Sci

Artificial intelligence (AI) is rapidly shifting from experimental pilots to mainstream clinical infrastructure, redefining how evidence, accountability, and ethics intersect in healthcare. This narrative review integrates insights from peer-reviewed studies and policy frameworks to examine seven cross-cutting aspects: bias and fairness, explainability, safety and quality, privacy and data protection, accountability and liability, human oversight, and procurement and deployment. Findings reveal persistent inequities driven by dataset bias and opaque design; the need for explainability tools that are validated, task-specific, and usable by clinicians; and the centrality of post-market surveillance for sustaining patient safety. Privacy-preserving methods such as federated learning and differential privacy show promise but demand rigorous validation and regulatory coherence. Emerging liability models advocate shared enterprise responsibility, while governance-by-design—embedding transparency, auditability, and equity across the AI lifecycle—appears most effective in balancing innovation with public trust. Ethical, legal, and technical safeguards must evolve together to ensure that AI augments, rather than replaces, clinical judgment and institutional accountability.

Pulmonary tuberculosis (TB) produces systemic alterations that can be reflected in biochemical parameters beyond microbiological resolution. Early characterization of the biochemical response to treatment could provide additional criteria for following up with hospitalized patients. A retrospective observational study was conducted focusing on patients with pulmonary TB from a tertiary care hospital, based on biochemical parameters upon admission (“before”) and between 2 and 10 days after starting anti-tuberculosis treatment (“after”). The patients were grouped into three clusters according to the results of the clinical tests: mild (70.1%), inflammatory (26.7%), and severe (3.2%). After the start of treatment, 30% of the patients migrated toward the most biochemically compromised phenotype (Cluster 3). Sixty-one percent showed deterioration in at least one of the three key parameters; only 12.8% improved simultaneously. Significant associations were identified between unfavorable biochemical evolution and HIV (p = 0.004) or patients with public health coverage (p = 0.01). Overall, after antituberculous therapy, a reduction in CRP and leukocytes was observed (p < 0.001), and progressive anemia (ΔHb: −1.7 g/dL) and renal deterioration (ΔCr: +0.52 mg/dL) were identified. The identification of dynamic phenotypes in patients with pulmonary TB can be used to establish early risk markers and contribute to individualized clinical surveillance.

The aim of this study was to investigate the effect of phyto-additive mixture supplementation on semen quality and on some reproductive parameters after artificial insemination in rabbits. The trial run 120 days on 20 adult New Zealand white rabbit bucks that were allocated into two different groups, first was control (CON; n = 10) fed with commercial pelleted-feed and second was considered experimental group (EXP; n = 10) which received in feed a natural feed additive mixture (0.1% of dried Chlorella vulgaris powder and 0.1% of dried Laurus nobilis leaves powder). Consequently, the quality assessment of semen by the Computer Assisted Semen Analyzer (CASA) system, samples were instrumentally inseminated on rabbit does for two consecutive reproductive cycles, and productive and reproductive indexes were evaluated. Results demonstrate that while spermatozoa concentration and ejaculate volume did not differ significantly among experimental groups or between reproduction cycles, spermatozoa motility parameters were significantly enhanced in rabbits receiving the phyto-additive mixture, as evidenced by increased total motility (87.83% vs. 70.63%) and progressive motility (75.68% vs. 50.10%) compared with the control group (p < 0.01). No differences were observed in prolificacy traits during the first reproductive cycle, whereas in the second cycle the phyto-additive treatment increased the number of kits born alive per litter (12.29 vs. 10.19; p < 0.05) and improved kit growth performance at birth (79.17 vs. 66.75 g), at weaning (1085.28 vs. 963.15 g), and in average daily gain (28.75 vs. 25.61 g/day). The study provides evidence of alternative practises based on feeding programme to enhance reproductive traits in rabbit production. The goal is to provide farmers with examples of good farming practise (such as precision farming), focused on sustainability and efficiency, and a certain transfer of knowledge.