The editorial examines several of the most active research fronts where AI is being tested in plastic surgery, and its conclusions are strikingly cautious. The central message is that while AI has already proven useful in non-clinical settings such as communication, accounting, feedback collection, and office management, the technology has not yet accumulated sufficient evidence to justify routine use in clinical practice. That gap between enthusiasm and proof, the author suggests, is where the specialty’s attention should now be focused.

One of the most debated applications is the computerized assessment of aesthetic outcomes. Plastic surgery has long struggled with the subjective nature of judging surgical results, and AI-driven computer vision promises to replace human impression with numbers. A study by Varghaei and colleagues assembled what is likely the largest curated paired pre- and postoperative facial image dataset to date, comprising 7,160 photographs from 1,259 patients. The team introduced a scalable, interpretable computer-vision framework designed to quantify aesthetic outcomes of facial plastic surgery using frontal photographs. On its face, this seems like a decisive step toward objectivity, but the editorial raises a fundamental objection: quantification does not guarantee objectivity.

The problem lies in how these systems learn. Current AI technology is dominated by multimodal large language models trained on human input, and the way humans train these models inevitably shapes the opinions they produce. The editorial coins the term compjective to describe this hybrid state, in which a machine’s judgment is neither fully objective nor entirely subjective. The authors of the facial analysis study themselves acknowledged that facial attractiveness, though inherently subjective, is often associated with geometric regularities such as bilateral symmetry, proportionality, and averageness. That concession raises an uncomfortable question: can computerized assessment of aesthetic outcomes ever truly be objective?

Independent evidence suggests the answer, for now, is no. A comparative evaluation by Haider and colleagues tested leading multimodal large language models, including ChatGPT-4o, GPT-4, Gemini, and Claude, on their ability to assess aesthetic features through both qualitative and quantitative analysis. The verdict was sobering. Without fine-tuning or integration with specialized computer-vision architectures, none of the models could provide a reliable aesthetic assessment. In practical terms, the general-purpose systems that most people call artificial intelligence today do not have the capacity to replace a surgeon’s trained eye and accumulated experience in evaluating an aesthetic result. The editorial’s assessment is that AI-driven computer vision has not yet proven to be a reliable resource for clinical integration and requires substantial advancement, though it may serve as a supplementary tool for quantifying treatment results in research settings.

A second frontier is continuous monitoring, where the case for machine assistance is arguably stronger. Every surgeon knows the classical signs of inflammation, calor, dolor, rubor, tumor, and functio laesa, and every training surgeon has endured the burden of watching flaps overnight with instructions as vague as calling if the color changes. A monitoring system that never gets drowsy and never loses focus could be a welcome alternative to a tired colleague. Kim and colleagues developed and tested an automated free flap monitoring system based on artificial intelligence, and the results were promising: sensitivity for detecting venous insufficiency reached 97.5 percent, while sensitivity for arterial compromise reached 92.8 percent. These figures suggest that supplemental monitoring and early warning systems could genuinely optimize patient care, but the editorial is careful to note that such technologies have yet to be evaluated in a clinical trial setting.

That trial evidence may not be far off. Several clinical studies are currently underway focusing on AI-assisted monitoring, diagnosis, and detection. Among them are WISDOM, a trial using AI within digital wound monitoring of surgical wounds to prioritize non-healing wounds for urgent review, registered as NCT06475703; AnAsToMoSIs, a study of artificial intelligence approaches to the monitoring of microvascular free-flap inpatients, registered as ISRCTN36476735; SeeWound2, a clinical evaluation of a digital wound-assessment device using AI-based image analysis, registered as NCT07211295; and an AI risk assessment model for complication prevention in plastic surgery, registered as NCT06507384. Most of these studies are observational or device precision studies, which means the field is still in the phase of establishing that the tools work as claimed, not yet proving that they improve outcomes.

Beneath these specific applications lies a deeper set of methodological problems that the editorial identifies as endemic to the literature. Many published AI models derive from single centers or demographically narrow datasets, which severely restricts their applicability to larger and more varied patient communities. When such models are ported to different populations, performance will likely degrade, because those populations do not resemble the original training data. There is little published work on cross-ethnicity fairness audits or performance stratification across different phenotypes, and many studies omit the detailed metadata that would make post-hoc analysis, standardization, or reproducibility possible. Patient-reported outcomes are frequently missing from pilot studies, a flaw the editorial calls major, since a patient’s understanding of a successful outcome may differ substantially from that of a computerized assessment program. Ultimately, the argument goes, AI metrics must map to patient-reported outcomes, to the surgeon’s decision-making, or to demonstrable improvements in care, and current models show signs of both selection bias and aesthetic bias.

There are, however, examples of success that point the way forward. The editorial cites work by Ravi Dhawan and colleagues reviewing FDA-approved AI-utilizing tools in the wider surgical field, which found that the devices that have cleared regulatory hurdles share common features, including necessity and efficiency. Future pioneers of AI technology in plastic and reconstructive surgery, the editorial argues, should weigh the necessity, safety, and reliability of their tools in improving patient outcomes, reducing complications, and ensuring minimum disruption to existing clinical standards and practices. A tool that is merely novel, in other words, is not enough.

To close the gap between promise and proof, the editorial lays out a set of steps for responsible integration. Reporting standards should be mandated, covering everything from methods to metadata, with proper study registration, open access data sharing, and detailed transparency as prerequisites for a reliable tool. Validation should be prospective and pre-registered, ideally multi-center, because retrospective validation is subpar for supporting the effectiveness of a new tool, even one designed only as a supplement. A patient-centered approach should govern development, with patient feedback included in assessing effectiveness and sufficient fairness audits, subgroup analyses, and stratification of performance by age, sex, ethnicity, and skin tone to avoid perpetuating aesthetic biases. Surgeon-AI workflow integration trials should investigate how AI metrics influence surgical planning, patient counseling, shared decision-making, and interrater variability, with clinician feedback loops refining the AI’s decision logic during testing. Finally, regulatory and consent frameworks need attention, since existing bodies such as the FDA and CE marking schemes do not define specific approval pathways for AI technology, and without a standardized approach governing patient consent and data protection, these tools cannot be freely deployed. The editorial’s conclusion is blunt: while the specialty is prepared for AI that optimizes care and improves outcomes, existing research does not yet provide sufficient evidence of safety for clinical integration, and future work must incorporate both patient and surgeon feedback while meeting the same methodological and reporting rigor that has become standard in clinical trials.

Subject of Research: Responsible and evidence-based integration of artificial intelligence into plastic and reconstructive surgery

Article Title: Responsible and safe integration of AI technology into clinical practice: accommodating the inevitable technological change

Article References: Sinelnikov, M. Y. (2025). Responsible and safe integration of AI technology into clinical practice: accommodating the inevitable technological change. BMC Plastic and Reconstructive Surgery, 1(1), Article 6. https://doi.org/10.1186/s44452-025-00006-4

Image Credits: AI Generated

DOI: 10.1186/s44452-025-00006-4

Keywords: artificial intelligence, plastic surgery, reconstructive surgery, computer vision, large language models, free flap monitoring, clinical trials, patient outcomes, FDA regulation, aesthetic assessment, fairness audits, surgical innovation

The study, authored by Sam El Abbadi of Klinikum Kassel in Germany and Rushil R. Dang of Henry Ford Hospital in Detroit, introduces what the authors call a perforator-based sentinel skin island. Instead of modifying the main skin paddle or attaching extra monitoring devices, the technique harvests a small additional island of skin, at least two centimeters by two centimeters, supplied by a perforating blood vessel branching off the flap’s main pedicle in the proximal forearm. Once the flap is transferred and sewn into place, this sentinel island is positioned along the cervical incision line in the neck, where clinicians can inspect it directly every day.

The logic is deceptively simple. Clinical examination remains the gold standard for assessing free flaps, because color, capillary refill, temperature and turgor tell an experienced observer more, faster, than any machine. Devices such as acoustic Doppler probes, implantable Dopplers, near-infrared spectroscopy, hyperspectral imaging and tissue oximetry all have roles, but none replaces a trained eye on visible skin. When a flap is buried, as in pharyngo-esophageal reconstruction after throat cancer, that eye has nothing to see. The sentinel island restores visibility without disturbing the reconstruction itself.

The surgical workflow begins before the first incision. The authors recommend marking the standard radial artery flap design and then using an ultrasound probe to trace the pedicle between the brachioradialis and flexor carpi radialis muscles in a distal-to-proximal direction. Typically, in the middle third of the forearm, a septocutaneous perforator can be identified and marked. The monitor island is then drawn around that perforator, with roughly four to five centimeters of separation from the proximal border of the traditional distal skin paddle. That spacing matters: it gives the monitor island enough reach to sit in the neck without kinking or twisting the main pedicle. While a perforator can, in principle, be found intraoperatively without imaging, the authors found that preoperative ultrasound makes the harvest faster and more reliable.

During the operation, the sequence is deliberately reversed compared with conventional practice. The surgeons first open the septum between the two forearm muscles, identify the pedicle along the proximal aspect of the traditional skin paddle, and trace it until the perforator comes into view. Because these perforators can be small in caliber and branch into several delicate terminal vessels feeding the skin, the authors advise against skeletonizing them, which risks injury or spasm. Instead, they recommend incising the fascia on both sides and carrying a cuff of fascia along the perforator’s course, protecting the fragile vessel within it. Only after the proximal pedicle and perforator are secured are the markings for the sentinel island finalized and incised.

The rest of the harvest proceeds in familiar fashion. The traditional distal skin paddle is raised in a suprafascial plane, with careful protection of the cephalic vein and the superficial radial nerve on the radial side. The distal radial artery and its venae comitantes are ligated and divided, the tourniquet is released, and the viability of both the main paddle and the monitor paddle is confirmed clinically and with indocyanine green fluorescence angiography before the pedicle is divided. After the flap is tubed to reconstruct the pharyngo-esophageal defect and the microvascular anastomoses are completed, the sentinel island is sutured into the cervical incision line, deliberately away from the stoma site, and the neck is closed in the standard manner. At two-week follow-up, the monitor island appeared healthy in the reported case.

The technique is not the first attempt to solve the buried-flap problem, and the authors situate their method carefully within a crowded literature. Furuta and colleagues described a small distal external monitoring paddle created by de-epithelializing part of the main skin paddle, an approach that works but consumes reconstructable skin and limits the monitor’s reach and flexibility. Pellini and colleagues based a monitor island on venous flow through the cephalic vein, but that requires using the cephalic vein for the venous anastomosis and makes islanding a superficial paddle difficult. Cho and colleagues described a septocutaneous technique but reported difficulty finding proximal perforators and cases of perforator torsion that produced false-positive alarms. A team from Chang Gung Memorial Hospital later demonstrated the reliability of harvesting two skin paddles from a single radial forearm donor site, particularly in patients undergoing trismus release.

Against that backdrop, the new approach claims several distinct advantages. The monitor island shares the flap’s main pedicle, so it does not depend on a separate vascular system such as the cephalic vein, which the authors argue reduces the risk of false-positive findings. The design of the traditional distal skin paddle is left completely untouched, preserving the full surface area available for reconstruction. Because no de-epithelialization is needed, the technique avoids the impaired flap inset and fistula risk that can accompany folded or partially de-epithelialized paddles. And the additional dissection required to raise the monitor paddle is minimal and time-efficient. The authors also report applying the same monitoring strategy beyond pharyngo-esophageal defects, including reconstructions of the tonsillar region where the flap is barely visible and oral cavity reconstructions in patients with severe trismus, in whom examining the main paddle postoperatively would be extremely difficult.

The authors are candid about the limitations. The series is small, and the study lacks objective monitoring endpoints beyond free flap survival itself. As previous work has shown, a monitor paddle can misreport the state of the flap, and the delicate perforators remain vulnerable to twisting, which could still generate false signals. Those caveats matter in a field where a single missed flap failure can be catastrophic and where false alarms can trigger unnecessary reoperations. Broader experience and comparative data will be needed before the technique can be considered validated rather than promising.

Even so, the contribution fits a broader trend in reconstructive microsurgery: rather than adding more technology to the postoperative ward, surgeons are redesigning the flap itself so that the oldest monitoring tool of all, direct clinical inspection, remains available even when the reconstruction is hidden from view. For patients facing some of the most demanding reconstructions in head and neck oncology, a two-centimeter island of skin tucked into the incision line could mean the difference between catching a vascular crisis in time and discovering it too late. The technique, the authors conclude, may serve as a practical adjunct that simplifies monitoring of the radial artery forearm free flap across a variety of challenging clinical scenarios.

Subject of Research: Perforator-based sentinel skin islands for monitoring buried radial artery forearm free flaps in head and neck reconstruction

Article Title: Perforator based sentinel skin Island for monitoring of buried radial artery forearm free flaps in head and neck reconstruction: a technical note and case series

Article References: El Abbadi, S., & Dang, R. R. (2026). Perforator based sentinel skin Island for monitoring of buried radial artery forearm free flaps in head and neck reconstruction: a technical note and case series. BMC Plastic and Reconstructive Surgery, 2(1), Article 7. https://doi.org/10.1186/s44452-026-00019-7

Image Credits: AI Generated

DOI: 10.1186/s44452-026-00019-7

Keywords: free flap monitoring, radial artery forearm free flap, sentinel skin island, perforator, head and neck reconstruction, pharyngo-esophageal reconstruction, microsurgery, plastic surgery, buried flap, ultrasound-guided surgery, oral cancer, reconstructive surgery