Discover Artificial Intelligence, Год журнала: 2024, Номер 4(1)
Опубликована: Дек. 19, 2024
Язык: Английский
Journal of Medical Systems, Год журнала: 2025, Номер 49(1)
Опубликована: Янв. 16, 2025
Generative Artificial Intelligence (Gen AI) has transformative potential in healthcare to enhance patient care, personalize treatment options, train professionals, and advance medical research. This paper examines various clinical non-clinical applications of Gen AI. In settings, AI supports the creation customized plans, generation synthetic data, analysis images, nursing workflow management, risk prediction, pandemic preparedness, population health management. By automating administrative tasks such as documentations, reduce clinician burnout, freeing more time for direct care. Furthermore, application may surgical outcomes by providing real-time feedback automation certain operating rooms. The data opens new avenues model training diseases simulation, enhancing research capabilities improving predictive accuracy. contexts, improves education, public relations, revenue cycle marketing etc. Its capacity continuous learning adaptation enables it drive ongoing improvements operational efficiencies, making delivery proactive, predictive, precise.
Язык: Английский
Процитировано
5
Current Opinion in Urology, Год журнала: 2025, Номер unknown
Опубликована: Март 3, 2025
Purpose of review By leveraging models such as large language (LLMs) and generative computer vision tools, artificial intelligence (GAI) is reshaping cancer research oncologic practice from diagnosis to treatment follow-up. This timely provides a comprehensive overview the current applications future potential GAI in oncology, including urologic malignancies. Recent findings has demonstrated significant improving by integrating multimodal data, diagnostic workflows, assisting imaging interpretation. In treatment, shows promise aligning clinical decisions with guidelines, optimizing systemic therapy choices, aiding patient education. Posttreatment, include streamlining administrative tasks, follow-up care, monitoring adverse events. image analysis, data extraction, outcomes research. Future developments could stimulate discovery, improve efficiency, enhance patient-physician relationship. Summary Integration into oncology shown some ability accuracy, optimize decisions, ultimately strengthening Despite these advancements, inherent stochasticity GAI's performance necessitates human oversight, more specialized models, proper physician training, robust guidelines ensure its well tolerated effective integration practice.
Язык: Английский
Процитировано
0
Current Opinion in Urology, Год журнала: 2025, Номер unknown
Опубликована: Март 19, 2025
Purpose of review The integration artificial intelligence (AI) into healthcare has significantly impacted the way is delivered, particularly with generative AI-powered chatbots. This aims to provide an analysis application, benefits, challenges and future chatbots in Urology. Recent findings advancements AI have led significant improvements chatbot performance applicability healthcare. Generative shown promise patient education, symptom assessment, administrative tasks, clinical decision-making urology. Studies demonstrate their ability reduce clinic burden, improve satisfaction, enhance accessibility. However, concerns remain about accuracy, data privacy, workflows. Summary Increasing number studies urological practice. As technology advances, likely integrate multiple aspects Concerns will need be examined before safe implementation.
Язык: Английский
Процитировано
0
N N Priorov Journal of Traumatology and Orthopedics, Год журнала: 2025, Номер unknown
Опубликована: Март 17, 2025
The review considers the use of generative artificial intelligence technologies in surgery, traumatology and orthopedics. Definitions key are given, as well difference between discriminative models intelligence. An analysis publication activity on orthopedics world macroregions is conducted. potential role various at preoperative, intraoperative postoperative stages healthcare analyzed. Data results clinical application most common problems associated with practical applications provided including issues quality safety surgical care. proposes solutions research directions to address these problems.
Язык: Английский
Процитировано
0
Journal of Robotic Surgery, Год журнала: 2025, Номер 19(1)
Опубликована: Март 31, 2025
Язык: Английский
Процитировано
0
Surgical Endoscopy, Год журнала: 2025, Номер unknown
Опубликована: Март 31, 2025
Robotic-assisted minimally invasive esophagectomy (RAMIE) is a complex surgical procedure for treating esophageal cancer. Artificial intelligence (AI) an uprising technology with increasing applications in the field. This scoping review aimed to assess current AI RAMIE, focus on intraoperative video analysis. To identify all articles utilizing comprehensive literature search was performed accordance Preferred Reporting Items Systematic Reviews and Meta-analysis reviews of Medline Embase databases Cochrane Library. Two independent reviewers assessed quality inclusion. One hundred seventeen were identified, which four included final Results demonstrated that main RAMIE assessment evaluation technical skills evaluate performance. also used phase recognition support clinical decision-making through guidance key anatomical landmarks. Various deep-learning networks generate models, there strong emphasis using high-quality standardized frames. The use especially analysis recognition, still relatively new field should be further explored. advantages algorithms videos automated manner may harnessed improve performance decision-making, achieve higher surgery, postoperative outcomes.
Язык: Английский
Процитировано
0
Cureus, Год журнала: 2025, Номер unknown
Опубликована: Апрель 15, 2025
Язык: Английский
Процитировано
0
Journal of Clinical Medicine, Год журнала: 2025, Номер 14(8), С. 2698 - 2698
Опубликована: Апрель 15, 2025
Background/Objectives: This study comprehensively examines how artificial intelligence (AI) technologies are transforming clinical practice in plastic and reconstructive surgery across the entire patient care continuum, with specific objective of identifying evidence-based applications, implementation challenges, emerging opportunities that will shape future specialty. Methods: A comprehensive narrative review was conducted analyzing integration AI surgery, including preoperative planning, intraoperative postoperative monitoring, quality improvement. Challenges related to implementation, ethics, regulatory frameworks were also examined, along technological trends practice. Results: applications demonstrate significant potential multiple domains. In enhances risk assessment, outcome prediction, surgical simulation. Intraoperatively, AI-assisted robotics enables increased precision technical capabilities beyond human limitations, particularly microsurgery. Postoperatively, improves complication detection, pain management, outcomes assessment. Despite these benefits, faces challenges data privacy concerns, algorithmic bias, liability questions, need for appropriate frameworks. Future directions include multimodal systems, federated learning approaches, extended reality regenerative medicine technologies. Conclusions: The into represents a opportunity enhance precision, improve expand boundaries what is surgically possible. However, successful requires addressing ethical considerations maintaining elements care. Plastic surgeons must actively engage development ensure address genuine needs while aligning specialty’s core values restoring form function, alleviating suffering, enhancing life.
Язык: Английский
Процитировано
0
Decision Analytics Journal, Год журнала: 2025, Номер unknown, С. 100580 - 100580
Опубликована: Май 1, 2025
Язык: Английский
Процитировано
0
Deleted Journal, Год журнала: 2024, Номер 63(9), С. 860 - 866
Опубликована: Июль 24, 2024
Large language models (LLMs) are gaining popularity due to their ability communicate in a human-like manner. Their potential for science, including urology, is increasingly recognized. However, unresolved concerns regarding transparency, accountability, and the accuracy of LLM results still exist.
Язык: Английский
Процитировано
2