Ruby Hall Clinic is a prominent hospital in the city of Pune, India. It is a 750-bed hospital facility with a staff of 300 consultants, 650 panel doctors and 1500 paramedical staff and was accredited by National Accreditation Board for Hospitals & Healthcare Providers (NABH).It is a recognized Post Graduate Teaching Hospital for the Students of Maharashtra University of Health Sciences, National Board of Examinations and College of Physicians & Surgeons of Mumbai. Dr. P.K. Grant is the managing trustee of Ruby Hall Clinic.
The rapid adoption of generative Artificial intelligence (AI) tools, particularly large language models, image generators, and analytics platforms, has opened transformative opportunities in the way medical research is conducted, reported, and evaluated. These tools offer substantial advantages in efficiency, analytical capacity, language equity, and evidence synthesis. When used responsibly, they have the potential to democratize scientific publishing and accelerate knowledge generation, particularly in resource-constrained settings. However, realizing this potential requires clear ethical guardrails. At present, no unified guidance exists that enables Indian researchers in diabetology and metabolic medicine to harness AI confidently while safeguarding scientific integrity. The Research Society for the Study of Diabetes in India (RSSDI), functioning as a national academic body, has developed this position statement to enable the responsible and confident adoption of AI tools across the entire arc of medical research and scholarly publication, while maintaining the ethical and professional standards that underpin scientific credibility. This consensus document was developed through a structured review of publicly available AI policies from the International Committee of Medical Journal Editors (ICMJE, January 2024 update), Springer Nature, Elsevier, Wiley, SAGE, Taylor Francis, the Committee on Publication Ethics (COPE), the World Association of Medical Editors (WAME), and the Indian Council of Medical Research (ICMR) Ethical Guidelines for Application of AI in Biomedical Research and Healthcare (2023). Thematic domains were extracted, compared for convergence, and synthesized into a unified framework. Consensus was achieved through iterative review among the authors. This statement establishes seven core ethical principles: human accountability, transparency and disclosure, data integrity and authenticity, ethical study design and execution, patient confidentiality and data privacy, responsible interpretation, and publication ethics. It provides role-specific guidance for authors, editors, and peer reviewers, with explicit delineation of permitted and prohibited AI uses, mandatory disclosure templates, an image policy aligned with international standards, a violations and sanctions framework, and practical checklists for compliance. This position statement offers a pragmatic, evidence-aligned framework that empowers RSSDI members, journal contributors, reviewers, and editors to use AI tools with confidence and integrity. Rather than restricting technological progress, it provides the ethical infrastructure needed for Indian diabetes researchers to lead in the responsible integration of AI into scholarly practice. It is intended as a living document subject to biannual review.
Although inappropriate therapy has been consistently associated with adverse outcomes, the magnitude and consistency of the benefit associated with appropriate empiric therapy in critically ill patients remain uncertain. We aimed to quantify the prevalence of appropriate empiric antimicrobial therapy and evaluate its association with outcomes and antimicrobial exposure in a large international ICU cohort. This predefined sub-analysis of the DIANA study included adult ICU patients receiving empirical antimicrobials for suspected or confirmed bacterial infection. Only patients with microbiologically confirmed infections were analyzed, and therapy was classified as appropriate if at least one agent demonstrated in vitro activity against the identified pathogen. Associations with 28-day mortality and antimicrobial-free days were assessed using multivariable logistic and Cox regression models. Pre-specified interaction analyses explored effect modification by disease severity and diagnostic certainty. Of 845 patients with microbiologically confirmed infections, 87.7
Sepsis remains a major cause of mortality and long-term morbidity worldwide. Data on long-term outcomes among Indian intensive care unit (ICU) survivors with sepsis remain limited. This study evaluated long-term mortality, readmission, and quality-of-life outcomes among sepsis survivors in Indian ICUs. LOSS study was a multicenter observational cohort/ follow-up study conducted among adult patients enrolled in the SEPSIS INDIA registry who survived hospital discharge. Baseline demographic, clinical, and comorbidity data were obtained from the registry. Comorbidity burden was assessed using the Charlson Comorbidity Index (CCI). Long-term outcomes were collected through structured telephone interviews. Outcome assessment included long-term mortality, hospital readmission, and health-related quality of life assessed using a modified SF-36 questionnaire. Follow-up was conducted for up to 39 months after discharged alive. A total of 608 patients were included. Median age was 65 years (IQR 54–75), and 58.1
