The Algorithmic Divide: A Systematic Review on AI-Driven Racial Disparities in Healthcare.

Introduction: As artificial intelligence (AI) continues to permeate various sectors, concerns about disparities arising from its deployment have surfaced. AI's effectiveness correlates not only with the algorithm's quality but also with its training data's integrity. This systematic review investigates the racial disparities perpetuated by AI systems across diverse medical domains and the implications of deploying them, particularly in healthcare.

Sensor technology and machine learning to guide clinical decision making in plastic surgery.

Subjective clinical evaluations are deeply rooted in medical practice. Recent advances in sensor technology facilitate the acquisition of extensive amounts of objective physiological data that can serve as a surrogate for subjective assessments. Along with sensor technology, a branch of artificial intelligence, known as machine learning, has provided decisive advances in several areas of medicine due to its pattern recognition and outcome prediction abilities.

Identification of Pain through Actigraphy-Recorded Patient Movement: A Comprehensive Review.

Chronic pain affects over 50 million people in the United States, particularly older adults, making effective assessment and treatment essential in primary care. Actigraphy, which monitors and records limb movement to estimate wakefulness and sleep, has emerged as a valuable tool for assessing pain by providing insights into activity patterns. This review highlights the non-invasive, cost-effective nature of actigraphy in pain monitoring, along with its ability to offer continuous, detailed data on patient movement.

The Impact of Digital Health Solutions on Bridging the Health Care Gap in Rural Areas: A Scoping Review.

Digital health tools can improve health care access and outcomes for individuals with limited access to health care, particularly those residing in rural areas. This scoping review examines the existing literature on using digital tools in patients with limited access to health care in rural areas. It assesses their effectiveness in improving health outcomes.

Innervated breast reconstruction: a narrative review of neurotization techniques and outcomes.

Background And Objective: While significant sensation recovery improvements in neurotized breasts following reconstruction have been reported, sensation testing methods and surgical techniques have been widely variable. This narrative review aims to summarize available literature on current neurotization practices and sensory recovery outcomes in patients undergoing innervated breast reconstruction.

Methods: A comprehensive literature search of PubMed Medline, Web of Science, and Embase was conducted to identify all studies reporting outcomes of neurotization in breast reconstruction surgeries.

Identification of Potential Factors Associated with Cellulitis Following Lymphovenous Bypass Surgery in Breast Cancer Survivors.

Background:  Breast cancer is one of the most common types of cancer, with around 2.3 million cases diagnosed in 2020. One in five cancer patients develops chronic lymphedema caused by multifactorial triggers and treatment-related factors.

Lymphedema Rates Following Axillary Lymph Node Dissection With and Without Immediate Lymphatic Reconstruction: A Prospective Trial.

Background: Immediate lymphatic reconstruction (ILR) has been proposed to decrease lymphedema rates. The primary aim of our study was to determine whether ILR decreased the incidence of lymphedema in patients undergoing axillary lymph node dissection (ALND).

Methods: We conducted a two-site pragmatic study of ALND with or without ILR, employing surgeon-level cohort assignment, based on breast surgeons' preferred standard practice.

Comparative Analysis of Large Language Models in Emergency Plastic Surgery Decision-Making: The Role of Physical Exam Data.

In the U.S., diagnostic errors are common across various healthcare settings due to factors like complex procedures and multiple healthcare providers, often exacerbated by inadequate initial evaluations.

Large Language Models for Intraoperative Decision Support in Plastic Surgery: A Comparison between ChatGPT-4 and Gemini.

Large language models (LLMs) are emerging as valuable tools in plastic surgery, potentially reducing surgeons' cognitive loads and improving patients' outcomes. This study aimed to assess and compare the current state of the two most common and readily available LLMs, Open AI's ChatGPT-4 and Google's Gemini Pro (1.0 Pro), in providing intraoperative decision support in plastic and reconstructive surgery procedures.

Clinical and Surgical Applications of Large Language Models: A Systematic Review.

Large language models (LLMs) represent a recent advancement in artificial intelligence with medical applications across various healthcare domains. The objective of this review is to highlight how LLMs can be utilized by clinicians and surgeons in their everyday practice. A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

Artificial Intelligence in Postoperative Care: Assessing Large Language Models for Patient Recommendations in Plastic Surgery.

Since their release, the medical community has been actively exploring large language models' (LLMs) capabilities, which show promise in providing accurate medical knowledge. One potential application is as a patient resource. This study analyzes and compares the ability of the currently available LLMs, ChatGPT-3.

Cell-Based Therapies Induce Tolerance of Vascularized Composite Allotransplants: A Systematic Review.

Introduction: Vascularized composite allotransplantation (VCA) is the transplantation of multiple tissue types as a solution for devastating injuries. Despite the highly encouraging functional outcomes of VCA, the consequences of long-term immunosuppression remain the main obstacle in its application. In this review, we provide researchers and surgeons with a summary of the latest advances in the field of cell-based therapies for VCA tolerance.

AI in Hand Surgery: Assessing Large Language Models in the Classification and Management of Hand Injuries.

: OpenAI's ChatGPT (San Francisco, CA, USA) and Google's Gemini (Mountain View, CA, USA) are two large language models that show promise in improving and expediting medical decision making in hand surgery. Evaluating the applications of these models within the field of hand surgery is warranted. This study aims to evaluate ChatGPT-4 and Gemini in classifying hand injuries and recommending treatment.

Artificial Intelligence Support for Informal Patient Caregivers: A Systematic Review.

This study aims to explore how artificial intelligence can help ease the burden on caregivers, filling a gap in current research and healthcare practices due to the growing challenge of an aging population and increased reliance on informal caregivers. We conducted a search with Google Scholar, PubMed, Scopus, IEEE Xplore, and Web of Science, focusing on AI and caregiving. Our inclusion criteria were studies where AI supports informal caregivers, excluding those solely for data collection.

Comparative Analysis of Artificial Intelligence Virtual Assistant and Large Language Models in Post-Operative Care.

In postoperative care, patient education and follow-up are pivotal for enhancing the quality of care and satisfaction. Artificial intelligence virtual assistants (AIVA) and large language models (LLMs) like Google BARD and ChatGPT-4 offer avenues for addressing patient queries using natural language processing (NLP) techniques. However, the accuracy and appropriateness of the information vary across these platforms, necessitating a comparative study to evaluate their efficacy in this domain.

A narrative review of telemedicine and its adoption across specialties.

Background And Objective: Telemedicine and video consultation are crucial advancements in healthcare, allowing remote delivery of care. Telemedicine, encompassing various technologies like wearable devices, mobile health, and telemedicine, plays a significant role in managing illnesses and promoting wellness. The corona virus disease 2019 (COVID-19) pandemic accelerated the adoption of telemedicine, ensuring convenient access to medical services while maintaining physical distance.

AI and Ethics: A Systematic Review of the Ethical Considerations of Large Language Model Use in Surgery Research.

Introduction: As large language models receive greater attention in medical research, the investigation of ethical considerations is warranted. This review aims to explore surgery literature to identify ethical concerns surrounding these artificial intelligence models and evaluate how autonomy, beneficence, nonmaleficence, and justice are represented within these ethical discussions to provide insights in order to guide further research and practice.

Methods: A systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

The usefulness of artificial intelligence in breast reconstruction: a systematic review.

Background: Artificial Intelligence (AI) offers an approach to predictive modeling. The model learns to determine specific patterns of undesirable outcomes in a dataset. Therefore, a decision-making algorithm can be built based on these patterns to prevent negative results.

Artificial-Intelligence-Based Clinical Decision Support Systems in Primary Care: A Scoping Review of Current Clinical Implementations.

Primary Care Physicians (PCPs) are the first point of contact in healthcare. Because PCPs face the challenge of managing diverse patient populations while maintaining up-to-date medical knowledge and updated health records, this study explores the current outcomes and effectiveness of implementing Artificial Intelligence-based Clinical Decision Support Systems (AI-CDSSs) in Primary Healthcare (PHC). Following the PRISMA-ScR guidelines, we systematically searched five databases, PubMed, Scopus, CINAHL, IEEE, and Google Scholar, and manually searched related articles.

In Vitro Enhanced Osteogenic Potential of Human Mesenchymal Stem Cells Seeded in a Poly (Lactic--Glycolic) Acid Scaffold: A Systematic Review.

Study Design: Human bone marrow stem cells (hBMSCs) and human adipose-derived stem cells (hADSCs) have demonstrated the capability to regenerate bone once they have differentiated into osteoblasts.

Objective: This systematic review aimed to evaluate the in vitro osteogenic differentiation potential of these cells when seeded in a poly (lactic--glycolic) acid (PLGA) scaffold.

Methods: A literature search of 4 databases following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was conducted in January 2021 for studies evaluating the osteogenic differentiation potential of hBMSCs and hADSCs seeded in a PLGA scaffold.

Development and evaluation of best practice advisory alert for patient eligibility in a hospital-at-home program: A multicenter retrospective study.

Background: Hospital-at-home (HaH) is a growing model of care that has been shown to improve patient outcomes, satisfaction, and cost-effectiveness. However, selecting appropriate patients for HaH is challenging, often requiring burdensome manual screening by clinicians. To facilitate HaH enrollment, electronic health record (EHR) tools such as best practice advisories (BPAs) can be used to alert providers of potential HaH candidates.