CURRENT ISSUE

ABSTRACT: Posterior reversible encephalopathy syndrome (PRES) is a neurological condition characterized by seizures, encephalopathy, visual disturbances, and headache, often occurring in the context of hypertension and immunosuppressive therapy after solid organ transplantation. Although classically presenting with vasogenic edema in the parieto-occipital regions, atypical patterns may also occur. Here we report our experience with a case of cyclosporine-related PRES after liver transplant and summarize PRES clinical features through a literature review.
The case was a 53-year-old man who received a deceased donor liver transplant. His initial immunosuppressive therapy comprised cyclosporine/mycophenolate mofetil/prednisolone. Five months after transplantation, he was admitted to our center with altered mental status. The patient was diagnosed with PRES based on neurological symptoms and neuroimaging findings and recovered after switching from cyclosporine to everolimus. In addition, the lowering of blood pressure with drugs reported in the literature for use in PRES proved to be effective but challenging, requiring the use of multiple agents and only slowly leading to adequate control of hypertensive peaks. Nonetheless, hypertension management and supportive therapy allowed for a complete neurological recovery of the patient.
In conclusion, cyclosporine-associated PRES has a generally favorable prognosis with early diagnosis and prompt treatment, including altering or discontinuing CNIs and controlling blood pressure. CNI-associated PRES should be considered in patients exhibiting acute neurological symptoms after transplantation. Early diagnosis and immediate treatment are critical for a favorable prognosis.

ABSTRACT: Abstract. Biliary strictures represent one of the most common complications after liver transplantation, occurring in 10-30% of recipients and significantly affecting patient quality of life and graft function. Combined hybrid approaches are increasingly widespread in contemporary practice.
Objective: To present a case series of successful application of combined methods for biliary stricture management in patients after liver transplantation and to analyze the efficacy and safety of these approaches.
Methods: A prospective observational study of three female patients (median age 57.0±7.8 years) after orthotopic liver transplantation with biliary strictures was conducted. All underwent a combined procedure using the rendezvous technique, integrating percutaneous transhepatic and endoscopic approaches with biliary stent placement.
Results: Technical success was achieved in 100% of cases (3/3). Clinical success with bilirubin reduction >50% was registered in all patients (3/3, 100%). Total bilirubin decreased from 71.3±22.8 μmol/L to 58.2±18.5 μmol/L by day 7 and to 32.4±8.6 μmol/L by day 30. No serious complications were registered (0/3, 0%). Mean hospitalization was 5.3±1.5 days (range 4-7 days). Mean procedure duration was 85±15 minutes.
Conclusion: The combined method demonstrated high technical feasibility (100%), clinical efficacy (100%), and a favorable safety profile with no serious complications, showing particular effectiveness in recurrent strictures. This approach can be considered as a promising alternative to isolated interventions in treating complex anastomotic biliary strictures.

ABSTRACT: Artificial intelligence (AI) has become increasingly integrated into radiology and nuclear medicine, particularly in oncology, where imaging plays a central role in diagnosis, staging, treatment planning, and response assessment. To date, evaluation of AI-enabled radiology has been dominated by diagnostic accuracy metrics derived from retrospective validation studies. While such measures are essential for technical assessment, they provide limited insight into real-world clinical value. High algorithmic performance does not necessarily translate into improved decision-making, workflow efficiency, patient outcomes, or health system performance. This narrative review critically examines AI-enabled radiology as a digital health intervention in oncology and nuclear medicine, emphasizing the need to move beyond accuracy-centric evaluation paradigms. We analyze the translational gap between controlled validation and routine clinical deployment, highlighting challenges related to dataset bias, generalizability, and human–AI interaction. Key domains of real-world impact are explored, including clinical decision-making, multidisciplinary integration, workflow and operational performance, patient-centered outcomes, and system-level implications. Methodological considerations for outcome-focused evaluation are discussed, alongside regulatory, ethical, and governance frameworks necessary for responsible implementation. We propose a clinical-impact–centered evaluation framework that links AI-assisted imaging to patient, clinician, and system-level outcomes within a continuous monitoring model. Reframing AI-enabled radiology as a clinical intervention rather than a standalone algorithm is essential for ensuring meaningful, equitable, and sustainable adoption in oncology and nuclear medicine practice.

ABSTRACT: Precision medicine aims to deliver the right treatment to the right patient at the right time, yet its widespread clinical adoption remains limited by challenges in accurate diagnosis, slow drug development processes and the difficulty of translating complex biological data into actionable clinical decisions. Conventional diagnostic and therapeutic approaches often rely on population averages, which can overlook individual genetic, molecular and clinical differences, leading to variable treatment responses and high drug development failure rates. In recent years, Artificial Intelligence (AI) and Machine Learning (ML) have gained increasing attention as clinical support tools capable of analyzing complex and large-scale biomedical data, improving diagnostic accuracy, accelerating drug development and enabling more personalized approaches to patient care. This study presents a systematic literature review conducted in accordance with the PRISMA guidelines, examining recent evidence on how AI and ML act as catalysts for precision medicine, particularly in diagnosis and drug development. Peer-reviewed studies published between 2019 and 2025 were systematically identified from major academic databases and screened using predefined inclusion and exclusion criteria. The selected studies were analyzed to assess clinical applications, AI techniques employed and their implications for personalized healthcare and pharmaceutical innovation. The findings indicate that AI and ML significantly enhance diagnostic accuracy through applications in medical imaging, genomics and electronic health record analysis, supporting earlier and more precise disease detection. In drug development, AI-driven methods improve target identification, lead optimization, toxicity prediction and clinical trial design, contributing to reduced development time and cost. Furthermore, the integration of multi-omics and clinical data through AI enables more personalized treatment strategies, improving therapeutic selection and dosing. This study concludes that AI and ML are powerful catalysts for precision medicine and capable of bridging the gap between complex biomedical data and clinical decision-making. With appropriate validation, explainable models and robust ethical and regulatory frameworks, these technologies have the potential to accelerate drug development and support clinicians in delivering more accurate diagnoses, more effective treatments and safer patient-centered, precision-based healthcare.