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Qin S, Chislett B, Ischia J, Ranasinghe W, de Silva D, Coles‐Black J, Woon D, Bolton D. ChatGPT and generative AI in urology and surgery-A narrative review. BJUI COMPASS 2024; 5:813-821. [PMID: 39323919 PMCID: PMC11420103 DOI: 10.1002/bco2.390] [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] [Received: 12/21/2023] [Revised: 04/27/2024] [Accepted: 05/12/2024] [Indexed: 09/27/2024] Open
Abstract
Introduction ChatGPT (generative pre-trained transformer [GPT]), developed by OpenAI, is a type of generative artificial intelligence (AI) that has been widely utilised since its public release. It orchestrates an advanced conversational intelligence, producing sophisticated responses to questions. ChatGPT has been successfully demonstrated across several applications in healthcare, including patient management, academic research and clinical trials. We aim to evaluate the different ways ChatGPT has been utilised in urology and more broadly in surgery. Methods We conducted a literature search of the PubMed and Embase electronic databases for the purpose of writing a narrative review and identified relevant articles on ChatGPT in surgery from the years 2000 to 2023. A PRISMA flow chart was created to highlight the article selection process. The search terms 'ChatGPT' and 'surgery' were intentionally kept broad given the nascency of the field. Studies unrelated to these terms were excluded. Duplicates were removed. Results Multiple papers have been published about novel uses of ChatGPT in surgery, ranging from assisting in administrative tasks including answering frequently asked questions, surgical consent, writing operation reports, discharge summaries, grants, journal article drafts, reviewing journal articles and medical education. AI and machine learning has also been extensively researched in surgery with respect to patient diagnosis and predicting outcomes. There are also several limitations with the software including artificial hallucination, bias, out-of-date information and patient confidentiality. Conclusion The potential of ChatGPT and related generative AI models are vast, heralding the beginning of a new era where AI may eventually become integrated seamlessly into surgical practice. Concerns with this new technology must not be disregarded in the urge to hasten progression, and potential risks impacting patients' interests must be considered. Appropriate regulation and governance of this technology will be key to optimising the benefits and addressing the intricate challenges of healthcare delivery and equity.
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Affiliation(s)
- Shane Qin
- Department of UrologyAustin HealthHeidelbergVictoriaAustralia
| | - Bodie Chislett
- Department of UrologyAustin HealthHeidelbergVictoriaAustralia
| | - Joseph Ischia
- Department of UrologyAustin HealthHeidelbergVictoriaAustralia
- Department of SurgeryUniversity of Melbourne, Austin HealthMelbourneVictoriaAustralia
| | - Weranja Ranasinghe
- Department of Anatomy and Developmental BiologyMonash UniversityMelbourneVictoriaAustralia
- Department of UrologyMonash HealthMelbourneVictoriaAustralia
| | - Daswin de Silva
- Research Centre for Data Analytics and CognitionLa Trobe UniversityMelbourneVictoriaAustralia
| | | | - Dixon Woon
- Department of UrologyAustin HealthHeidelbergVictoriaAustralia
- Department of SurgeryUniversity of Melbourne, Austin HealthMelbourneVictoriaAustralia
| | - Damien Bolton
- Department of UrologyAustin HealthHeidelbergVictoriaAustralia
- Department of SurgeryUniversity of Melbourne, Austin HealthMelbourneVictoriaAustralia
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Kim KA, Kim H, Ha EJ, Yoon BC, Kim DJ. Artificial Intelligence-Enhanced Neurocritical Care for Traumatic Brain Injury : Past, Present and Future. J Korean Neurosurg Soc 2024; 67:493-509. [PMID: 38186369 PMCID: PMC11375068 DOI: 10.3340/jkns.2023.0195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 01/04/2024] [Indexed: 01/09/2024] Open
Abstract
In neurointensive care units (NICUs), particularly in cases involving traumatic brain injury (TBI), swift and accurate decision-making is critical because of rapidly changing patient conditions and the risk of secondary brain injury. The use of artificial intelligence (AI) in NICU can enhance clinical decision support and provide valuable assistance in these complex scenarios. This article aims to provide a comprehensive review of the current status and future prospects of AI utilization in the NICU, along with the challenges that must be overcome to realize this. Presently, the primary application of AI in NICU is outcome prediction through the analysis of preadmission and high-resolution data during admission. Recent applications include augmented neuromonitoring via signal quality control and real-time event prediction. In addition, AI can integrate data gathered from various measures and support minimally invasive neuromonitoring to increase patient safety. However, despite the recent surge in AI adoption within the NICU, the majority of AI applications have been limited to simple classification tasks, thus leaving the true potential of AI largely untapped. Emerging AI technologies, such as generalist medical AI and digital twins, harbor immense potential for enhancing advanced neurocritical care through broader AI applications. If challenges such as acquiring high-quality data and ethical issues are overcome, these new AI technologies can be clinically utilized in the actual NICU environment. Emphasizing the need for continuous research and development to maximize the potential of AI in the NICU, we anticipate that this will further enhance the efficiency and accuracy of TBI treatment within the NICU.
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Affiliation(s)
- Kyung Ah Kim
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea
| | - Hakseung Kim
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea
| | - Eun Jin Ha
- Department of Critical Care Medicine, Seoul National University Hospital, Seoul, Korea
| | - Byung C Yoon
- Department of Radiology, Stanford University School of Medicine, VA Palo Alto Heath Care System, Palo Alto, CA, USA
| | - Dong-Joo Kim
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea
- Department of Neurology, Korea University College of Medicine, Seoul, Korea
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Villa M, Sancho J, Rosa G, Chavarrias M, Juarez E, Sanz C. HyperMRI: hyperspectral and magnetic resonance fusion methodology for neurosurgery applications. Int J Comput Assist Radiol Surg 2024; 19:1367-1374. [PMID: 38761318 PMCID: PMC11230967 DOI: 10.1007/s11548-024-03102-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 03/04/2024] [Indexed: 05/20/2024]
Abstract
PURPOSE Magnetic resonance imaging (MRI) is a common technique in image-guided neurosurgery (IGN). Recent research explores the integration of methods like ultrasound and tomography, among others, with hyperspectral (HS) imaging gaining attention due to its non-invasive real-time tissue classification capabilities. The main challenge is the registration process, often requiring manual intervention. This work introduces an automatic, markerless method for aligning HS images with MRI. METHODS This work presents a multimodal system that combines RGB-Depth (RGBD) and HS cameras. The RGBD camera captures the patient's facial geometry, which is used for registration with the preoperative MR through ICP. Once MR-depth registration is complete, the integration of HS data is achieved using a calibrated homography transformation. The incorporation of external tracking with a novel calibration method allows camera mobility from the registration position to the craniotomy area. This methodology streamlines the fusion of RGBD, HS and MR images within the craniotomy area. RESULTS Using the described system and an anthropomorphic phantom head, the system has been characterised by registering the patient's face in 25 positions and 5 positions resulted in a fiducial registration error of 1.88 ± 0.19 mm and a target registration error of 4.07 ± 1.28 mm, respectively. CONCLUSIONS This work proposes a new methodology to automatically register MR and HS information with a sufficient accuracy. It can support the neurosurgeons to guide the diagnosis using multimodal data over an augmented reality representation. However, in its preliminary prototype stage, this system exhibits significant promise, driven by its cost-effectiveness and user-friendly design.
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Affiliation(s)
- Manuel Villa
- CITSEM, Universidad Politécnica de Madrid, 28031, Madrid, Spain
| | - Jaime Sancho
- CITSEM, Universidad Politécnica de Madrid, 28031, Madrid, Spain
| | - Gonzalo Rosa
- CITSEM, Universidad Politécnica de Madrid, 28031, Madrid, Spain
| | | | - Eduardo Juarez
- CITSEM, Universidad Politécnica de Madrid, 28031, Madrid, Spain.
| | - Cesar Sanz
- CITSEM, Universidad Politécnica de Madrid, 28031, Madrid, Spain
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Seghier ML. 7 T and beyond: toward a synergy between fMRI-based presurgical mapping at ultrahigh magnetic fields, AI, and robotic neurosurgery. Eur Radiol Exp 2024; 8:73. [PMID: 38945979 PMCID: PMC11214939 DOI: 10.1186/s41747-024-00472-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Accepted: 04/22/2024] [Indexed: 07/02/2024] Open
Abstract
Presurgical evaluation with functional magnetic resonance imaging (fMRI) can reduce postsurgical morbidity. Here, we discuss presurgical fMRI mapping at ultra-high magnetic fields (UHF), i.e., ≥ 7 T, in the light of the current growing interest in artificial intelligence (AI) and robot-assisted neurosurgery. The potential of submillimetre fMRI mapping can help better appreciate uncertainty on resection margins, though geometric distortions at UHF might lessen the accuracy of fMRI maps. A useful trade-off for UHF fMRI is to collect data with 1-mm isotropic resolution to ensure high sensitivity and subsequently a low risk of false negatives. Scanning at UHF might yield a revival interest in slow event-related fMRI, thereby offering a richer depiction of the dynamics of fMRI responses. The potential applications of AI concern denoising and artefact removal, generation of super-resolution fMRI maps, and accurate fusion or coregistration between anatomical and fMRI maps. The latter can benefit from the use of T1-weighted echo-planar imaging for better visualization of brain activations. Such AI-augmented fMRI maps would provide high-quality input data to robotic surgery systems, thereby improving the accuracy and reliability of robot-assisted neurosurgery. Ultimately, the advancement in fMRI at UHF would promote clinically useful synergies between fMRI, AI, and robotic neurosurgery.Relevance statement This review highlights the potential synergies between fMRI at UHF, AI, and robotic neurosurgery in improving the accuracy and reliability of fMRI-based presurgical mapping.Key points• Presurgical fMRI mapping at UHF improves spatial resolution and sensitivity.• Slow event-related designs offer a richer depiction of fMRI responses dynamics.• AI can support denoising, artefact removal, and generation of super-resolution fMRI maps.• AI-augmented fMRI maps can provide high-quality input data to robotic surgery systems.
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Affiliation(s)
- Mohamed L Seghier
- Department of Biomedical Engineering and Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, UAE.
- Healtcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, UAE.
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Morris MX, Fiocco D, Caneva T, Yiapanis P, Orgill DP. Current and future applications of artificial intelligence in surgery: implications for clinical practice and research. Front Surg 2024; 11:1393898. [PMID: 38783862 PMCID: PMC11111929 DOI: 10.3389/fsurg.2024.1393898] [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] [Received: 02/29/2024] [Accepted: 04/29/2024] [Indexed: 05/25/2024] Open
Abstract
Surgeons are skilled at making complex decisions over invasive procedures that can save lives and alleviate pain and avoid complications in patients. The knowledge to make these decisions is accumulated over years of schooling and practice. Their experience is in turn shared with others, also via peer-reviewed articles, which get published in larger and larger amounts every year. In this work, we review the literature related to the use of Artificial Intelligence (AI) in surgery. We focus on what is currently available and what is likely to come in the near future in both clinical care and research. We show that AI has the potential to be a key tool to elevate the effectiveness of training and decision-making in surgery and the discovery of relevant and valid scientific knowledge in the surgical domain. We also address concerns about AI technology, including the inability for users to interpret algorithms as well as incorrect predictions. A better understanding of AI will allow surgeons to use new tools wisely for the benefit of their patients.
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Affiliation(s)
- Miranda X. Morris
- Duke University School of Medicine, Duke University Hospital, Durham, NC, United States
| | - Davide Fiocco
- Department of Artificial Intelligence, Frontiers Media SA, Lausanne, Switzerland
| | - Tommaso Caneva
- Department of Artificial Intelligence, Frontiers Media SA, Lausanne, Switzerland
| | - Paris Yiapanis
- Department of Artificial Intelligence, Frontiers Media SA, Lausanne, Switzerland
| | - Dennis P. Orgill
- Harvard Medical School, Brigham and Women’s Hospital, Boston, MA, United States
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Han IH. Commentary on "Development and Validation of an Online Calculator to Predict Proximal Junctional Kyphosis After Adult Spinal Deformity Surgery Using Machine Learning". Neurospine 2023; 20:1281-1283. [PMID: 38171295 PMCID: PMC10762415 DOI: 10.14245/ns.2347302.651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2024] Open
Affiliation(s)
- In Ho Han
- Department of Neurosurgery, Pusan National University Hospital, Pusan National University School of Medicine, Busan, Korea
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Roman A, Al-Sharif L, Al Gharyani M. The Expanding Role of ChatGPT (Chat-Generative Pre-Trained Transformer) in Neurosurgery: A Systematic Review of Literature and Conceptual Framework. Cureus 2023; 15:e43502. [PMID: 37719492 PMCID: PMC10500385 DOI: 10.7759/cureus.43502] [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: 08/15/2023] [Indexed: 09/19/2023] Open
Abstract
The objective of this study is to explore the use of ChatGPT (Chat-Generative Pre-Trained Transformer) in neurosurgery and its potential impact on the field. The authors aim to discuss, through a systematic review of current literature, how this rising new artificial intelligence (AI) technology may prove to be a useful tool in the future, weighing its potential benefits and limitations. The authors conducted a comprehensive and systematic literature review of the use of ChatGPT and its applications in healthcare and different neurosurgery topics. Through a systematic review of the literature, with a search strategy using the databases such as PubMed, Google Scholar, and Embase, we analyzed the advantages and limitations of using ChatGPT in neurosurgery and evaluated its potential impact. ChatGPT has demonstrated promising results in various applications, such as natural language processing, language translation, and text summarization. In neurosurgery, ChatGPT can assist in different areas such as surgical planning, image recognition, medical diagnosis, patient care, and scientific production. A total of 128 articles were retrieved from databases, where the final 22 articles were included for thorough analysis. The studies reviewed demonstrate the potential of AI and deep learning (DL), through language models such as ChatGPT, to improve the accuracy and efficiency of neurosurgical procedures, as well as diagnosis, treatment, and patient outcomes across various medical specialties, including neurosurgery. There are, however, limitations to its use, including the need for large datasets and the potential for errors in the output, which most authors concur will need human verification for the final application. Our search demonstrated the potential that ChatGPT holds for the present and future, in accordance with the studies' authors' findings herein analyzed and expert opinions. Further research and development are required to fully understand its capabilities and limitations. AI technology can serve as a useful tool to augment human intelligence; however, it is essential to use it in a responsible and ethical manner.
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Affiliation(s)
- Alex Roman
- Neurological Surgery, Cleveland Clinic Abu Dhabi, Abu Dhabi, ARE
| | - Lubna Al-Sharif
- Physiology, Pharmacology and Toxicology, An-Najah National University, Nablus, PSE
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Chung M. Editors' Pick in March 2023. J Korean Neurosurg Soc 2023; 66:111-112. [PMID: 36800674 PMCID: PMC10009246 DOI: 10.3340/jkns.2023.0032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 02/17/2023] [Indexed: 02/21/2023] Open
Affiliation(s)
- Moonyoung Chung
- Managing Editor, Journal of Korean Neurosurgical Society; Department of Neurosurgery, Soonchunhyang University Bucheon Hospital, Soonchunhayng University College of Medicine, Bucheon, Korea
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