This study evaluated three prominent Large Language Models(LLMs)-Google’s AI BARD,Bing’s AI,and ChatGPT-4 in providing patient advice for hand laceration.Five simulated patient inquiries on hand trauma were prompted...This study evaluated three prominent Large Language Models(LLMs)-Google’s AI BARD,Bing’s AI,and ChatGPT-4 in providing patient advice for hand laceration.Five simulated patient inquiries on hand trauma were prompted to them.A panel of Board-certified plastic surgical residents evaluated the responses for accuracy,comprehensiveness,and appropriate sources.Responses were also compared against existing literature and guidelines.This study suggests that ChatGPT outperforms BARD and Bing AI in providing reliable,evidence-based clinical advice,but they still face limitations in depth and specificity.Healthcare professionals are essential in interpreting LLM recommendations,and future research should improve LLM performance by integrating specialized databases and human expertise to advance nerve injury management and optimize patient-centred care.展开更多
The integration of artificial intelligence(AI)into plastic surgery is transforming the field by enhancing precision in preoperative planning,diagnostic accuracy,intraoperative assistance,and postoperative care.AI enco...The integration of artificial intelligence(AI)into plastic surgery is transforming the field by enhancing precision in preoperative planning,diagnostic accuracy,intraoperative assistance,and postoperative care.AI encompasses machine learning,natural language processing,computer vision,and artificial neural networks,each offering unique advancements to surgical practice.This narrative review explores the ethical challenges of AI in plastic surgery,addressing concerns such as data protection,algorithmic bias,transparency,accountability,and informed consent.A comprehensive search adhering to PRISMA guidelines identified 63 studies,with 15 selected for in-depth analysis.Findings indicate significant ethical issues:data privacy needs stringent cybersecurity,biases in AI models must be mitigated,and transparency in AI decision making is essential.The review emphasizes the necessity for updated Health Insurance Portability and Accountability Act(HIPAA)regulations,robust validation mechanisms,and the development of explainable AI models.It also highlights the need for an independent regulatory body to oversee AI integration,ensuring ethical standards and protecting patient welfare.Although AI presents promising benefits,its successful application in plastic surgery hinges on addressing these ethical challenges comprehensively.展开更多
The increasing adoption and widespread acceptance of negative pressure wound therapy(NPWT)have paralleled the expansion of its indications in clinical practice.The spectrum of indications for NPWT now extends to encom...The increasing adoption and widespread acceptance of negative pressure wound therapy(NPWT)have paralleled the expansion of its indications in clinical practice.The spectrum of indications for NPWT now extends to encompass soft tissue defects arising from trauma,infection,surgical wound care,and soft tissue grafting procedures.Recent advancements in NPWT devices have introduced various adjuncts,such as instillation of fluids or antibiotics into the wound.These additions empower surgeons to enhance the wound healing environment and contribute to combatting infections more effectively.This review delves into the latest literature addressing the proposed mechanisms underlying NPWT's action,its cost-effectiveness,its impact on patient quality of life,and the essential components necessary for its safe use.The review examines the evidence supporting NPWT's application in managing traumatic extremity injuries,controlling infections,and wound care.While NPWT generally exhibits a low complication rate,surgeons must remain aware of the potential risks linked to its utilization.Moreover,the review explores the widening scope of indications for NPWT,shedding light on prospective avenues for innovation and research in this field.展开更多
Aim:In the digital age,artificial intelligence(AI)platforms have gradually replaced traditional manual techniques for information retrieval.However,their effectiveness in conducting academic literature searches remain...Aim:In the digital age,artificial intelligence(AI)platforms have gradually replaced traditional manual techniques for information retrieval.However,their effectiveness in conducting academic literature searches remains unclear,necessitating a comparative assessment.This study examined the efficacy of AI search engines(Elicit,Consensus,ChatGPT)vs.manual search for literature retrieval,focusing on the surgical management of trapeziometacarpal osteoarthritis.Methods:The study was executed per the Cochrane Handbook for Systematic Reviews and PRISMA guidelines.AI platforms were given relevant keywords and prompts,while manual searches used PubMed,Cochrane CENTRAL,Web of Science,and Scopus databases from January 1901 to April 2024.The study focused on English-language randomized controlled trials(RCTs)comparing surgical management of trapeziometacarpal osteoarthritis(TMCJ OA).Two independent evaluators screened and extracted data from the studies.Primary outcomes involved the quality and relevancy of studies chosen by both search methods,evaluated by false positive rates and number of studies,including outcomes of interest.Results:The manual search yielded the most results(6,018),followed by Elicit(4,980),Consensus(3,436),and ChatGPT(6).Elicit identified the highest number of RCTs(205)but also had the greatest false positive rate(94%).Ultimately,the manual search identified 23 suitable studies,Elicit found 10,Consensus found 9,and ChatGPT identified only 1.No additional studies were found by AI search engines that were not discovered in the manual search.Conclusion:The findings highlight the potential advantages and drawbacks of AI search engines for literature searches.While Elicit was prone to error,Consensus and ChatGPT were less comprehensive.Significant enhancements in the precision and thoroughness of AI search engines are required before they can be effectively utilized in academia.展开更多
文摘This study evaluated three prominent Large Language Models(LLMs)-Google’s AI BARD,Bing’s AI,and ChatGPT-4 in providing patient advice for hand laceration.Five simulated patient inquiries on hand trauma were prompted to them.A panel of Board-certified plastic surgical residents evaluated the responses for accuracy,comprehensiveness,and appropriate sources.Responses were also compared against existing literature and guidelines.This study suggests that ChatGPT outperforms BARD and Bing AI in providing reliable,evidence-based clinical advice,but they still face limitations in depth and specificity.Healthcare professionals are essential in interpreting LLM recommendations,and future research should improve LLM performance by integrating specialized databases and human expertise to advance nerve injury management and optimize patient-centred care.
文摘The integration of artificial intelligence(AI)into plastic surgery is transforming the field by enhancing precision in preoperative planning,diagnostic accuracy,intraoperative assistance,and postoperative care.AI encompasses machine learning,natural language processing,computer vision,and artificial neural networks,each offering unique advancements to surgical practice.This narrative review explores the ethical challenges of AI in plastic surgery,addressing concerns such as data protection,algorithmic bias,transparency,accountability,and informed consent.A comprehensive search adhering to PRISMA guidelines identified 63 studies,with 15 selected for in-depth analysis.Findings indicate significant ethical issues:data privacy needs stringent cybersecurity,biases in AI models must be mitigated,and transparency in AI decision making is essential.The review emphasizes the necessity for updated Health Insurance Portability and Accountability Act(HIPAA)regulations,robust validation mechanisms,and the development of explainable AI models.It also highlights the need for an independent regulatory body to oversee AI integration,ensuring ethical standards and protecting patient welfare.Although AI presents promising benefits,its successful application in plastic surgery hinges on addressing these ethical challenges comprehensively.
文摘The increasing adoption and widespread acceptance of negative pressure wound therapy(NPWT)have paralleled the expansion of its indications in clinical practice.The spectrum of indications for NPWT now extends to encompass soft tissue defects arising from trauma,infection,surgical wound care,and soft tissue grafting procedures.Recent advancements in NPWT devices have introduced various adjuncts,such as instillation of fluids or antibiotics into the wound.These additions empower surgeons to enhance the wound healing environment and contribute to combatting infections more effectively.This review delves into the latest literature addressing the proposed mechanisms underlying NPWT's action,its cost-effectiveness,its impact on patient quality of life,and the essential components necessary for its safe use.The review examines the evidence supporting NPWT's application in managing traumatic extremity injuries,controlling infections,and wound care.While NPWT generally exhibits a low complication rate,surgeons must remain aware of the potential risks linked to its utilization.Moreover,the review explores the widening scope of indications for NPWT,shedding light on prospective avenues for innovation and research in this field.
文摘Aim:In the digital age,artificial intelligence(AI)platforms have gradually replaced traditional manual techniques for information retrieval.However,their effectiveness in conducting academic literature searches remains unclear,necessitating a comparative assessment.This study examined the efficacy of AI search engines(Elicit,Consensus,ChatGPT)vs.manual search for literature retrieval,focusing on the surgical management of trapeziometacarpal osteoarthritis.Methods:The study was executed per the Cochrane Handbook for Systematic Reviews and PRISMA guidelines.AI platforms were given relevant keywords and prompts,while manual searches used PubMed,Cochrane CENTRAL,Web of Science,and Scopus databases from January 1901 to April 2024.The study focused on English-language randomized controlled trials(RCTs)comparing surgical management of trapeziometacarpal osteoarthritis(TMCJ OA).Two independent evaluators screened and extracted data from the studies.Primary outcomes involved the quality and relevancy of studies chosen by both search methods,evaluated by false positive rates and number of studies,including outcomes of interest.Results:The manual search yielded the most results(6,018),followed by Elicit(4,980),Consensus(3,436),and ChatGPT(6).Elicit identified the highest number of RCTs(205)but also had the greatest false positive rate(94%).Ultimately,the manual search identified 23 suitable studies,Elicit found 10,Consensus found 9,and ChatGPT identified only 1.No additional studies were found by AI search engines that were not discovered in the manual search.Conclusion:The findings highlight the potential advantages and drawbacks of AI search engines for literature searches.While Elicit was prone to error,Consensus and ChatGPT were less comprehensive.Significant enhancements in the precision and thoroughness of AI search engines are required before they can be effectively utilized in academia.
