The urgent need to accelerate synthetic data privacy frameworks for medical research.

Synthetic data, generated through artificial intelligence technologies such as generative adversarial networks and latent diffusion models, maintain aggregate patterns and relationships present in the real data the technologies were trained on without exposing individual identities, thereby mitigating re-identification risks. This approach has been gaining traction in biomedical research because of its ability to preserve privacy and enable dataset sharing between organisations. Although the use of synthetic data has become widespread in other domains, such as finance and high-energy physics, use in medical research raises novel issues.

The not so 'simple' laparoscopic nephrectomy: Experience with 3D laparoscopy from a tertiary care centre.

Introduction: Laparoscopic simple nephrectomy (LSNx) is among the gold standard options for management of selected poorly functioning and most non-functioning kidneys (PFK/NFKs). Our objective was to audit our patients who had undergone LSNx for benign causes and assess their perioperative outcomes and analyse the factors correlating with these outcomes.

Materials And Methods: We reviewed our prospectively collected data of patients with PFK/NFKs due to benign causes who underwent a LSNx on a three-dimensional (3D) system via a transperitoneal approach between January 2017 and June 2023.

OSAIRIS: Lessons Learned From the Hospital-Based Implementation and Evaluation of an Open-Source Deep-Learning Model for Radiotherapy Image Segmentation.

Several studies report the benefits and accuracy of using autosegmentation for organ at risk (OAR) outlining in radiotherapy treatment planning. Typically, evaluations focus on accuracy metrics, and other parameters such as perceived utility and safety are routinely ignored. Here, we report our finding from the implementation and clinical evaluation of OSAIRIS, an open-source AI model for radiotherapy image segmentation that was carried out as part of its development into a medical device.

Burden of vaso-occlusive crisis, its management and impact on quality of life of Indian sickle cell disease patients.

Sickle cell disease (SCD) with vaso-occlusive pain crisis (VOC) significantly impacts patient well-being and often results in extensive healthcare resource utilization. This study assessed the VOC burden, its management and its impact on patients' quality of life (QoL). A cross-sectional observational study was conducted between November 2021 and June 2022, including 1000 SCD patients from high-prevalence states in India.