Exploring the Balance Between Artificial Intelligence and Human Expertise in Shaping Breast Reconstruction Outcomes: A Comparative Reflection Study.

Integrating artificial intelligence (AI) in cosmetic surgery presents both transformative opportunities and significant challenges. From preoperative planning to postoperative care, AI offers tools that enhance precision, efficiency, and personalization. However, concerns such as algorithmic bias, ethical implications, and the potential depersonalization of patient care must be carefully considered. This article explores AI’s impact on cosmetic surgery, addressing its benefits, risks, and ethical considerations while proposing solutions on evidence-based strategies for safe and effective AI integration into clinical practice. By evaluating AI-driven advancements in patient education, treatment planning, and postoperative management, we aim to equip surgeons with actionable insights for navigating the future of our field. To fully realize AI’s potential in aesthetic and cosmetic surgery, it is essential to approach its integration through a structured framework that balances innovation with responsibility. This includes identifying key risk areas, implementing mitigation strategies, and outlining best practices for clinical adoption. The following sections are organized around major challenges—such as bias, ethical concerns, and depersonalization—and we aim to offer specific solutions to guide surgeons in applying AI thoughtfully and safely within their own practices. A combination of keywords related to “cosmetic surgery” and “artificial intelligence,” such as “AI trends,” “AI risks,” and “AI in surgery,” was used for a literature search across PubMed and Web of Science. The search was focused on published articles in English, in the past 10 years, to point out the recent trends and risks arising due to the use of AI in cosmetic plastic surgery. The search showed a long list of potential risks and complications associated with the use of AI when not applied carefully. On the contrary, the search also showed its versatility and colossal potential in transforming the field of cosmetic plastic surgery. While AI offers promising advancements in cosmetic plastic surgery, it is essential to navigate its integration carefully. By addressing issues such as potential biases, ethical concerns, and the risk of depersonalization, the field can leverage AI to enhance patient care and outcomes. The future of facial cosmetic plastic surgery with AI holds great potential, but it must be grounded in principles that prioritize patient safety, inclusivity, and the preservation of the human touch.

ObjectiveThe integration of Artificial Intelligence (AI) in healthcare offers opportunities to transform patient care, improve efficiency, and support clinical decision-making. Yet, its implementation is hindered by technical, organizational, and collaborative challenges. This study explores the key success factors for sustainable AI adoption from the perspectives of healthcare professionals (HCPs) and AI experts, with the aim of identifying alignments and gaps that influence collaboration and sustainability.MethodsGuided by the NASSS framework, semi-structured interviews were conducted with four HCPs and five AI experts with experience related to AI technologies in the healthcare sector. Thematic analysis was applied to examine stakeholder perspectives, focusing on gaps and alignments between stakeholders and identifying common patterns.ResultsTransparency leading to trust was the most emphasized factor by both AI experts and HCPs. Alignments were also found in recognizing the importance of interorganizational cooperation, demand-side value, scope of use, usability, responsibility and redefining the roles. Gaps included challenges in cooperation with HCPs, misunderstandings between stakeholders and the need for interdisciplinary experts, insufficient training, concerns over data quality and privacy, and limited attention to usability. Based on the found patterns, a framework is proposed to strengthen collaboration between AI experts and HCPs, enabling effective and sustainable AI implementation in healthcare.ConclusionThis study extends the NASSS framework to a dual-stakeholder context, offering insights into the alignments and gaps between HCPs and AI experts. The proposed framework supports improved collaboration, guiding healthcare professionals, AI developers, and managers toward effective, sustainable AI implementation and fostering mutual understanding for successful adoption.

Plastic Surgery of the Breast: A 75-Year Journey.

In recent decades, the financial sector has undergone a transformation from conventional banking to innovation-based services. The collaboration of artificial intelligence and digital finance solutions has revolutionized financial activities, decision-making, and the customer experience, highlighting the emerging interplay between digital technologies and human involvement. This paper explores the collaborative future of Artificial Intelligence (AI), financial technology (fintech), and human expertise. As AI rapidly transforms the financial services landscape, it's crucial to understand how this technology can enhance rather than replace human capabilities. This study examines the synergetic relationship between AI and human expertise in fintech through a comprehensive literature review and analysis of current trends. The research objectives include analysing the impact of this synergy, assessing how human insights and AI collaboration enhance customer experience, and exploring the benefits and challenges of human-AI integration in fintech. Findings suggest that while AI excels at data processing, pattern recognition, and automation, human skills remain essential for ethical decision-making, complex problem-solving, and building customer relationships. The study concludes that a balanced approach, leveraging AI capabilities and human expertise, is key to driving innovation and improving outcomes in the fintech sector.

BackgroundDespite the unprecedented performance of deep learning algorithms in clinical domains, full reviews of algorithmic predictions by human experts remain mandatory. Under these circumstances, artificial intelligence (AI) models are primarily designed as clinical decision support systems (CDSSs). However, from the perspective of clinical practitioners, the lack of clinical interpretability and user-centered interfaces hinders the adoption of these AI systems in practice.ObjectiveThis study aims to develop an AI-based CDSS for assisting polysomnographic technicians in reviewing AI-predicted sleep staging results. This study proposed and evaluated a CDSS that provides clinically sound explanations for AI predictions in a user-centered manner.MethodsOur study is based on a user-centered design framework for developing explanations in a CDSS that identifies why explanations are needed, what information should be contained in explanations, and how explanations can be provided in the CDSS. We conducted user interviews, user observation sessions, and an iterative design process to identify three key aspects for designing explanations in the CDSS. After constructing the CDSS, the tool was evaluated to investigate how the CDSS explanations helped technicians. We measured the accuracy of sleep staging and interrater reliability with macro-F1 and Cohen κ scores to assess quantitative improvements after our tool was adopted. We assessed qualitative improvements through participant interviews that established how participants perceived and used the tool.ResultsThe user study revealed that technicians desire explanations that are relevant to key electroencephalogram (EEG) patterns for sleep staging when assessing the correctness of AI predictions. Here, technicians wanted explanations that could be used to evaluate whether the AI models properly locate and use these patterns during prediction. On the basis of this, information that is closely related to sleep EEG patterns was formulated for the AI models. In the iterative design phase, we developed a different visualization strategy for each pattern based on how technicians interpreted the EEG recordings with these patterns during their workflows. Our evaluation study on 9 polysomnographic technicians quantitatively and qualitatively investigated the helpfulness of the tool. For technicians with <5 years of work experience, their quantitative sleep staging performance improved significantly from 56.75 to 60.59 with a P value of .05. Qualitatively, participants reported that the information provided effectively supported them, and they could develop notable adoption strategies for the tool.ConclusionsOur findings indicate that formulating clinical explanations for automated predictions using the information in the AI with a user-centered design process is an effective strategy for developing a CDSS for sleep staging.

"Background: Robotic surgery currently offers a number of benefits in specific areas of plastic and reconstructive surgery such as head and neck and microsurgery, however, many subspecialties are yet to benefit from robotic operating techniques. In this paper we review recent developments in the use of robotics for head and neck, breast reconstruction, microneural, lymphoedema, craniofacial and aesthetic surgery. Methods: A literature review of English-only articles on the use of robots in plastic and reconstructive surgery was conducted using the PubMed and Web of Science databases along with a free text search using the terms ‘robotic surgery plastic surgery’, ‘robotic-assisted plastic surgery’, ‘robot-assisted reconstructive surgery’, ‘da Vinci plastic surgery’, ‘da Vinci reconstructive surgery’ and ‘supermicrosurgery’. No limitation was placed on time from publication. Results: Of the studies reviewed, the most beneficial to reconstructive surgery is the area of the head and neck, with the most common procedures being transoral surgeries and microvascular procedures. Robotic assisted breast reconstructive surgery is a promising field. The main current limitations are the lack of adequate microsurgical instrumentation and the prolonged setting-up and operative time. Conclusion: As technology improves the outlook for robotic surgery is optimistic. Areas like microsurgery, head and neck, cleft palate, breast reconstructive surgery, lymphatic and microneural surgery already demonstrate promising results. Further technological advances and evidence-based studies are necessary to address current limitations such as appropriate microsurgical instrumentation, high costs and prolonged operative time."

Machine Learning Nomogram to Predict Breast Reconstruction Complications with and without Radiation

Background: Artificial intelligence (AI) is increasingly applied in medicine, including plastic and reconstructive surgery, to enhance diagnostic accuracy, surgical planning, outcome evaluation, and efficiency. However, integration into clinical practice remains limited. This systematic review assessed the current peer-reviewed clinical applications of AI across all plastic surgery subspecialties. Methods: Following PRISMA guidelines, we searched Medline, Embase, Cochrane, and PubMed for English-language studies (2015–2025) on AI in plastic/reconstructive surgery. Inclusion was limited to peer-reviewed clinical studies involving patients or patient data. Data on subspecialty, AI use-case, performance, and stage of development were extracted. Study quality was appraised with a modified MINORS tool. Results: The initial search yielded 2,153 records; 24 studies met all inclusion criteria. All major subspecialties were represented, especially aesthetic, breast and craniofacial. AI was applied across all subdisciplines, most commonly in aesthetic/cosmetic and craniofacial surgery. Key applications included image-based diagnostics, predictive analytics for surgical outcomes, augmented reality for surgical planning, and chatbot tools for patient education. Many algorithms achieved high accuracy or expert-level performance in research settings. However, the research was largely early-stage: most studies were retrospective and focused on model development (preclinical) with only one study demonstrating clinical implementation as of 2022. Quality appraisal showed that while nearly all studies had clearly stated aims and appropriate endpoints, only ~20% were prospective and only ~10–15% compared AI performance to current standards or clinicians. Overfitting was a concern, with just ~40% reporting use of validation techniques. Overall, included studies showed moderate methodological quality. Conclusions: AI applications in plastic surgery expanded substantially over the last decade, showing promise in improving diagnostic accuracy, surgical planning, and patient counseling. Nevertheless, most studies remain preliminary, with limited clinical translation to date. Stronger study designs – including prospective trials, external validation, and direct comparisons to standard care – are needed to establish the real-world efficacy of AI. Future research and clearer regulatory guidance are essential to safely integrate AI into routine plastic surgical practice.

This chapter delves into the perceptions of artificial intelligence (AI) experts on the societal implications, governance, and ethical responsibilities associated with AI. Drawing on qualitative research, including interviews with six AI experts, the study investigates three key questions: experts' perceptions of AI's societal impacts, their visions for an AI-governed society, and their views on their responsibilities in addressing AI's consequences. The findings suggest that AI experts emphasize the importance of balancing innovation with education and regulation to ensure AI's responsible development and application, leaving out important questions to analyze further, such as their role in AI political governance, and the interest of wide public participation in the process of AI creation.

BackgroundA large language model is a type of artificial intelligence (AI) model that opens up great possibilities for health care practice, research, and education, although scholars have emphasized the need to proactively address the issue of unvalidated and inaccurate information regarding its use. One of the best-known large language models is ChatGPT (OpenAI). It is believed to be of great help to medical research, as it facilitates more efficient data set analysis, code generation, and literature review, allowing researchers to focus on experimental design as well as drug discovery and development.ObjectiveThis study aims to explore the potential of ChatGPT as a real-time literature search tool for systematic reviews and clinical decision support systems, to enhance their efficiency and accuracy in health care settings.MethodsThe search results of a published systematic review by human experts on the treatment of Peyronie disease were selected as a benchmark, and the literature search formula of the study was applied to ChatGPT and Microsoft Bing AI as a comparison to human researchers. Peyronie disease typically presents with discomfort, curvature, or deformity of the penis in association with palpable plaques and erectile dysfunction. To evaluate the quality of individual studies derived from AI answers, we created a structured rating system based on bibliographic information related to the publications. We classified its answers into 4 grades if the title existed: A, B, C, and F. No grade was given for a fake title or no answer.ResultsFrom ChatGPT, 7 (0.5%) out of 1287 identified studies were directly relevant, whereas Bing AI resulted in 19 (40%) relevant studies out of 48, compared to the human benchmark of 24 studies. In the qualitative evaluation, ChatGPT had 7 grade A, 18 grade B, 167 grade C, and 211 grade F studies, and Bing AI had 19 grade A and 28 grade C studies.ConclusionsThis is the first study to compare AI and conventional human systematic review methods as a real-time literature collection tool for evidence-based medicine. The results suggest that the use of ChatGPT as a tool for real-time evidence generation is not yet accurate and feasible. Therefore, researchers should be cautious about using such AI. The limitations of this study using the generative pre-trained transformer model are that the search for research topics was not diverse and that it did not prevent the hallucination of generative AI. However, this study will serve as a standard for future studies by providing an index to verify the reliability and consistency of generative AI from a user’s point of view. If the reliability and consistency of AI literature search services are verified, then the use of these technologies will help medical research greatly.

Artificial Intelligence (AI) is reshaping preoperative preparation for both patients and surgeons in plastic surgery, offering innovative tools that improve efficiency, precision, and outcomes. This manuscript examines the growing role of AI in preoperative patient education, surgeon preparation, and clinical workflows, and provides an overview of its main domains of impact. AI-powered tools show great promise in consultations, providing personalized, clear patient education, simplifying administrative tasks such as drafting consultation letters, and enhancing communication. For surgeons, AI can support preoperative planning with reinforcement learning, advanced imaging algorithms, and virtual simulations. These technologies assist in accurate surgical decision making, as seen in deep inferior epigastric artery perforator (DIEP) flap breast reconstruction, where AI may be able to reduce perforator analysis times while maintaining precision. In aesthetic surgery, AI-driven 3D imaging and generative algorithms enable realistic postoperative simulations, improving communication and helping patients set realistic expectations. From a patient communication perspective, AI promotes patient-centered care by simplifying medical jargon, making information more accessible, and fostering better understanding and adherence. Despite challenges such as ethical considerations, data security, and algorithmic bias, AI’s integration into preoperative workflows highlights its potential to improve patient and surgeon experiences. By enhancing patient education, refining surgical strategies, and optimizing preparation, AI is redefining how care is delivered in plastic surgery, aiming for better patient satisfaction and outcomes. Collaboration among clinicians, engineers, and ethicists remains crucial to ensure the responsible use of AI, balancing innovation with the core values of medical care.

All women undergoing a mastectomy have the right to reconstruction. However, many women do not receive reconstruction and many more are not aware of all the reconstructive options available to them. Travel distance to a center that provides reconstruction and subsequent follow-up may be a contributing factor to this disparity especially among those who seek microsurgical options. Telehealth, which provides patients with remote video consultations and decreases the travel burden, may be a solution to optimize the accessibility of breast reconstruction for these patients. The purpose of this study was to discuss the efficacy and reliability of telehealth to overcome geographic barriers. Patients who received breast reconstruction and participated in video telehealth visits between February and May 2020 were included in this study. Patient demographics, comorbidities, and clinical outcomes were collected. Video telehealth encounters were reviewed to determine specific concerns and questions discussed during these encounters. A total of 235 breast reconstruction surgery patient encounters were recorded for 4 plastic surgeons who offer microsurgical breast reconstruction. Eighty-eight patients (37.4%) were seen as telehealth visits, 20 (22.7%) of which were new patient visits. Eight (9.09%) patients were microsurgical breast reconstruction candidates and 25 (28.4%) were following-up after microsurgical breast reconstruction. The majority of telehealth visits included normally healing wounds in the postoperative patient. Telehealth provides an avenue for premastectomy consultation, second opinion visits, and postoperative follow-up for patients who have geographical barriers precluding them from reaching plastic surgeons who perform all types of breast reconstruction.

Due to the limited surgical variety, laggard surgical approaches and the unpopularity of breast reconstruction, domestic techniques on breast reconstructive and plastic surgery was relatively backward internationally. Recently, it has made enormous progress generally on the following six aspects: ①The awareness on breast reconstruction has been fully advocated and popularized; ②Breast reconstructive techniques have been creatively applied; ③Breakthrough has been made on simplex fat grafting based breast reconstruction; ④The improvement on surgical accuracy and security has been acquired via the application of imaging technologies, such as CTA and ICG; ⑤Three dimensional (3D) technique has been applied on the whole breast plastic surgical process from the pre-operative design to the post-operative follow-ups; ⑥Distinctive and advanced system of implant-based breast augmentation has formed in China. Articles in this column could be regarded as the epitome of recent developments on breast reconstructive and plastic surgery. Looking forward to more relevant innovative achievements and findings with Chinese characteristics in the future. Key words: Mammaplasty; Breast reconstruction; Augmentation mammoplasty; Digitization; Plastic surgery

Machine Learning and Artificial Intelligence (AI) more broadly have great immediate and future potential for transforming almost all aspects of medicine. However, in many applications, even outside medicine, a lack of transparency in AI applications has become increasingly problematic. This is particularly pronounced where users need to interpret the output of AI systems. Explainable AI (XAI) provides a rationale that allows users to understand why a system has produced a given output. The output can then be interpreted within a given context. One area that is in great need of XAI is that of Clinical Decision Support Systems (CDSSs). These systems support medical practitioners in their clinic decision-making and in the absence of explainability may lead to issues of under or over-reliance. Providing explanations for how recommendations are arrived at will allow practitioners to make more nuanced, and in some cases, life-saving decisions. The need for XAI in CDSS, and the medical field in general, is amplified by the need for ethical and fair decision-making and the fact that AI trained with historical data can be a reinforcement agent of historical actions and biases that should be uncovered. We performed a systematic literature review of work to-date in the application of XAI in CDSS. Tabular data processing XAI-enabled systems are the most common, while XAI-enabled CDSS for text analysis are the least common in literature. There is more interest in developers for the provision of local explanations, while there was almost a balance between post-hoc and ante-hoc explanations, as well as between model-specific and model-agnostic techniques. Studies reported benefits of the use of XAI such as the fact that it could enhance decision confidence for clinicians, or generate the hypothesis about causality, which ultimately leads to increased trustworthiness and acceptability of the system and potential for its incorporation in the clinical workflow. However, we found an overall distinct lack of application of XAI in the context of CDSS and, in particular, a lack of user studies exploring the needs of clinicians. We propose some guidelines for the implementation of XAI in CDSS and explore some opportunities, challenges, and future research needs.

Background: In developing countries, late presentation of breast cancer is a common problem because of the fear of losing the breast as well as ignorance of post mastectomy reconstructive options. In Nigeria, about two-third of patients with this disease present late at a time when therapy offers minimal benefit. Purpose: This review is aimed at summarizing the role of the plastic surgeon in reducing the problem of mutilating surgery in breast cancer and alert surgeons in the developing countries on the need to educate women on various reconstructive options that improve the look and psychological/self image problems that often accompany mastectomy. Materials and methods: Literature review using Medline, Pubmed, AJOL and Google search engines was carried out. Result: Over 38,000 articles and abstracts were noted as discussing the subject matter of breast cancer and reconstructive procedures. There is a dearth of write ups from Africa on breast reconstruction. Several options exist for the reconstructive surgeon to assist in reducing the psychological and image problems that usually accompany mastectomy in breast cancer patients especially in developing countries. Many of the options are discussed in this paper. Conclusion: The place of an oncoplastic surgeon is important especially in the developing world where plastic surgeons are few and the general work load is heavy. Education of women on breast reconstructive options will encourage early presentation and better prognosis. Key words: Breast cancer, post mastectomy breast reconstruction, late presentation of breast cancer, developing countries.