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370 results on '"Forte, Antonio Jorge"'

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

Author

Jakub, James W., Boughey, Judy C., Hieken, Tina J., Piltin, Mara, Forte, Antonio Jorge, Vijayasekaran, Aparna, Mazur, Monica, Sturz, Jenna, Corbin, Kim, Vallow, Laura, Johnson, Jeffrey E., Mrdutt, Mary, Fahradyan, Vahe, Li, Zhuo, Blumenfeld, Sophia, Degnim, Amy, Yost, Kathleen J., Cheville, Andrea, and McLaughlin, Sarah A.

Published
2024
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3. ASO Visual Abstract: Lymphedema Rates Following Axillary Lymph Node Dissection With and Without Immediate Lymphatic Reconstruction: A Prospective Trial

Author

Jakub, James W., Boughey, Judy C., Hieken, Tina J., Piltin, Mara, Forte, Antonio Jorge, Vijaysakaran, Aparna, Mazur, Monica, Sturz, Jenna, Corbin, Kim, Vallow, Laura, Johnson, Jeffrey E., Mrdutt, Mary, Fahradyan, Vahe, Li, Zhuo, Blumenfeld, Sophia, Degnim, Amy, Yost, Kathleen J., Cheville, Andrea, and McLaughlin, Sarah A.

Published
2024
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11. The Impact of Digital Health Solutions on Bridging the Health Care Gap in Rural Areas: A Scoping Review.

Author

Maita, Karla C., Maniaci, Michael J., Haider, Clifton R., Avila, Francisco R., Torres-Guzman, Ricardo A., Borna, Sahar, Lunde, Julianne J., Coffey, Jordan D., Demaerschalk, Bart M., and Forte, Antonio Jorge

Subjects
DIGITAL health, RURAL health services, HEALTH services accessibility, DISEASE management, MOBILE health
Abstract

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. The review adopts a comprehensive search strategy to identify relevant studies from electronic databases, and the selected studies are analyzed descriptively. The findings highlight the advantages and barriers of digital health interventions in rural populations. The advantages include increased access to health care practitioners through teleconsultations, improved health care outcomes through remote monitoring, better disease management through mobile health applications and wearable devices, and enhanced access to specialized care and preventive programs. However, limited internet connectivity and a lack of familiarity with digital tools are barriers that must be addressed to ensure equitable access to digital health interventions in rural areas. Overall, digital tools improve health outcomes for individuals with limited health care access in rural areas. [ABSTRACT FROM AUTHOR]

Published
2024
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12. Identification of Pain through Actigraphy-Recorded Patient Movement: A Comprehensive Review.

Author

Torres-Guzman, Ricardo A., Ho, Olivia A., Borna, Sahar, Gomez-Cabello, Cesar A., Haider, Syed Ali, and Forte, Antonio Jorge

Subjects
DIGITAL health, PATIENT monitoring, PAIN measurement, ACTIGRAPHY, PRIMARY care
Abstract

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. However, actigraphy's reliance on physical activity as a proxy for pain, and its inability to directly measure pain intensity, limit its applicability to certain pain types, such as neuropathic pain. Further research is needed to overcome these limitations and to improve the effectiveness of actigraphy in diverse clinical settings. [ABSTRACT FROM AUTHOR]

Published
2024
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17. Evaluating Large Language Model (LLM) Performance on Established Breast Classification Systems.

Author

Haider, Syed Ali, Pressman, Sophia M., Borna, Sahar, Gomez-Cabello, Cesar A., Sehgal, Ajai, Leibovich, Bradley C., and Forte, Antonio Jorge

Subjects
MACHINE learning, LANGUAGE models, CHATGPT, ARTIFICIAL intelligence, PLASTIC surgeons, GYNECOMASTIA
Abstract

Medical researchers are increasingly utilizing advanced LLMs like ChatGPT-4 and Gemini to enhance diagnostic processes in the medical field. This research focuses on their ability to comprehend and apply complex medical classification systems for breast conditions, which can significantly aid plastic surgeons in making informed decisions for diagnosis and treatment, ultimately leading to improved patient outcomes. Fifty clinical scenarios were created to evaluate the classification accuracy of each LLM across five established breast-related classification systems. Scores from 0 to 2 were assigned to LLM responses to denote incorrect, partially correct, or completely correct classifications. Descriptive statistics were employed to compare the performances of ChatGPT-4 and Gemini. Gemini exhibited superior overall performance, achieving 98% accuracy compared to ChatGPT-4's 71%. While both models performed well in the Baker classification for capsular contracture and UTSW classification for gynecomastia, Gemini consistently outperformed ChatGPT-4 in other systems, such as the Fischer Grade Classification for gender-affirming mastectomy, Kajava Classification for ectopic breast tissue, and Regnault Classification for breast ptosis. With further development, integrating LLMs into plastic surgery practice will likely enhance diagnostic support and decision making. [ABSTRACT FROM AUTHOR]

Published
2024
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20. Comparative Analysis of Artificial Intelligence Virtual Assistant and Large Language Models in Post-Operative Care.

Author

Borna, Sahar, Gomez-Cabello, Cesar A., Pressman, Sophia M., Haider, Syed Ali, Sehgal, Ajai, Leibovich, Bradley C., Cole, Dave, and Forte, Antonio Jorge

Subjects
LANGUAGE models, ARTIFICIAL intelligence, NATURAL language processing, POSTOPERATIVE care, GEMINI (Chatbot)
Abstract

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. We conducted a study comparing AIVA (using Google Dialogflow) with ChatGPT-4 and Google BARD, assessing the accuracy, knowledge gap, and response appropriateness. AIVA demonstrated superior performance, with significantly higher accuracy (mean: 0.9) and lower knowledge gap (mean: 0.1) compared to BARD and ChatGPT-4. Additionally, AIVA's responses received higher Likert scores for appropriateness. Our findings suggest that specialized AI tools like AIVA are more effective in delivering precise and contextually relevant information for postoperative care compared to general-purpose LLMs. While ChatGPT-4 shows promise, its performance varies, particularly in verbal interactions. This underscores the importance of tailored AI solutions in healthcare, where accuracy and clarity are paramount. Our study highlights the necessity for further research and the development of customized AI solutions to address specific medical contexts and improve patient outcomes. [ABSTRACT FROM AUTHOR]

Published
2024
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21. Artificial Intelligence Algorithms for Expert Identification in Medical Domains: A Scoping Review.

Author

Borna, Sahar, Barry, Barbara A., Makarova, Svetlana, Parte, Yogesh, Haider, Clifton R., Sehgal, Ajai, Leibovich, Bradley C., and Forte, Antonio Jorge

Subjects
MACHINE learning, ARTIFICIAL intelligence, NATURAL language processing, ALGORITHMS, LANGUAGE models, SUCCESS, KNOWLEDGE management
Abstract

With abundant information and interconnectedness among people, identifying knowledgeable individuals in specific domains has become crucial for organizations. Artificial intelligence (AI) algorithms have been employed to evaluate the knowledge and locate experts in specific areas, alleviating the manual burden of expert profiling and identification. However, there is a limited body of research exploring the application of AI algorithms for expert finding in the medical and biomedical fields. This study aims to conduct a scoping review of existing literature on utilizing AI algorithms for expert identification in medical domains. We systematically searched five platforms using a customized search string, and 21 studies were identified through other sources. The search spanned studies up to 2023, and study eligibility and selection adhered to the PRISMA 2020 statement. A total of 571 studies were assessed from the search. Out of these, we included six studies conducted between 2014 and 2020 that met our review criteria. Four studies used a machine learning algorithm as their model, while two utilized natural language processing. One study combined both approaches. All six studies demonstrated significant success in expert retrieval compared to baseline algorithms, as measured by various scoring metrics. AI enhances expert finding accuracy and effectiveness. However, more work is needed in intelligent medical expert retrieval. [ABSTRACT FROM AUTHOR]

Published
2024
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28. Innovative Burn Treatment Using Tilapia Skin as a Xenograft: A Phase II Randomized Controlled Trial

Author

Lima Júnior, Edmar Maciel, De Moraes Filho, Manoel Odorico, Costa, Bruno Almeida, Rohleder, Andréa Vieira Pontes, Sales Rocha, Marina Becker, Fechine, Francisco Vagnaldo, Forte, Antonio Jorge, Alves, Ana Paula Negreiros Nunes, Silva Júnior, Francisco Raimundo, Martins, Camila Barroso, Mathor, Mônica Beatriz, and Moraes, Maria Elisabete Amaral de

Published
2020
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49. Facial Injuries

Author

Ali, Hirra, primary, Forte, Antonio Jorge V., additional, and Shin, Joseph H., additional

Published
2016
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