The health emergency linked to the SARS-CoV-2 pandemic has highlighted problems in the health management of chronic patients due to their risk of infection, suggesting the need of new methods to monitor patients. People living with HIV/AIDS (PLWHA) represent a paradigm of chronic patients where an e-health-based remote monitoring could have a significant impact in maintaining an adequate standard of care. The key objective of the study is to provide both an efficient operating model to “follow” the patient, capture the evolution of their disease, and establish proximity and relief through a remote collaborative model. These dimensions are collected through a dedicated mobile application that triggers questionnaires on the basis of decision-making algorithms, tagging patients and sending alerts to staff in order to tailor interventions. All outcomes and alerts are monitored and processed through an innovative e-Clinical platform. The processing of the collected data aims into learning and evaluating predictive models for the possible upcoming alerts on the basis of past data, using machine learning algorithms. The models will be clinically validated as the study collects more data, and, if successful, the resulting multidimensional vector of past attributes will act as a digital composite biomarker capable of predicting HIV-related alerts. Design: All PLWH > 18 sears old and stable disease followed at the outpatient services of a university hospital (n = 1500) will be enrolled in the interventional study. The study is ongoing, and patients are currently being recruited. Preliminary results are yielding monthly data to facilitate learning of predictive models for the alerts of interest. Such models are learnt for one or two months of history of the questionnaire data. In this manuscript, the protocol—including the rationale, detailed technical aspects underlying the study, and some preliminary results—are described. Conclusions: The management of HIV-infected patients in the pandemic era represents a challenge for future patient management beyond the pandemic period. The application of artificial intelligence and machine learning systems as described in this study could enable remote patient management that takes into account the real needs of the patient and the monitoring of the most relevant aspects of PLWH management today.

The growing capabilities of Large Language Models (LLMs) in understanding and generating clinical text are transforming the processing of unstructured medical data. This study presents a prompt-based framework for extracting structured information from Multidisciplinary Tumor Board (MTB) reports on ovarian cancer, written in Italian. Focusing on the anamnestic section, the main features (about Demographic, Scores, Comorbidities, Treatments and Gynecological History) are extracted through in-context learning prompts, and evaluated with the LLM-as-a-judge majority-voting scheme. Results show high accuracy across macroareas and strong agreement among judge models, confirming the robustness of the approach. The occurrence-based analysis further highlights how variable frequency influences extraction reliability, ensuring a more balanced interpretation of model performance. Overall, the framework enables reliable LLM-based information extraction and supports the creation of structured clinical knowledge for oncology applications.

Artificial intelligence, particularly Large Language Models (LLM) such as ChatGPT, is emerging as a potentially transformative support for traditionally complex and time-consuming Systematic Literature Reviews (SLRs). In this study, we compared the traditional SLR process executed accordingly with Cochrane guidelines, with an AI-assisted approach using ChatGPT across various stages, from research question formulation to report writing. Effectiveness was assessed through quantitative measurements of time savings at each phase. Results showed substantial time reductions in several operational tasks, including Gantt chart, generating search terms and suggesting selection criteria. However, critical issues arose in stages requiring interpretative judgement, such as analyzing results, assessing risk of bias and final drafting. While AI cannot replace the role of the researcher, it is a valuable tool to optimize SLR workflow. The combination of human expertise and LLM capabilities presents a promising solution, provided it is accompanied by continuous development of AI systems to improve their reliability, transparency and interoperability.