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Issues And Trends In Healthcare Delivery System

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...

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A Postoperative Evaluation Guideline for Computer-Assisted Reconstruction of the Mandible

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Machine Learning, Deep Learning, Artificial Intelligence and Aesthetic Plastic Surgery: A Qualitative Systematic

Raquel Nogueira¹, Marina Eguchi², Julia Kasmirski³

¹Department of Surgery, Montefiore Medical Center, 1825 Eastchester Rd, Bronx, NY, 10461, USA. raquelnogueiramd@gmail.com.

Aesthetic Plastic Surgery

|October 9, 2024

Summary

Machine learning (ML), deep learning (DL), and artificial intelligence (AI) show great potential in aesthetic plastic surgery. These technologies can optimize treatments and predict complications, but careful use is needed to manage patient expectations.

Keywords:

Aesthetic surgery Artificial intelligence Deep learning Machine learning Plastic surgery

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Area of Science:

Medical Informatics
Computer-Aided Surgery
Plastic Surgery

Background:

The integration of advanced computational techniques is rapidly evolving within various medical disciplines.
Aesthetic plastic surgery is increasingly exploring innovative tools to enhance patient outcomes and surgical precision.

Purpose of the Study:

To systematically review the application of machine learning (ML), deep learning (DL), and artificial intelligence (AI) in aesthetic plastic surgery.
To assess the current landscape and potential impact of these technologies on surgical decision-making and complication prediction.

Main Methods:

A qualitative systematic review was conducted following PRISMA guidelines.
Searches were performed on MEDLINE/PubMed, EMBASE, and Cochrane Library using terms related to ML, DL, AI, and plastic surgery.
The ROBINS-I tool was used for risk-of-bias assessment of included non-randomized studies.

Main Results:

Eighteen studies published between 2019 and 2024 met the inclusion criteria.
Applications spanned various procedures including breast augmentation, rhinoplasty, facial rejuvenation, and body contouring.
Image-based AI, ML, and DL algorithms were utilized to improve decision-making and identify factors influencing postoperative complications.

Conclusions:

AI, ML, and DL algorithms hold significant promise for revolutionizing aesthetic plastic surgery.
These technologies can aid in optimizing treatment plans, predicting complications, and clarifying patient concerns.
Careful implementation is crucial to avoid setting unrealistic patient expectations, emphasizing the continued importance of conservative surgical communication.