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Page 377 Novotny et al. Art Int Surg 2024;4:376-86 https://dx.doi.org/10.20517/ais.2024.52

[1]
enhance diagnostic accuracy, therapeutic outcomes, and clinical efficiency . From machine learning (ML)
algorithms that analyze large datasets to neural networks capable of image recognition, AI has begun to
[2,3]
revolutionize various specialties, including radiology, oncology, and pathology . In recent years, plastic
[4]
and reconstructive surgery has emerged as a field where AI applications demonstrate significant promise .

In plastic surgery, where precision and individualized patient care are paramount, AI technologies facilitate
more accurate preoperative planning, optimizing surgical techniques, and improving postoperative
outcomes. For instance, AI-driven 3D imaging systems allow for more detailed preoperative simulations,
while ML algorithms aid in predicting patient-specific surgical outcomes based on vast clinical datasets.
Moreover, AI is enhancing patient safety through real-time monitoring and predictive analytics that foresee
[5]
complications before they arise .

Reconstructive surgery, often dealing with complex defects and requiring meticulous planning, can also
benefit from AI. From automating the design of tissue flaps to integrating robotic systems for microsurgical
precision, AI contributes to improved reconstructive outcomes, shorter operative times, and reduced
recovery periods. The integration of AI in these specialized procedures is driving a paradigm shift toward
[6]
more personalized, data-driven surgical care .

This paper explores the recent advancements and applications of AI in plastic and reconstructive surgery,
emphasizing the novel techniques, obstacles, and future potential. By examining how AI is reshaping these
fields, we aim to highlight the role of technology in driving the next era of surgical innovation. This paper
gives an overview of AI in plastic surgery, ML, and virtual reality (VR) and its pros and cons in applications.

AI IN PLASTIC SURGERY
To best illustrate its use in plastic surgery, we divided it into three categories: preoperative, intraoperative,
and postoperative. In the preoperative setting, population screening, early and accurate diagnosis and
statistical risk assessment can be used to create a precise surgical plan and individualized overall assessment
[7]
of the patient . To incorporate AI into plastic surgery, it is essential to first grasp how these tools can be
utilized in the medical field . Predicting surgical results, particularly in complex reconstructive procedures,
[8]
is challenging and can lead to discrepancies between expected and actual outcomes for the patient and the
plastic surgeon . AI can be used to analyze medical images, like magnetic resonance imaging (MRI) or
[9]
computed tomography (CT) scans, to ensure significantly higher and faster accuracy of diagnostic
confirmation. This AI-integrated technique is more efficient than conventional techniques and shows
detailed and precise visual representations of the anatomy. The benefit to the plastic surgeon is a thorough
understanding of the individual’s facial or body structure to assist in the preoperative planning. AI-driven
3D simulation tools enable surgeons and patients to preview possible surgery results, helping to align
expectations and surgical strategies. This supports communication with the patient for the expected
outcomes in the case of flap surgery, aesthetic face surgery, or breast surgery .
[8]

In plastic surgery, the aesthetic outcome is very important, and by analyzing the patient’s anatomy, the
healthy breast in breast cancer, cultural differences, and ideal values with the help of AI, an ideal image can
be generated as a template. Having a template or plan for the planned procedure improves patient safety.
Traditional manual methods are often limited by human dexterity, fatigue, and the variability of complex
cases. This is particularly true for tasks like tissue flap design and vascular anastomoses, which are intricate
and error-prone .
[9]