Growth in FDA-Approved Artificial Intelligence Devices in Plastic Surgery: A Key Look Into the Future

Plastic surgery has recently seen a significant rise in the adoption of artificial intelligence (AI) and machine learning (ML), aligning with broader trends across the medical field.¹ These technologies aim to enhance surgical efficiency, optimize treatment planning, predict postsurgical aesthetic results, streamline patient management, and improve overall surgical decision-making.^2,3 However, the integration of AI/ML into clinical practice presents ethical and practical challenges, including concerns about transparency, data bias, and security.² The regulation and approval of AI/ML technologies, much like other medical devices, remain complex and continuously evolving. The US FDA has been proactive in addressing these challenges, developing specific frameworks for AI- and/or ML-based devices. As of now, the FDA has approved 950 AI-enabled medical devices, with the majority of approvals occurring in recent years.⁴ Although previous studies have examined the growth of AI-enabled devices across medical specialties, there has been limited focus on their growth in plastic surgery, or on their overall impact in this constantly changing field.⁵

Herein, the first study of its kind, we identified 6 FDA-approved AI/ML-based devices in plastic surgery, approved between 2016 and 2024. These devices, categorized as general/plastic surgery–specific by the FDA, primarily focus on intraoperative support, aiming to reduce complications, improve efficiency, and lower costs for payers and providers. Innovations include tools for estimating blood loss through “smart sponges,” minimally invasive robotic tools, and high-resolution intraoperative medical imaging (Table 1).

For these 6 FDA-approved medical devices, we identified 6 corporations responsible for advancing the respective AI-enabled innovation in plastic and reconstructive surgery. We gathered data from Pitchbook (Seattle, WA), Google Patents (Alphabet, Inc., Mountain View, CA), and clinicaltrials.gov to determine the number of patents, clinical trials, capital raised, and company status for each of the corporations responsible for these innovations (Table 1). These companies show diverse financial approaches: 2 are publicly listed, 2 have been acquired, 1 is a private start-up, and 1 recently filed for bankruptcy. Patent and clinical trial activity varied widely, depending on the parent company's portfolio, size, and longevity. These differences highlight the range of innovation and development stages within this sector.

Our analysis found that AI-enabled medical devices in plastic surgery lag behind other medical specialties. Radiology leads with 723 (76.1%) of all AI device approvals, followed by cardiovascular medicine (98 devices, 10.3%), neurology (34 devices, 3.6%), and other specialties. Plastic surgery accounts for only 0.63% of approvals. The accelerated growth in approvals from 2017 to 2024 has been driven by radiology, cardiovascular medicine, and neurology (Figure 1).

Figure 1.

Growth of FDA-approved AI-enabled devices across various healthcare specialties from 1995 to August 2024. Radiology (gray area) exhibits the highest growth, accounting for 723 approved devices by August 2024, which represents nearly 80% of the total approvals. Cardiovascular devices (blue area) follow with 98 approvals, showing steady growth since the early 2000s. Neurology (red area) accounts for 34 approved devices, reflecting substantial growth, particularly since 2015. General and plastic surgery (orange area) has seen modest growth, with 6 approved devices by 2024. The “All other specialties” category (black area) includes fields such as pathology, anesthesiology, and ophthalmology, contributing 9.4% (89) to the overall total of 950 AI/ML-enabled devices approved by the FDA by August 2024. AI, artificial intelligence; ML, machine learning.

Open in new tabDownload slide

The slower adoption of AI in plastic surgery can be attributed to several key factors. First, the image-based technologies that have driven significant advancements in fields such as cardiology, neurology, and radiology are less directly applicable to plastic surgery. These specialties are more algorithmic and predictable, allowing for the development of software that can effectively enhance clinical decision-making. In contrast, plastic surgery involves greater complexity and variability, making it less suitable for standardization through algorithms. Additionally, the barriers to entry for surgical devices—such as robotic tools and microsurgery applications—are typically higher than those for noninvasive imaging modalities or software-based algorithms.

Moreover, the risks associated with innovation in surgery are uniquely high. Visionary innovators often challenge conventional thinking, but within medicine, and particularly in surgery, these risks are amplified. Unlike in nonmedical fields, physician-innovators face heightened challenges due to the potential for complications that can directly impact patient safety. In surgery, the stakes are even higher, as experimental technologies carry the risk of adverse outcomes, including patient mortality. Surgeon-innovators are, therefore, tasked with carefully balancing the potential long-term benefits of advancing surgical practice against the immediate risks posed by unproven technologies.⁶ This need to prioritize patient safety often results in a more cautious approach to adopting new technologies, even when they hold promise for improving outcomes on a broader scale in the future.

Another factor is that plastic surgeons frequently rely on technologies classified under other medical specialties—such as orthopedics, general healthcare, or radiology—using these applications for diagnostic or predictive purposes. This practice of using products “off-label” reduces the financial incentive for companies to develop devices specifically tailored for plastic surgery. Moreover, the inherently interdisciplinary nature of plastic surgery, which draws from a wide range of techniques and knowledge areas, might also contribute to the lower number of device approvals within the field. This overlap with other specialties dilutes the demand for plastic surgery–specific AI tools, further slowing innovation in the sector.

Plastic surgery stands to benefit significantly from advanced algorithms that enhance surgical planning by offering more objective insights into preoperative decision-making, predictive analytics for postoperative outcomes, and automated systems for postoperative care. These algorithms, capable of modeling preoperative plans, detecting complications preemptively through aggregated patient data, and predicting outcomes for better risk stratification, have the potential to substantially reduce adverse events. Additionally, innovations in workflow optimization, such as automating administrative tasks like patient management and billing, could alleviate the administrative burden on surgeons, allowing them to focus more on patient care.

Unique to the field of plastic surgery, and inherent to its multifaceted nature, plastic surgeons experience considerable advancement through cross-disciplinary innovation structures that incentivize collaboration across specialties. By working closely with imaging specialists in radiology and engaging with other surgical leaders, plastic surgeons can help bridge gaps in healthcare disparities and drive the development and adoption of FDA-approved AI products in clinical practice. This collaborative approach will accelerate innovation and also ensures that AI technologies are better aligned with the nuanced needs of the field, ultimately enhancing patient outcomes and advancing the practice of plastic surgery.

To incentivize and drive innovation in plastic surgery, physician-led research backed by financial support from academic medical centers could be transformative. A venture-capital model for surgical innovation has been proposed, in which health systems invest in solutions created by surgeons who identify clinical challenges through their routine interactions with patients.⁷ This approach could expedite the development of new solutions, bypassing the often slow and highly competitive National Institutes of Health grant process. The result would be quicker advancements in patient care, benefiting all stakeholders involved in healthcare innovation.

However, these funding strategies and innovations are not without ethical considerations. Venture-backed innovations are often driven by the need for rapid growth and continuous iteration, which can introduce financial incentives for venture capital firms and physician-inventors that differ significantly from those associated with traditional funding sources such as National Institutes of Health grants or clinical trials.^7,8 Despite these pressures, innovations emerging from this model must still undergo the same rigorous FDA testing and approval processes as any other medical product. To ensure ethical integrity, it is essential that investment strategies led by academic medical and surgical centers take a “secular” approach, in which legal and ethical guidelines are embedded in the development process, ensuring independence and maintaining rigorous evaluation throughout.

When it comes to developing AI technologies for plastic surgery, it is critical that surgeons make the risks associated with new experimental technologies abundantly clear to patients. The ethical responsibility for deciding whether to use a new AI technology lies primarily with the surgeon, rather than with private companies or hospitals. This decision should be made on a case-by-case basis, considering the specific needs and circumstances of each patient. Ensuring shared responsibility and securing informed consent are vital, just as with any other medical technology. The introduction of AI in surgery should not compromise the trust and strength of the relationship between surgeons and patients; this relationship must be preserved as the foundation of ethical medical practice.

Safeguarding patient privacy also remains paramount, given the vast amounts of protected health information required for both the development and ongoing improvement of these AI systems.^9,10 Ensuring data security and maintaining the confidentiality of sensitive patient information are essential to upholding patient trust as these technologies continue to evolve and integrate into clinical practice. This challenge is compounded by the need to address data bias and maintain transparency in decision-making processes.^9,10 Currently, many healthcare AI models are trained on biased and nonrepresentative datasets, leading to results that cannot be reliably generalized to the broader population, largely due to the historic underrepresentation of minority groups in clinical research.

Plastic and reconstructive surgery, in particular, faces unique challenges in this context due to the limited availability of comprehensive data needed to support AI algorithms, especially compared to more data-rich and objective fields, such as radiology. To address this, innovations in AI for plastic surgery must prioritize improved data-collection strategies, enhanced methods for gathering diverse patient information, and expanding the scope of participants in clinical studies. Only through these measures can we effectively reduce disparities in care and develop AI products that serve a broad and diverse patient population. As ethical frameworks continue to evolve, collaboration with policymakers and patient advocacy groups will be essential to ensure that these technologies remain patient-centered and equitable.

This descriptive study highlights the increasing presence of FDA-approved AI medical devices in healthcare, with plastic surgery being no exception. Other specialties have seen more substantial growth, and plastic surgery is poised to experience similar advances. We envision a future where AI-enabled medical devices will transform patient care, reduce morbidity and mortality following surgical procedures, and help address disparities in outcomes. Future research should focus on evaluating the long-term effectiveness of FDA-approved AI-enabled devices in plastic surgery, comparing them with non-AI FDA-approved medical devices, and investigating potential complications associated with these technologies.

For those seeking to innovate within plastic surgery—a field that is inherently less algorithmic than others—the focus should be on developing technologies that uniquely support the plastic surgeon's practice. Emphasis should be placed on AI and ML solutions that enhance perioperative efficiency and assist in perioperative decision-making. The authors believe that innovations driven by surgeons who directly encounter the challenges faced by their patients and colleagues on a daily basis will be the most impactful and successful in advancing the field.

Disclosures

Mr Ravi Dhawan reports owning stock/equity in Stryker Corporation (Kalamazoo, MI). Dr Albert Losken reports serving on the advisory board of Bimini HealthTech (Plano, TX) and Novus Scientific AB (Uppsala, Sweden). All other authors declared no potential conflicts of interest with respect to the research, authorship, and publication of this article.

Funding

The authors received no financial support for the research, authorship, and publication of this article.

REFERENCES

Author notes