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Page 311 Brenac et al. Art Int Surg 2024;4:296-315 https://dx.doi.org/10.20517/ais.2024.49

[55]
preoperative and postoperative CT scans . The authors also utilized experimental design to infer the
contributions of input parameters, including Young’s modulus and viscoelasticity, to soft tissue
[55]
displacement . In other examples, surgeon-scientists have utilized generative AI to produce 3D models of
facial shapes based on 2D medical images [42,56-58] . For instance, a 3D morphable model trained on over 4,000
faces has been applied for the diagnosis, risk stratification, and treatment simulation of jaw surgery
patients . Ultimately, ML algorithms have demonstrated the potential to help improve surgical outcomes
[42]
and reduce medical costs by creating patient-specific predictive models prior to operation .
[56]
3D printing for planning and implantation
In addition to 3D modeling, 3D printing may be utilized in combination with AI to generate increasingly
patient-specific models and implants. On the printing and technological side, ML can be utilized to identify
the optimal printing parameters to generate a desired shape and/or internal architecture . Hierarchical ML
[59]
algorithms, for instance, have been used to identify optimal material formulations, process variables, and
fiber geometries for the production of silicone implants and other constructs [60,61] . With the support of AI
methods, patient-specific models of the breast, vasculature, craniofacial tissue, and more have accordingly
been fabricated by 3D printing for purposes of preoperative planning . Chae et al., for instance, used CT
[54]
and MRI scans to visualize and print breast tissue models for mastectomies . In addition to supporting the
[62]
planning process, “bedside” 3D printing has been explored in some early instances for the development of
patient-specific implants . In one notable example, Lei et al. utilized ML models to design cochlear
[54]
[63]
implants with optimal electro-anatomical properties given the patient’s specific inner ear geometry .
Ultimately, 3D printing methods can produce more geometrically complex and site-specific constructs
compared to traditional fabrication methods, particularly when paired with AI models.

DISCUSSION
Plastic surgery is a growing field, with cosmetic and surgical procedures recently seeing more than a 5%
annual increase, according to the ASPS . As the specialty advances with recent innovations such as
[64]
minimally invasive treatments, organ transplantation, super microsurgery, and the integration of AI, it
continues to encounter challenges related to patient information, pre- and postoperative assessments, and
preoperative planning . The subjective nature of plastic surgery often leads to variability in result
[64]
assessments, and it requires tailored procedures to accommodate individual/ethnic features as well as
excellent comprehension between the surgeon and the patient to obtain satisfaction with results [16,65] .

AI is considered an innovation in plastic surgery, characterized as “something new or a modification to an
existing product, idea, or field” . Applications of AI tools have the potential to significantly address the
[64]
limitations of plastic surgery by improving the efficiency and precision of surgical procedures, diagnostic
analytics, and patient outcomes. ML algorithms can also analyze patient-specific data to create highly
[66]
personalized treatment plans and predict surgical success in a more accurate manner . Virtual planning,
3D modeling, and patient-specific cutting guides allow for increased precision, decreased operative time,
and improved cosmetic outcomes . Additionally, autonomous surgical robots are emerging as a novel AI-
[67]
based healthcare technology. These robots have the potential to be trained using cadavers, similar to
students learning through dissecting cadavers, allowing the robots to experience a full-contact ML
environment . These advancements are particularly beneficial to plastic surgery due to the intricate and
[68]
complex nature of these procedures. While these tools have shown to be promising, the benefits closely
depend on the quality of input data and the ability to address potential inherent biases within AI models.
The use of large datasets and varying patient demographics could affect the accuracy of AI predictions and
introduce certain biases if the training data are not diverse. Ethical considerations, such as ensuring patient
data privacy and maintaining transparency of AI-driven decision making, must also be considered for the