This review examines the significant influence of Digital Twins (DTs) and their variant, Digital Human Twins (DHTs), on the healthcare field. DTs represent virtual replicas that encapsulate both medical and physiological characteristics—such as tissues, organs, and biokinetic data—of patients. These virtual models facilitate a deeper understanding of disease progression and enhance the customization and optimization of treatment plans by modeling complex interactions between genetic factors and environmental influences. By establishing dynamic, bidirectional connections between the DTs of physical objects and their digital counterparts, these technologies enable real-time data exchange, thereby transforming electronic health records. Leveraging the increasing availability of extensive historical datasets from clinical trials and real-world sources, AI models can now generate comprehensive predictions of future health outcomes for specific patients in the form of AI-generated DTs. Such models can also offer insights into potential diagnoses, disease progression, and treatment responses. This remarkable progression in healthcare paves the way for precision medicine and personalized health, allowing for high-level individualized medical interventions and therapies. However, the integration of DTs into healthcare faces several challenges, including data security, accessibility, bias, and quality. Addressing these obstacles is crucial to realizing the full potential of DHTs, heralding a new era of personalized, precise, and accurate medicine.

Aims and Objectives: the present study aimed to assess the knowledge and attitudes of medical and nursing students at the University of Patras, western Greece, regarding sexually transmitted infections (STIs), sexual behavior and STI prevention measures, as well as the level of future healthcare professionals’ education. Method: A descriptive, cross-sectional study was conducted. A total of 231 medical and nursing students (n = 106 medical, and n = 125 nursing) completed and returned the pre-tested study questionnaire. Results: Most participants (77.5%) were females and46.1% were in the age group of 18–21 years. Syphilis, HIV/AIDS, and Hepatitis B were regarded as STIs by 65.8% of them. Medical students could predominantly list the widely known STIs compared to nursing students (p = 0.004). Regarding HIV/AIDS, 72.7% of the respondents reported that it is transmitted sexually and through blood transfusion. However, medical students were better informed than nursing students (p = 0.001). Medical students as well as students in the final year of their studies were found to be better informed about the vaccines available to prevent STIs. Regarding the question about what constitutes a risky sexual behavior, 71.4% answered sexual intercourse without the use of condom and 18.6% indicated having sex with an unknown partner. Most participants (69.7%) were satisfied with the education provided by their institution and no statistically significant difference was observed between medical and nursing students. Almost all students (97.8%) agreed that the course/subject of sex education must be included in school programs. Conclusions: A comprehensive analysis of knowledge and attitudes of Greek medical and nursing students regarding STIs, prevention measures and education level was conducted. The results of the present study could assist in the development of targeted training courses that can improve healthcare professionals’ knowledge and ability to manage STIs.

Robot-assisted surgery (RAS) has been integrated into clinical practice to overcome several limitations of conventional open or laparoscopic surgery. After the expiration of the long key patent period of Intuitive Surgical, various robotic systems (RSs) have been developed aiming at improving certain characteristics of the first robotic platform, the Da Vinci RS. This narrative review provides an overview of the current RSs utilized in urology along with the initial results from their application in urologic procedures. Nine robotic platforms are being analyzed regarding their unique characteristics as well as their efficacy, safety, feasibility, and outcomes in urologic, oncological or non-oncological operations. The main barrier to the wide application of RAS has been the increased cost which refers to both acquisition and maintenance costs. Besides, the health inequality resulting from the lack of expert robotic surgeons and the difficulty of performing robot-assisted procedures in provincial hospitals should be overcome. However, large properly designed comparative studies are required to establish the role of newly introduced RSs. Additionally, urologists should keep abreast of new developments and research in robot-assisted urologic procedures.

To evaluate the performance of pre-trained deep learning schemes (DLS) in hepatic steatosis (HS) grading of Non-Alcoholic Fatty Liver Disease (NAFLD) patients, using as input B-mode US images containing right kidney (RK) cortex and liver parenchyma (LP) areas indicated by an expert radiologist. A total of 112 consecutively enrolled, biopsy-validated NAFLD patients underwent a regular abdominal B-mode US examination. For each patient, a radiologist obtained a B-mode US image containing RK cortex and LP and marked a point between the RK and LP, around which a window was automatically cropped. The cropped image dataset was augmented using up-sampling, and the augmented and non-augmented datasets were sorted by HS grade. Each dataset was split into training (70%) and testing (30%), and fed separately as input to InceptionV3, MobileNetV2, ResNet50, DenseNet201, and NASNetMobile pre-trained DLS. A receiver operating characteristic (ROC) analysis of hepatorenal index (HRI) measurements by the radiologist from the same cropped images was used for comparison with the performance of the DLS. With the test data, the DLS reached 89.15 %-93.75 % accuracy when comparing HS grades S0-S1 vs. S2-S3 and 79.69 %-91.21 % accuracy for S0 vs. S1 vs. S2 vs. S3 with augmentation, and 80.45-82.73 % accuracy when comparing S0-S1 vs. S2-S3 and 59.54 %-63.64 % accuracy for S0 vs. S1 vs. S2 vs. S3 without augmentation. The performance of radiologists' HRI measurement after ROC analysis was 82 %, 91.56 %, and 96.19 % for thresholds of S ≥ S1, S ≥ S2, and S = S3, respectively. All networks achieved high performance in HS assessment. DenseNet201 with the use of augmented data seems to be the most efficient supplementary tool for NAFLD diagnosis and grading.

General practice/family medicine is a primary healthcare discipline that focuses on providing comprehensive, patient-centered care to individuals and families across their lifespan. This study aimed to assess the implementation of artificial intelligence (AI) in general practice/family medicine regarding the 12 characteristics of its discipline as described in the recently revised European Definition of General Practice/Family Medicine. AI has the potential to revolutionize family medicine practice by improving the accuracy, efficiency, and effectiveness of various clinical tasks. From diagnostic decision-making to chronic condition management and clinical workflow optimization, AI-powered tools offer a range of benefits for patients and clinicians. However, appropriate education and training of the primary healthcare providers are deemed necessary to maximize benefits and minimize potential pitfalls. To ensure efficient and safe integration of AI in medical practice, important implications regarding privacy, malpractice, bias, autonomy, and overtreatment should also be addressed. AI application should be treated as a practical, assistive tool for the clinician, that can hardly take over decision-making and cannot replace the doctor-patient relationship.

Background: The aim of our study was to determine the impact of CYP3A5*1 and CYP3A5*3 on the kinetics of tacrolimus in renal transplant recipients.