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Dundaru-Bandi D, Antel R, Ingelmo P. Advances in pediatric perioperative care using artificial intelligence. Curr Opin Anaesthesiol 2024; 37:251-258. [PMID: 38441085 DOI: 10.1097/aco.0000000000001368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2024]
Abstract
PURPOSE OF THIS REVIEW This article explores how artificial intelligence (AI) can be used to evaluate risks in pediatric perioperative care. It will also describe potential future applications of AI, such as models for airway device selection, controlling anesthetic depth and nociception during surgery, and contributing to the training of pediatric anesthesia providers. RECENT FINDINGS The use of AI in healthcare has increased in recent years, largely due to the accessibility of large datasets, such as those gathered from electronic health records. Although there has been less focus on pediatric anesthesia compared to adult anesthesia, research is on- going, especially for applications focused on risk factor identification for adverse perioperative events. Despite these advances, the lack of formal external validation or feasibility testing results in uncertainty surrounding the clinical applicability of these tools. SUMMARY The goal of using AI in pediatric anesthesia is to assist clinicians in providing safe and efficient care. Given that children are a vulnerable population, it is crucial to ensure that both clinicians and families have confidence in the clinical tools used to inform medical decision- making. While not yet a reality, the eventual incorporation of AI-based tools holds great potential to contribute to the safe and efficient care of our patients.
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Affiliation(s)
| | - Ryan Antel
- Department of Anesthesia, McGill University
| | - Pablo Ingelmo
- Department of Anesthesia, McGill University
- Division of Pediatric Anesthesia
- Edwards Family Interdisciplinary Center for Complex Pain. Montreal Children's Hospital
- Research Institute, McGill University Health Center
- Alan Edwards for Research on Pain. McGill University, Montreal, Quebec, Canada
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Chakroun-Walha O, Karray R, Jerbi M, Affes H, Nasri A, Salem I, Issaoui F, Ben Dhaou M, Rekik N. Catheterized chicken for training on ultrasound-guided vascular access: A simple, cost-effective, and effective model. Afr J Emerg Med 2024; 14:91-95. [PMID: 38660415 PMCID: PMC11039968 DOI: 10.1016/j.afjem.2024.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 03/17/2024] [Accepted: 03/28/2024] [Indexed: 04/26/2024] Open
Abstract
Ultrasound-guided vascular access is a medical procedure that is becoming increasingly common in daily practice and is recommended to avoid iatrogenic complications. One of the procedures with a high-risk rate of complications is the vascular puncture. However, training on this technique can be challenging due to the limited availability of simulation models. We propose a simple, cost-effective, and effective ultrasound-guided vascular access simulation model that utilizes chicken breast and a urine catheter to address this need.
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Affiliation(s)
- Olfa Chakroun-Walha
- Emergency department, Habib Bourguiba university hospital, Sfax Medical School Simulation Center, Faculty of Medicine, Sfax university, Tunisia
| | - Rim Karray
- Emergency Department, Habib Bourguiba University Hospital, Faculty of Medicine, Sfax University, Tunisia
| | - Mouna Jerbi
- Emergency Department, Habib Bourguiba University Hospital, Faculty of Medicine, Sfax University, Tunisia
| | - Houcem Affes
- Emergency Department, Habib Bourguiba University Hospital, Faculty of Medicine, Sfax University, Tunisia
| | - Abdennour Nasri
- Emergency Department, Habib Bourguiba University Hospital, Faculty of Medicine, Sfax University, Tunisia
| | - Imen Salem
- Emergency Department, Habib Bourguiba University Hospital, Faculty of Medicine, Sfax University, Tunisia
| | - Fadhila Issaoui
- Emergency Department, Habib Bourguiba University Hospital, Faculty of Medicine, Sfax University, Tunisia
| | - Mahdi Ben Dhaou
- Sfax Medical School Simulation Center, Faculty of Medicine, Sfax University, Tunisia
| | - Noureddine Rekik
- Emergency Department, Habib Bourguiba University Hospital, Faculty of Medicine, Sfax University, Tunisia
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Hamilton A. Artificial Intelligence and Healthcare Simulation: The Shifting Landscape of Medical Education. Cureus 2024; 16:e59747. [PMID: 38840993 PMCID: PMC11152357 DOI: 10.7759/cureus.59747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/20/2024] [Indexed: 06/07/2024] Open
Abstract
The impact of artificial intelligence (AI) will be felt not only in the arena of patient care and deliverable therapies but will also be uniquely disruptive in medical education and healthcare simulation (HCS), in particular. As HCS is intertwined with computer technology, it offers opportunities for rapid scalability with AI and, therefore, will be the most practical place to test new AI applications. This will ensure the acquisition of AI literacy for graduates from the country's various healthcare professional schools. Artificial intelligence has proven to be a useful adjunct in developing interprofessional education and team and leadership skills assessments. Outcome-driven medical simulation has been extensively used to train students in image-centric disciplines such as radiology, ultrasound, echocardiography, and pathology. Allowing students and trainees in healthcare to first apply diagnostic decision support systems (DDSS) under simulated conditions leads to improved diagnostic accuracy, enhanced communication with patients, safer triage decisions, and improved outcomes from rapid response teams. However, the issue of bias, hallucinations, and the uncertainty of emergent properties may undermine the faith of healthcare professionals as they see AI systems deployed in the clinical setting and participating in diagnostic judgments. Also, the demands of ensuring AI literacy in our healthcare professional curricula will place burdens on simulation assets and faculty to adapt to a rapidly changing technological landscape. Nevertheless, the introduction of AI will place increased emphasis on virtual reality platforms, thereby improving the availability of self-directed learning and making it available 24/7, along with uniquely personalized evaluations and customized coaching. Yet, caution must be exercised concerning AI, especially as society's earlier, delayed, and muted responses to the inherent dangers of social media raise serious questions about whether the American government and its citizenry can anticipate the security and privacy guardrails that need to be in place to protect our healthcare practitioners, medical students, and patients.
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Affiliation(s)
- Allan Hamilton
- Artificial Intelligence Division, Arizona Simulation Technology and Education Center (ASTEC) University of Arizona, Tucson, USA
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Caglayan A, Slusarczyk W, Rabbani RD, Ghose A, Papadopoulos V, Boussios S. Large Language Models in Oncology: Revolution or Cause for Concern? Curr Oncol 2024; 31:1817-1830. [PMID: 38668040 PMCID: PMC11049602 DOI: 10.3390/curroncol31040137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 03/13/2024] [Accepted: 03/29/2024] [Indexed: 04/28/2024] Open
Abstract
The technological capability of artificial intelligence (AI) continues to advance with great strength. Recently, the release of large language models has taken the world by storm with concurrent excitement and concern. As a consequence of their impressive ability and versatility, their provide a potential opportunity for implementation in oncology. Areas of possible application include supporting clinical decision making, education, and contributing to cancer research. Despite the promises that these novel systems can offer, several limitations and barriers challenge their implementation. It is imperative that concerns, such as accountability, data inaccuracy, and data protection, are addressed prior to their integration in oncology. As the progression of artificial intelligence systems continues, new ethical and practical dilemmas will also be approached; thus, the evaluation of these limitations and concerns will be dynamic in nature. This review offers a comprehensive overview of the potential application of large language models in oncology, as well as concerns surrounding their implementation in cancer care.
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Affiliation(s)
- Aydin Caglayan
- Department of Medical Oncology, Medway NHS Foundation Trust, Gillingham ME7 5NY, UK; (A.C.); (R.D.R.); (A.G.)
| | | | - Rukhshana Dina Rabbani
- Department of Medical Oncology, Medway NHS Foundation Trust, Gillingham ME7 5NY, UK; (A.C.); (R.D.R.); (A.G.)
| | - Aruni Ghose
- Department of Medical Oncology, Medway NHS Foundation Trust, Gillingham ME7 5NY, UK; (A.C.); (R.D.R.); (A.G.)
- Department of Medical Oncology, Barts Cancer Centre, St Bartholomew’s Hospital, Barts Heath NHS Trust, London EC1A 7BE, UK
- Department of Medical Oncology, Mount Vernon Cancer Centre, East and North Hertfordshire Trust, London HA6 2RN, UK
- Health Systems and Treatment Optimisation Network, European Cancer Organisation, 1040 Brussels, Belgium
- Oncology Council, Royal Society of Medicine, London W1G 0AE, UK
| | | | - Stergios Boussios
- Department of Medical Oncology, Medway NHS Foundation Trust, Gillingham ME7 5NY, UK; (A.C.); (R.D.R.); (A.G.)
- Kent Medway Medical School, University of Kent, Canterbury CT2 7LX, UK;
- Faculty of Life Sciences & Medicine, School of Cancer & Pharmaceutical Sciences, King’s College London, Strand Campus, London WC2R 2LS, UK
- Faculty of Medicine, Health, and Social Care, Canterbury Christ Church University, Canterbury CT2 7PB, UK
- AELIA Organization, 9th Km Thessaloniki—Thermi, 57001 Thessaloniki, Greece
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Bellini V, Semeraro F, Montomoli J, Cascella M, Bignami E. Between human and AI: assessing the reliability of AI text detection tools. Curr Med Res Opin 2024; 40:353-358. [PMID: 38265047 DOI: 10.1080/03007995.2024.2310086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 01/22/2024] [Indexed: 01/25/2024]
Abstract
OBJECTIVE Large language models (LLMs) such as ChatGPT-4 have raised critical questions regarding their distinguishability from human-generated content. In this research, we evaluated the effectiveness of online detection tools in identifying ChatGPT-4 vs human-written text. METHODS A two texts produced by ChatGPT-4 using differing prompts and one text created by a human author were analytically assessed using the following online detection tools: GPTZero, ZeroGPT, Writer ACD, and Originality. RESULTS The findings revealed a notable variance in the detection capabilities of the employed detection tools. GPTZero and ZeroGPT exhibited inconsistent assessments regarding the AI-origin of the texts. Writer ACD predominantly identified texts as human-written, whereas Originality consistently recognized the AI-generated content in both samples from ChatGPT-4. This highlights Originality's enhanced sensitivity to patterns characteristic of AI-generated text. CONCLUSION The study demonstrates that while automatic detection tools may discern texts generated by ChatGPT-4 significant variability exists in their accuracy. Undoubtedly, there is an urgent need for advanced detection tools to ensure the authenticity and integrity of content, especially in scientific and academic research. However, our findings underscore an urgent need for more refined detection methodologies to prevent the misdetection of human-written content as AI-generated and vice versa.
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Affiliation(s)
- Valentina Bellini
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Federico Semeraro
- Department of Anesthesia, Intensive Care and Prehospital Emergency, Maggiore Hospital Carlo Alberto Pizzardi, Bologna, Italy
| | - Jonathan Montomoli
- Department of Anesthesia and Intensive Care, Infermi Hospital, Romagna Local Health Authority, Rimini, Italy
| | - Marco Cascella
- Anesthesia and Pain Medicine. Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno, Baronissi, Italy
| | - Elena Bignami
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, Italy
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Cascella M, Semeraro F, Montomoli J, Bellini V, Piazza O, Bignami E. The Breakthrough of Large Language Models Release for Medical Applications: 1-Year Timeline and Perspectives. J Med Syst 2024; 48:22. [PMID: 38366043 PMCID: PMC10873461 DOI: 10.1007/s10916-024-02045-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 02/10/2024] [Indexed: 02/18/2024]
Abstract
Within the domain of Natural Language Processing (NLP), Large Language Models (LLMs) represent sophisticated models engineered to comprehend, generate, and manipulate text resembling human language on an extensive scale. They are transformer-based deep learning architectures, obtained through the scaling of model size, pretraining of corpora, and computational resources. The potential healthcare applications of these models primarily involve chatbots and interaction systems for clinical documentation management, and medical literature summarization (Biomedical NLP). The challenge in this field lies in the research for applications in diagnostic and clinical decision support, as well as patient triage. Therefore, LLMs can be used for multiple tasks within patient care, research, and education. Throughout 2023, there has been an escalation in the release of LLMs, some of which are applicable in the healthcare domain. This remarkable output is largely the effect of the customization of pre-trained models for applications like chatbots, virtual assistants, or any system requiring human-like conversational engagement. As healthcare professionals, we recognize the imperative to stay at the forefront of knowledge. However, keeping abreast of the rapid evolution of this technology is practically unattainable, and, above all, understanding its potential applications and limitations remains a subject of ongoing debate. Consequently, this article aims to provide a succinct overview of the recently released LLMs, emphasizing their potential use in the field of medicine. Perspectives for a more extensive range of safe and effective applications are also discussed. The upcoming evolutionary leap involves the transition from an AI-powered model primarily designed for answering medical questions to a more versatile and practical tool for healthcare providers such as generalist biomedical AI systems for multimodal-based calibrated decision-making processes. On the other hand, the development of more accurate virtual clinical partners could enhance patient engagement, offering personalized support, and improving chronic disease management.
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Affiliation(s)
- Marco Cascella
- Anesthesia and Pain Medicine, Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno, Via S. Allende, Baronissi, 84081, Italy
| | - Federico Semeraro
- Department of Anesthesia, Intensive Care and Prehospital Emergency, Maggiore Hospital Carlo Alberto Pizzardi, Bologna, Italy
| | - Jonathan Montomoli
- Department of Anesthesia and Intensive Care, Infermi Hospital, AUSL Romagna, Viale Settembrini 2, Rimini, 47923, Italy
| | - Valentina Bellini
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, Parma, 43126, Italy.
| | - Ornella Piazza
- Anesthesia and Pain Medicine, Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno, Via S. Allende, Baronissi, 84081, Italy
| | - Elena Bignami
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, Parma, 43126, Italy
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Kundra P, Senthilnathan M. Amalgamation of artificial intelligence and simulation in anaesthesia training: Much-needed future endeavour. Indian J Anaesth 2024; 68:8-10. [PMID: 38406343 PMCID: PMC10893798 DOI: 10.4103/ija.ija_1264_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Accepted: 12/27/2023] [Indexed: 02/27/2024] Open
Affiliation(s)
- Pankaj Kundra
- Department of Anaesthesiology and Critical Care, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
| | - Muthapillai Senthilnathan
- Department of Anaesthesiology and Critical Care, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
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Komasawa N, Yokohira M. Learner-Centered Experience-Based Medical Education in an AI-Driven Society: A Literature Review. Cureus 2023; 15:e46883. [PMID: 37954813 PMCID: PMC10636515 DOI: 10.7759/cureus.46883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/12/2023] [Indexed: 11/14/2023] Open
Abstract
This review proposes and explores the significance of "experience-based medical education" (EXPBME) in the context of an artificial intelligence (AI)-driven society. The rapid advancements in AI, particularly driven by deep learning, have revolutionized medical practices by replicating human cognitive functions, such as image analysis and data interpretation, significantly enhancing efficiency and precision across medical settings. The integration of AI into healthcare presents substantial potential, ranging from precise diagnostics to streamlined data management. However, non-technical skills, such as situational awareness on recognizing AI's fallibility or inherent risks, are critical for future healthcare professionals. EXPBME in a clinical or simulation environment plays a vital role, allowing medical practitioners to navigate AI failures through sufficient reflections. As AI continues to evolve, aligning educational frameworks to nurture these fundamental non-technical skills is paramount to adequately prepare healthcare professionals. Learner-centered EXPBME, combined with the AI literacy acquirement, stands as a key pillar in shaping the future of medical education.
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Affiliation(s)
- Nobuyasu Komasawa
- Community Medicine Education Promotion Office, Faculty of Medicine, Kagawa University, Takamatsu, JPN
| | - Masanao Yokohira
- Department of Medical Education, Kagawa University, Takamatsu, JPN
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