since there are still some limitations to these models, for proper information, they should be used in addition to the advice provided by the physicians.

Background/Objectives: Representing the most common malignancy worldwide, skin cancer requires timely detection to improve prognosis. Both educational level of the patients and health literacy are important variables in terms of prevention and diagnosis in early stages of the disease, but data from Central and Eastern Europe are limited. Methods: We realized a prospective observational study that included 76 patients who were diagnosed with skin cancer and treated at the “Prof. Dr. I. Chiricuță” Institute of Oncology in Cluj-Napoca, Romania. Demographic, clinical, histopathological, and psychosocial data were collected in a standardized form. The primary aim was the measurement of diagnostic delay, defined as the interval since symptom onset until diagnosis. Secondary variables included education level, place of residence, participation in awareness campaigns and understanding capacity. Statistical analyses were applied. Results: The mean age in the cohort was 58.3 years; 52.6% were male and 84.2% were urban residents. The most frequent histological type was melanoma (47.4%), followed by basal cell carcinoma (36.8%), and squamous cell carcinoma (10.5%). The median delay in diagnostic was equal to 3 weeks. Education level was significantly related to earlier presentation (Kruskal–Wallis, p = 0.043), with shorter delays noticed in patients with university or postgraduate degrees (compared to those with secondary education). However, there were no significant differences between patients with rural and urban provenience (p = 0.483). Patients’ capacity of understanding showed no correlation with diagnostic delay, but their prior participation in awareness campaigns was strongly associated with higher comprehension (p < 0.001). Also, skin self-examination did not significantly impact time to diagnosis (p = 0.86). Conclusions: Higher levels of education and patients’ exposure to awareness campaigns might represent predictors of shorter diagnostic delay, highlighting the impact of public health initiatives and targeted educational strategies to improve early detection of skin cancers in Romania. However, the findings must be interpreted in light of the study’s limitations, namely the relatively small sample size and single-center design.