Rare genetic disorders (RDs), characterized by their low prevalence and diagnostic complexities, present significant challenges to healthcare systems. This article explores the transformative impact of artificial intelligence (AI) and machine learning (ML) in addressing these challenges. It emphasizes the need for accurate and early diagnosis of RDs, often hindered by genetic and clinical heterogeneity. This article discusses how AI and ML are reshaping healthcare, providing examples of their effectiveness in disease diagnosis, prognosis, image analysis, and drug repurposing. It highlights AI's ability to efficiently analyze extensive datasets and expedite diagnosis, showcasing case studies like Face2Gene. Furthermore, the article explores how AI tailors treatment plans for RDs, leveraging ML and deep learning (DL) to create personalized therapeutic regimens. It emphasizes AI's role in drug discovery, including the identification of potential candidates for rare disease treatments. Challenges and limitations related to AI in healthcare, including ethical, legal, technical, and human aspects, are addressed. This article underscores the importance of data ethics, privacy, and algorithmic fairness, as well as the need for standardized evaluation techniques and transparency in AI research. It highlights second-generation AI systems that prioritize patient-centric care, efficient patient recruitment for clinical trials, and the significance of high-quality data. The integration of AI with telemedicine, the growth of health databases, and the potential for personalized therapeutic recommendations are identified as promising directions for the field. In summary, this article provides a comprehensive exploration of how AI and ML are revolutionizing the diagnosis and treatment of RDs, addressing challenges while considering ethical implications in this rapidly evolving healthcare landscape.
Bariatric surgery is a critical strategy in managing morbid obesity. Enhanced recovery after surgery (ERAS) protocols have revolutionized perioperative care in this field. This systematic review aims to synthesize current evidence on the impact of ERAS protocols on patient-centered outcomes in bariatric surgery. A comprehensive search across multiple databases was conducted, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Studies involving adult patients undergoing bariatric surgery and focusing on the implementation and outcomes of ERAS protocols were included. Data extraction and analysis emphasized patient recovery, well-being, and satisfaction. Eleven studies met the inclusion criteria. The review revealed that ERAS protocols are associated with reduced postoperative recovery times, decreased hospital stays, and enhanced patient satisfaction. Notably, ERAS protocols effectively reduced complications and optimized resource utilization in bariatric surgery. Comparative insights from non-bariatric surgeries highlighted the versatility and adaptability of ERAS protocols across different surgical disciplines. ERAS protocols significantly improve patient-centered outcomes in bariatric surgery. Their adoption facilitates a patient-focused approach, accelerating recovery and enhancing overall patient well-being. The findings advocate for the broader implementation of ERAS protocols in surgical care, emphasizing the need for continuous refinement to meet evolving healthcare demands. This review supports the paradigm shift toward integrating ERAS protocols in bariatric surgery and potentially other surgical fields.