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cost reduction due to a drastic decrease in the staffing burden required for frequent flap checks, especially
[32]
within the first 24 h post-operation .
Predictive analytics can also be utilized to create personalized postoperative care plans and enhance patient-
[33]
specific, value-based care and recovery . ML models based on preoperative and intraoperative information
can help plastic surgeons quickly stratify risk factors of flap failure in microvascular breast reconstruction
[34]
and guide postoperative treatment planning . For example, ML models have been developed to predict
30-day reoperation and readmission for head and neck free flap patients using patient demographics and
perioperative factors (operation time, hospital length of stay, surgical site infection) . In the instances of
[35]
autologous breast reconstruction, such models could potentially shorten hospital stays and reduce spending
among low-risk patients while informing preventative measures to reduce flap failure among high-risk
patients. For implant-based breast reconstruction, ML models were also able to identify significant
predictors of periprosthetic infection and explantation, as well as nipple-areolar complex necrosis following
immediate breast reconstruction [36,37] . Breast reconstruction has been among the top 5 most performed
reconstructive procedures for the last 2 years, and this volume only continues to grow . As more data
[38]
become available to train these models, their acuity and surgical utility will continue to improve.
Additionally, this growing volume of autologous procedures also signals the need for more efficient and
[39]
cost-effective postoperative management strategies, potentially enabled by AI . As we transition to a future
with shorter hospital stays and more focus on personalized medicine [40,41] , AI could greatly enhance the
precision and effectiveness of postoperative care, leading to better patient outcomes and overall satisfaction.

Other AI applications that are revolutionizing postoperative care include remote monitoring and
telemedicine. Wearable AI devices have been applied in aesthetic postoperative settings to monitor patient
[42]
compliance with positioning after discharge . This could be used to detect clinical derangements and alert
surgeons in real time, enabling prompt response and better outcomes, while allowing patients to recover
from the comfort of their own homes.

Patient satisfaction and outcomes initiatives
Patient satisfaction with breast reconstruction following mastectomy is highly variable and subject to
influence from various factors involved in their cancer treatment process. ML models developed from an
international cohort of patients diagnosed with breast cancer who underwent mastectomy and
reconstruction demonstrated the ability to accurately predict individual patient-reported outcome measures
[43]
(PROMs), as evaluated by BREAST-Q, prior to the initiation of the reconstruction process . By integrating
these AI models into treatment planning for each patient, plastic surgeons may better guide the patients in
making treatment plans that maximize patient satisfaction.

AI tools such as chatbots and virtual assistants can significantly enhance the patient experience with
consultation and education by promptly addressing patients’ queries. For real-time patient question
resolution, a survey from a single institution demonstrated that 96% of patients were satisfied or somewhat
satisfied with their interaction with the patient-facing AI chatbots . Regarding general simplification of
[44]
patient instructions and educational materials, Chat Generative Pretrained Transformer 3.5 (OpenAI, San
Francisco, California, 2022) could effectively simplify these resources down to a broadly acceptable reading
level . This is especially relevant for patients considering implant-based reconstruction, who often face
[45]
lengthy and evolving educational materials about implant safety and risks, as the Food and Drug
Administration (FDA) continues to update its recommendations and warnings surrounding breast
implants . For example, AI-driven tools can help break down lengthy patient materials regarding the safety
[46]
profile and risks associated with these implants into more digestible formats, which will help patients make