Artificial Intelligence in Facial Plastic Surgery: A Review of Current Applications, Future Applications, and Ethical Considerations

Applications of Artificial Intelligence in Facial Plastic and Reconstructive Surgery: A Narrative Review

Importance: Artificial intelligence (AI) has made invaluable contributions to the technologic advancements across many fields. It is transforming health care and may have a role in improving patient outcomes in facial plastic and reconstructive surgery (FPRS). Observations: In recent years, new automated approaches to simulating and analyzing outcomes using AI have emerged. Advances in rhinoplasty, facelifts, orthognathic surgery, facial reanimation, and preoperative consultation are currently being developed in FPRS. Conclusions and Relevance: Applications of AI have been applied to assist facial plastic surgeons in the preoperative stage, intraoperative planning process, and objective assessment of postoperative outcomes. The application of AI provides avenues to improve postoperative outcomes, while also optimizing patient care.

Management of Burns: Multi-Center Assessment Comparing AI Models and Experienced Plastic Surgeons

Background: Burn injuries require accurate assessment for effective management, and artificial intelligence (AI) is gaining attention in burn care for diagnosis, treatment planning, and decision support. This study compares the effectiveness of AI-driven models with experienced plastic surgeons in burn assessment and management. Methods: Ten anonymized burn images of varying severity and anatomical location were selected from publicly available databases. Three AI systems (ChatGPT-4o, Claude, and Kimi AI) analyzed these images, generating clinical descriptions and management plans. Three experienced plastic surgeons reviewed the same images to establish a clinical reference standard and evaluated AI-generated recommendations using a five-point Likert scale for accuracy, relevance, and appropriateness. Statistical analyses, including Cohen's kappa coefficient, assessed inter-rater reliability and comparative accuracy. Results: AI models showed high diagnostic agreement with clinicians, with ChatGPT-4o achieving the highest Likert ratings. However, treatment recommendations varied in specificity, occasionally lacking individualized considerations. Readability scores indicated that AI-generated outputs were more comprehensible than the traditional medical literature, though some recommendations were overly simplistic. Cohen's kappa coefficient suggested moderate to high inter-rater agreement among human evaluators. Conclusions: While AI-driven models demonstrate strong diagnostic accuracy and readability, further refinements are needed to improve treatment specificity and personalization. This study highlights AI's potential as a supplementary tool in burn management while emphasizing the need for clinical oversight to ensure safe and individualized patient care.

The Transformative Role of Artificial Intelligence in Plastic and Reconstructive Surgery: Challenges and Opportunities

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 the specific objective of identifying evidence-based applications, implementation challenges, and emerging opportunities that will shape the future of the specialty. Methods: A comprehensive narrative review was conducted analyzing the integration of AI technologies in plastic surgery, including preoperative planning, intraoperative applications, postoperative monitoring, and quality improvement. Challenges related to implementation, ethics, and regulatory frameworks were also examined, along with emerging technological trends that will shape future practice. Results: AI applications in plastic surgery demonstrate significant potential across multiple domains. In preoperative planning, AI enhances risk assessment, outcome prediction, and surgical simulation. Intraoperatively, AI-assisted robotics enables increased precision and technical capabilities beyond human limitations, particularly in microsurgery. Postoperatively, AI improves complication detection, pain management, and outcomes assessment. Despite these benefits, implementation faces challenges including data privacy concerns, algorithmic bias, liability questions, and the need for appropriate regulatory frameworks. Future directions include multimodal AI systems, federated learning approaches, and integration with extended reality and regenerative medicine technologies. Conclusions: The integration of AI into plastic surgery represents a significant opportunity to enhance surgical precision, improve outcome prediction, and expand the boundaries of what is surgically possible. However, successful implementation requires addressing ethical considerations and maintaining the human elements of surgical care. Plastic surgeons must actively engage with AI development to ensure these technologies address genuine clinical needs while aligning with the specialty's core values of restoring form and function, alleviating suffering, and enhancing quality of life.

Artificial Intelligence Scribe and Large Language Model Technology in Healthcare Documentation: Advantages, Limitations, and Recommendations

Artificial intelligence (AI) scribe applications in the healthcare community are in the early adoption phase and offer unprecedented efficiency for medical documentation. They typically use an application programming interface with a large language model (LLM), for example, generative pretrained transformer 4. They use automatic speech recognition on the physician-patient interaction, generating a full medical note for the encounter, together with a draft follow-up e-mail for the patient and, often, recommendations, all within seconds or minutes. This provides physicians with increased cognitive freedom during medical encounters due to less time needed interfacing with electronic medical records. However, careful proofreading of the AI-generated language by the physician signing the note is essential. Insidious and potentially significant errors of omission, fabrication, or substitution may occur. The neural network algorithms of LLMs have unpredictable sensitivity to user input and inherent variability in their output. LLMs are unconstrained by established medical knowledge or rules. As they gain increasing levels of access to large corpora of medical records, the explosion of discovered knowledge comes with large potential risks, including to patient privacy, and potential bias in algorithms. Medical AI developers should use robust regulatory oversights, adhere to ethical guidelines, correct bias in algorithms, and improve detection and correction of deviations from the intended output.

Comparison of Aesthetic Results of Mercedes-Y Versus Inverted-V Incision for Umbilical Reconstruction: A Randomized Clinical Trial

BACKGROUND

The appearance and position of navel which are considered as a marker for overall abdominal aesthetics, are important for the final results of abdominal surgeries. However, reconstructing and improving its appearance have been a challenge in plastic surgery.

HYPOTHESIS

In this study, we aimed to compare satisfaction of the patients and the plastic surgery professors with the aesthetic results of the two methods of umbilical reconstruction: Mercedes (Y) incision versus inverted-V incision.

METHODS

This is a randomized clinical trial performed on umbilicoplasty patients following abdominoplasty, breast reconstruction, or abdominal flap surgery whom referred to our center. They were divided into two equal groups of twenty patients with Mercedes (Y) incision and patients with inverted-V incision. The results of surgery were compared three months after the surgery by the opinions of patients, plastic surgery professors, and unbiased observers.

RESULTS

The average scores of patients, professors, and observers showed that Mercedes (Y) had significantly higher scores compared to inverted-V incision in terms of position, size, shape, natural appearance, and the overall satisfaction. Surgical complications including stenosis, necrosis, and wound dehiscence were not statistically different in our small sample size.

CONCLUSION

In this study, Mercedes (Y) incision was preferred by all of the groups in all five parameters that were assessed including size, shape, position, natural appearance, and overall satisfaction. The simplicity of the Y incision with less visible scar makes it a suitable method for further investigations with a larger sample size. Level of Evidence I This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Role of Artificial Intelligence and Machine Learning in Facial Aesthetic Surgery: A Systematic Review

Objective: To analyze the quality of artificial intelligence (AI) and machine learning (ML) tools developed for facial aesthetic surgery. Data Sources: Medline, Embase, CINAHL, Central, Scopus, and Web of Science databases were searched in February 2024. Study Selection: All original research in adults undergoing facial aesthetic surgery was included. Pilot reports, case reports, case series (n < 5), conference proceedings, letters (except research letters and brief reports), and editorials were excluded. Main Outcomes and Measures: Facial aesthetic surgery procedures employing AI and ML tools to measure improvements in diagnostic accuracy, predictive outcomes, precision patient counseling, and the scope of facial aesthetic surgery procedures where these tools have been implemented. Results: Out of 494 initial studies, 66 were included in the qualitative analysis. Of these, 42 (63.6%) were of "good" quality, 20 (30.3%) were of "fair" quality, and 4 (6.1%) were of "poor" quality. Conclusion: AI improves diagnostic accuracy, predictive capabilities, patient counseling, and facial aesthetic surgery treatment planning.

Ethical Considerations and Equipoise in Cancer Surgery

The changing landscape of cancer surgery requires ongoing consideration of ethical issues to ensure patient-centered care and fair access to treatments. With technological advancements and the global expansion of surgical interventions, healthcare professionals must navigate complex ethical dilemmas related to patient autonomy, informed consent, and the impact of new technologies on the physician-patient relationship. Additionally, ethical principles and decision-making in oncology, especially in the context of genetic predisposition to breast cancer, highlight the importance of integrating patient knowledge, preferences, and alignment between goals and treatments. As global surgery continues to grow, addressing ethical considerations becomes crucial to reduce disparities in access to surgical interventions and uphold ethical duties in patient care. Furthermore, the rise of digital applications in healthcare, such as digital surgery, requires heightened awareness of the unique ethical issues in this domain. The ethical implications of using artificial intelligence (AI) in robotic surgical training have drawn attention to the challenges of protecting patient and surgeon data, as well as the ethical boundaries that innovation may encounter. These discussions collectively emphasize the complex ethical issues associated with surgical innovation and underscore the importance of upholding ethical standards in the pursuit of progress in the field. In this study, we thoroughly analyzed previous scholarly works on ethical considerations and equipoise in the field of oncological surgery. Our main focus was on the use of AI in this specific context.

Applications of artificial intelligence in facial plastic and reconstructive surgery: a systematic review

PURPOSE OF REVIEW

Arguably one of the most disruptive innovations in medicine of the past decade, artificial intelligence is dramatically changing how healthcare is practiced today. A systematic review of the most recent artificial intelligence advances in facial plastic surgery is presented for surgeons to stay abreast of the latest in our field.

RECENT FINDINGS

Artificial intelligence applications developed for use in perioperative patient evaluation and management, education, and research in facial plastic surgery are highlighted. Selected themes include automated facial analysis with landmark detection, automated facial palsy grading and emotional assessment, generation of artificial facial profiles for testing and model training, automated postoperative patient communications, and improving ethnicity-sensitive facial morphometry norms. Inherent bias can exist in artificial intelligence models, and care must be taken to utilize algorithms trained with diverse datasets.

SUMMARY

Artificial intelligence tools are helping clinicians provide more standardized, objective, and efficient care to their patients. Increasing surgeon awareness of available tools, and their widespread implementation into clinical workflows are the next frontier. Ethical considerations must also shape the adoption of any artificial intelligence functionality. As artificial intelligence applications become a fixture in medicine, surgeons must employ them effectively to stay at the vanguard of modern medicine.

Artificial intelligence in maxillofacial and facial plastic and reconstructive surgery

PURPOSE OF REVIEW

To provide a current review of artificial intelligence and its subtypes in maxillofacial and facial plastic surgery including a discussion of implications and ethical concerns.

RECENT FINDINGS

Artificial intelligence has gained popularity in recent years due to technological advancements. The current literature has begun to explore the use of artificial intelligence in various medical fields, but there is limited contribution to maxillofacial and facial plastic surgery due to the wide variance in anatomical facial features as well as subjective influences. In this review article, we found artificial intelligence's roles, so far, are to automatically update patient records, produce 3D models for preoperative planning, perform cephalometric analyses, and provide diagnostic evaluation of oropharyngeal malignancies.

SUMMARY

Artificial intelligence has solidified a role in maxillofacial and facial plastic surgery within the past few years. As high-quality databases expand with more patients, the role for artificial intelligence to assist in more complicated and unique cases becomes apparent. Despite its potential, ethical questions have been raised that should be noted as artificial intelligence continues to thrive. These questions include concerns such as compromise of the physician-patient relationship and healthcare justice.

Ethics for AI in Plastic Surgery: Guidelines and Review

INTRODUCTION

Artificial intelligence (AI) holds the potential to revolutionize medicine, offering vast improvements for plastic surgery. While human physicians are limited to one lifetime of experience, AI is poised to soon surpass human capabilities, as it draws on limitless information and continuous learning abilities. Nevertheless, as AI becomes increasingly prevalent in this domain, it gives rise to critical ethical considerations that must be addressed by professionals.

MATERIALS AND METHODS

This work reviews the literature referring to the ethical challenges brought on by the ever-expanding use of AI in plastic surgery and offers guidelines for its application.

RESULTS

Ethical challenges include the disclosure of use of AI by caregivers, validation of decision-making, data privacy, informed consent and autonomy, potential biases in AI systems, the opaque nature of AI models, questions of liability, and the need for regulations.

CONCLUSIONS

There is a lack of consensus for the ethical use of AI in plastic surgery. Guidelines, such as those presented in this work, are needed within each discipline of medicine to respond to important ethical considerations for the safe use of AI.

LEVEL OF EVIDENCE V

This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .