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Mandal S, Chakraborty S, Tariq MA, Ali K, Elavia Z, Khan MK, Garcia DB, Ali S, Al Hooti J, Kumar DV. Artificial Intelligence and Deep Learning in Revolutionizing Brain Tumor Diagnosis and Treatment: A Narrative Review. Cureus 2024; 16:e66157. [PMID: 39233936 PMCID: PMC11372433 DOI: 10.7759/cureus.66157] [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: 08/05/2024] [Indexed: 09/06/2024] Open
Abstract
The emergence of artificial intelligence (AI) in the medical field holds promise in improving medical management, particularly in personalized strategies for the diagnosis and treatment of brain tumors. However, integrating AI into clinical practice has proven to be a challenge. Deep learning (DL) is very convenient for extracting relevant information from large amounts of data that has increased in medical history and imaging records, which shortens diagnosis time, that would otherwise overwhelm manual methods. In addition, DL aids in automated tumor segmentation, classification, and diagnosis. DL models such as the Brain Tumor Classification Model and the Inception-Resnet V2, or hybrid techniques that enhance these functions and combine DL networks with support vector machine and k-nearest neighbors, identify tumor phenotypes and brain metastases, allowing real-time decision-making and enhancing preoperative planning. AI algorithms and DL development facilitate radiological diagnostics such as computed tomography, positron emission tomography scans, and magnetic resonance imaging (MRI) by integrating two-dimensional and three-dimensional MRI using DenseNet and 3D convolutional neural network architectures, which enable precise tumor delineation. DL offers benefits in neuro-interventional procedures, and the shift toward computer-assisted interventions acknowledges the need for more accurate and efficient image analysis methods. Further research is needed to realize the potential impact of DL in improving these outcomes.
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Affiliation(s)
- Shobha Mandal
- Internal Medicine, Guthrie Robert Packer Hospital, Sayre, USA
| | - Subhadeep Chakraborty
- Electronics and Communication, Maulana Abul Kalam Azad University of Technology, West Bengal, IND
| | | | - Kamran Ali
- Internal Medicine, United Medical and Dental College, Karachi, PAK
| | - Zenia Elavia
- Medical School, Dr. D. Y. Patil Medical College, Hospital & Research Centre, Pune, IND
| | - Misbah Kamal Khan
- Internal Medicine, Peoples University of Medical and Health Sciences, Nawabshah, PAK
| | | | - Sofia Ali
- Medical School, Peninsula Medical School, Plymouth, GBR
| | | | - Divyanshi Vijay Kumar
- Internal Medicine, Smt. Nathiba Hargovandas Lakhmichand Municipal Medical College, Ahmedabad, IND
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Jalal AHB, Ngai V, Hanrahan JG, Das A, Khan DZ, Cotton E, Sharela S, Stasiak M, Marcus HJ, Pandit AS. Empowering Early Career Neurosurgeons in the Critical Appraisal of Artificial Intelligence and Machine Learning: The Design and Evaluation of a Pilot Course. World Neurosurg 2024:S1878-8750(24)01307-X. [PMID: 39074581 DOI: 10.1016/j.wneu.2024.07.166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 07/22/2024] [Accepted: 07/23/2024] [Indexed: 07/31/2024]
Abstract
BACKGROUND Artificial intelligence (AI) is expected to play a greater role in neurosurgery. There is a need for neurosurgeons capable of critically appraising AI literature to evaluate its implementation or communicate information to patients. However, there are a lack of courses delivered at a level appropriate for individuals to develop such skills. We assessed the impact of a 2-day (non-credit bearing) online digital literacy course on the ability of individuals to critically appraise AI literature in neurosurgery. METHODS We performed a prospective, quasi-experimental non-randomized, controlled study with an intervention arm comprising individuals enrolled in our 2-day digital health literacy course and a waiting-list control arm used for comparison. We assessed participants' pre- and post-course knowledge, confidence, and course acceptability using Qualtrics surveys designed for the purpose of this study. RESULTS A total of 62 individuals (33 participants, 29 waitlist controls), including neurosurgical trainees and both undergraduate and post-graduate students, attended the course and completed the pre-course survey. The 2 groups did not vary significantly in terms of age or demographics. Following the course, participants significantly improved in their knowledge of AI (mean difference = 3.86, 95% CI = 2.97-4.75, P-value < 0.0001) and confidence in critically appraising literature using AI (P-value = 0.002). Similar differences in knowledge (mean difference = 3.15, 95% CI = 1.82-4.47, P-value < 0.0001) and confidence (P-value < 0.0001) were found when compared to the control group. CONCLUSIONS Bespoke courses delivered at an appropriate level can improve clinicians' understanding of the application of AI in neurosurgery, without the need for in-depth technical knowledge or programming skills.
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Affiliation(s)
| | - Victoria Ngai
- UCL Medical School, University College London, London, United Kingdom
| | - John Gerrard Hanrahan
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom; Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom
| | - Adrito Das
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom
| | - Danyal Z Khan
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom; Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom
| | - Elizabeth Cotton
- UCL Medical School, University College London, London, United Kingdom
| | - Shazia Sharela
- UCL Medical School, University College London, London, United Kingdom
| | - Martyna Stasiak
- Division of Psychology and Language Sciences, University College London, London, United Kingdom
| | - Hani J Marcus
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom; Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom
| | - Anand S Pandit
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom; High-Dimensional Neurology Group, Institute of Neurology, University College London, London, United Kingdom.
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Hawezy DJ. Perceptions of Surgeons in the Kurdistan Region of Iraq Regarding the Use of Artificial Intelligence. Cureus 2024; 16:e64885. [PMID: 39035593 PMCID: PMC11259771 DOI: 10.7759/cureus.64885] [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: 07/19/2024] [Indexed: 07/23/2024] Open
Abstract
INTRODUCTION Artificil intelligence (AI) is revolutionizing healthcare by seamlessly integrating into various aspects of human life. From robotic surgery to virtual nursing assistants and image analysis applications, AI is transforming the way we approach and deliver healthcare. By leveraging AI, patients can gain a deeper understanding of their symptoms, empowering them to make informed decisions about their health and ultimately improving their quality of life. Methods: An online survey collected data from social media platforms regarding the surgeon society in the Kurdistan region of Iraq. All statistical analyses were carried out using IBM SPSS Statistics for Windows, Version 25 (Released 2017; IBM Corp., Armonk, New York). RESULTS A total of 316 surgeons responded to the survey. A significant majority believed that using artificial intelligence benefits patients, and a substantial number advocated for its avoidance as a matter of principle. More than half said that AI would always impact education, and half of the participants said that AI would always affect complication prediction. CONCLUSIONS This is the first study investigating surgeon attitudes and perceptions regarding the use of AI in the Kurdistan region. The surgeons who responded generally appreciated AI's use in their practice. Notably, general surgeons showed greater openness to integrating AI into their daily practices compared to those in other surgical specialties.
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Affiliation(s)
- Dawan J Hawezy
- Surgery, Faculty of General Medicine, Koya University, Koya, IRQ
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Awuah WA, Aderinto N, Poornaselvan J, Tan JK, Shah MH, Ashinze P, Pujari AG, Bharadwaj HR, Abdul‐Rahman T, Atallah O. Empowering health care consumers & understanding patients' perspectives on AI integration in oncology and surgery: A perspective. Health Sci Rep 2024; 7:e2268. [PMID: 39050906 PMCID: PMC11266117 DOI: 10.1002/hsr2.2268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Revised: 03/24/2024] [Accepted: 07/08/2024] [Indexed: 07/27/2024] Open
Abstract
Introduction Artificial intelligence (AI) is transforming oncology and surgery by improving diagnostics, personalizing treatments, and enhancing surgical precision. Patients appreciate AI for its potential to provide accurate prognoses and tailored therapies. However, AI's implementation raises ethical concerns, data privacy issues, and the need for transparent communication between patients and health care providers. This study aims to understand patients' perspectives on AI integration in oncology and surgery to foster a balanced and patient-centered approach. Methods The study utilized a comprehensive literature review and analysis of existing research on AI applications in oncology and surgery. The focus was on examining patient perceptions, ethical considerations, and the potential benefits and risks associated with AI integration. Data was collected from peer-reviewed journals, conference proceedings, and expert opinions to provide a broad understanding of the topic. The perspectives of patients was also emphasized to highlight the nuances of their acceptance and concerns regarding AI in their health care. Results Patients generally perceive AI in oncology and surgery as beneficial, appreciating its potential for more accurate diagnoses, personalized treatment plans, and improved surgical outcomes. They particularly value AI's role in providing timely and precise diagnostics, which can lead to better prognoses and reduced anxiety. However, concerns about data privacy, ethical implications, and the reliability of AI systems were prevalent. Consequently, trust in AI and health care providers was deemed as a crucial factor for patient acceptance. Additionally, the need for transparent communication and ethical safeguards was also highlighted to address these concerns effectively. Conclusion The integration of AI in oncology and surgeryholds significant promise for enhancing patient care and outcomes. Patients view AI as a valuable tool that can provide accurate prognoses and personalized treatments. However, addressing ethical concerns, ensuring data privacy, and building trust through transparent communication are essential for successful AI integration. Future initiatives should focus on refining AI algorithms, establishing robust ethical guidelines, and enhancing patient education to harmonize technological advancements with patient-centered care principles.
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Affiliation(s)
| | - Nicholas Aderinto
- Internal Medicine DepartmentLAUTECH Teaching HospitalOgbomosoNigeria
| | | | | | | | - Patrick Ashinze
- Faculty of Clinical SciencesUniversity of IlorinIlorinNigeria
| | | | | | | | - Oday Atallah
- Department of NeurosurgeryHannover Medical SchoolHannoverGermany
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Livanis E, Voultsos P, Vadikolias K, Pantazakos P, Tsaroucha A. Understanding the Ethical Issues of Brain-Computer Interfaces (BCIs): A Blessing or the Beginning of a Dystopian Future? Cureus 2024; 16:e58243. [PMID: 38745805 PMCID: PMC11091939 DOI: 10.7759/cureus.58243] [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: 04/14/2024] [Indexed: 05/16/2024] Open
Abstract
In recent years, scientific discoveries in the field of neuroscience combined with developments in the field of artificial intelligence have led to the development of a range of neurotechnologies. Advances in neuroimaging systems, neurostimulators, and brain-computer interfaces (BCIs) are leading to new ways of enhancing, controlling, and "reading" the brain. In addition, although BCIs were developed and used primarily in the medical field, they are now increasingly applied in other fields (entertainment, marketing, education, defense industry). We conducted a literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to provide background information about ethical issues related to the use of BCIs. Among the ethical issues that emerged from the thematic data analysis of the reviewed studies included questions revolving around human dignity, personhood and autonomy, user safety, stigma and discrimination, privacy and security, responsibility, research ethics, and social justice (including access to this technology). This paper attempts to address the various aspects of these concerns. A variety of distinct ethical issues were identified, which, for the most part, were in line with the findings of prior research. However, we identified two nuances, which are related to the empirical research on ethical issues related to BCIs and the impact of BCIs on international relationships. The paper also highlights the need for the cooperation of all stakeholders to ensure the ethical development and use of this technology and concludes with several recommendations. The principles of bioethics provide an initial guiding framework, which, however, should be revised in the current artificial intelligence landscape so as to be responsive to challenges posed by the development and use of BCIs.
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Affiliation(s)
- Efstratios Livanis
- Department of Accounting and Finance, University of Macedonia, Thessaloniki, GRC
- Postgraduate Program on Bioethics, School of Medicine, Democritus University of Thrace, Alexandroupoli, GRC
| | - Polychronis Voultsos
- Laboratory of Forensic Medicine & Toxicology (Medical Law and Ethics) School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, GRC
- Postgraduate Program on Bioethics, School of Medicine, Democritus University of Thrace, Alexandroupoli, GRC
| | - Konstantinos Vadikolias
- Postgraduate Program on Bioethics, School of Medicine, Democritus University of Thrace, Alexandroupoli, GRC
- Department of Neurology, University Hospital of Alexandroupolis, Alexandroupoli, GRC
| | - Panagiotis Pantazakos
- Department of Philosophy, School of Philosophy, National and Kapodistrian University of Athens, Athens, GRC
- Postgraduate Program on Bioethics, School of Medicine, Democritus University of Thrace, Alexandroupoli, GRC
| | - Alexandra Tsaroucha
- Postgraduate Program on Bioethics, School of Medicine, Democritus University of Thrace, Alexandroupoli, GRC
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St John A, Cooper L, Kavic SM. The Role of Artificial Intelligence in Surgery: What do General Surgery Residents Think? Am Surg 2024; 90:541-549. [PMID: 37863479 DOI: 10.1177/00031348231209524] [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] [Indexed: 10/22/2023]
Abstract
BACKGROUND Artificial intelligence (AI) holds significant potential in medical education and patient care, but its rapid emergence presents ethical and practical challenges. This study explored the perspectives of surgical residents on AI's role in medicine. METHODS We performed a cross-sectional study surveying general surgery residents at a university-affiliated teaching hospital about their views on AI in medicine and surgical training. The survey covered demographics, residents' understanding of AI, its integration into medical practice, and use of AI tools like ChatGPT. The survey design was inspired by a recent national survey and underwent pretesting before deployment. RESULTS Of the 31 participants surveyed, 24% identified diagnostics as AI's top application, 12% favored its use in identifying anatomical structures in surgeries, and 20% endorsed AI integration into EMRs for predictive models. Attitudes toward AI varied based on its intended application: 77.41% expressed concern about AI making life decisions and 70.97% felt excited about its application for repetitive tasks. A significant 67.74% believed AI could enhance the understanding of medical knowledge. Perception of AI integration varied with AI familiarity (P = .01), with more knowledgeable respondents expressing more positivity. Moreover, familiarity influenced the perceived academic use of ChatGPT (P = .039) and attitudes toward AI in operating rooms (P = .032). Conclusion: This study provides insights into surgery residents' perceptions of AI in medical practice and training. These findings can inform future research, shape policy decisions, and guide AI development, promoting a harmonious collaboration between AI and surgeons to improve both training and patient care.
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Affiliation(s)
- Ace St John
- University of Maryland Medical Center, Baltimore, MD, USA
| | - Laura Cooper
- University of Maryland Medical Center, Baltimore, MD, USA
| | - Stephen M Kavic
- University of Maryland School of Medicine, Baltimore, MD, USA
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Mehmood Qadri H, Bashir M, Khan M, Amir A, Khan AYY, Safdar Z, Chaudhry H, Younas UA, Bashir A. Knowledge, Awareness and Practice of Artificial Intelligence and Types of Realities Among Healthcare Professionals: A Nationwide Survey From Pakistan. Cureus 2024; 16:e57695. [PMID: 38711703 PMCID: PMC11070734 DOI: 10.7759/cureus.57695] [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: 04/05/2024] [Indexed: 05/08/2024] Open
Abstract
Background Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, enabling them to perform tasks. The advancements in AI have also improved virtual reality (VR), augmented reality (AR) and mixed reality (MR) experience allowing a greater opportunity for use in the field of medicine. Objective To evaluate the knowledge, attitude and practice of AI and types of realities among Pakistani healthcare professionals (HCPs). Materials and methods This was a prospective, nationwide study designed at the Department of Neurosurgery at Punjab Institute of Neurosciences (PINS), Lahore, was conducted between January 2024 to February 2024. More than 500 HCPs were approached, out of which 176 participated in this survey consensually. A pre-formed general questionnaire based on knowledge, attitude and practices of AI and types of realities was modified according to local conditions. Google Forms (Google Inc., USA) was used to conduct the one-time sign up response. Statistical Package for Social Sciences (IBM SPSS Statistics for Windows, Version 24, USA) was used to analyze submitted responses. Results About 69.9% respondents were male HCPs. Most of the respondents were from the fields of neurosurgery, medicine and general surgery, i.e., 10.80%, 10.20% and 4%, respectively. More than 90% HCPs used Internet and electronic devices daily. A majority of 62.50% respondents agreed that AI brings benefits for the patients, while at the same time, 45.50% agreed that they would not trust the assessment of AI more than that of HCPs. 61% HCPs feared that AI-based systems could be manipulated from the outside sources, like terrorists and hackers. Although 90% respondents knew the definition of AR and VR, a strikingly low 40% respondents could only identify the practical applications of these realities when asked in a mini-quiz. About 61.40% HCPs never used any AI-based application throughout their clinical practice, but Google Health was used by 29.50% respondents, followed by Remote Patient Monitoring AI application used by 3.4% individuals. Conclusion There is an evident under-utilization of AI and types of realities in clinical practice in Pakistan. Lack of awareness, paucity of resources and conventional clinical practices are the key reasons identified. Pakistan is on the path towards the point where the developed world is currently. There is a potential to move past the initial stages of AI implementation and into more advanced modes of adopting AI and types of realities.
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Affiliation(s)
| | - Momin Bashir
- Microbiology, New York University, New York, USA
| | - Manal Khan
- Neurological Surgery, Punjab Institute of Neurosciences, Lahore, PAK
| | - Arham Amir
- General Surgery and Surgical Oncology, Shaikh Zayed Medical Complex, Lahore, PAK
| | | | - Zainab Safdar
- General Surgery, Lahore General Hospital, Lahore, PAK
| | | | | | - Asif Bashir
- Neurological Surgery, Punjab Institute of Neurosciences, Lahore, PAK
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Schonfeld E, Mordekai N, Berg A, Johnstone T, Shah A, Shah V, Haider G, Marianayagam NJ, Veeravagu A. Machine Learning in Neurosurgery: Toward Complex Inputs, Actionable Predictions, and Generalizable Translations. Cureus 2024; 16:e51963. [PMID: 38333513 PMCID: PMC10851045 DOI: 10.7759/cureus.51963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Accepted: 01/08/2024] [Indexed: 02/10/2024] Open
Abstract
Machine learning can predict neurosurgical diagnosis and outcomes, power imaging analysis, and perform robotic navigation and tumor labeling. State-of-the-art models can reconstruct and generate images, predict surgical events from video, and assist in intraoperative decision-making. In this review, we will detail the neurosurgical applications of machine learning, ranging from simple to advanced models, and their potential to transform patient care. As machine learning techniques, outputs, and methods become increasingly complex, their performance is often more impactful yet increasingly difficult to evaluate. We aim to introduce these advancements to the neurosurgical audience while suggesting major potential roadblocks to their safe and effective translation. Unlike the previous generation of machine learning in neurosurgery, the safe translation of recent advancements will be contingent on neurosurgeons' involvement in model development and validation.
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Affiliation(s)
- Ethan Schonfeld
- Neurosurgery, Stanford University School of Medicine, Stanford, USA
| | | | - Alex Berg
- Neurosurgery, Stanford University School of Medicine, Stanford, USA
| | - Thomas Johnstone
- Neurosurgery, Stanford University School of Medicine, Stanford, USA
| | - Aaryan Shah
- School of Humanities and Sciences, Stanford University, Stanford, USA
| | - Vaibhavi Shah
- Neurosurgery, Stanford University School of Medicine, Stanford, USA
| | - Ghani Haider
- Neurosurgery, Stanford University School of Medicine, Stanford, USA
| | | | - Anand Veeravagu
- Neurosurgery, Stanford University School of Medicine, Stanford, USA
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Ruparelia J, Manjunath N, Nachiappan DS, Raheja A, Suri A. Virtual Reality in Preoperative Planning of Complex Cranial Surgery. World Neurosurg 2023; 180:e11-e18. [PMID: 37307986 DOI: 10.1016/j.wneu.2023.06.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 06/05/2023] [Indexed: 06/14/2023]
Abstract
OBJECTIVE Changing paradigms of neurosurgical training and limited operative exposure during the residency period have made it necessary to evaluate newer technologies for training. Virtual reality (VR) technology provides three-dimensional reconstruction of routine imaging, along with the ability to see as well as interact. The application of VR technology in operative planning, which is an important part of neurosurgical training, has been incompletely studied so far. METHODS Sixteen final-year residents, post-M.Ch. (magister chirurgiae) residents, and fellows were included as study participants. They were divided into 2 groups based on their seniority for further analysis. Five complex cranial cases were selected and a multiple-choice question-based test was prepared by the authors, with 5 questions for each of the cases. The pretest score was determined based on performance on the test after participants accessed routine preoperative imaging. The posttest score was calculated after use of the VR system (ImmersiveTouch VR System, ImmersiveTouch Inc.). Analysis was performed by the investigators, who were blinded to the identity of the participant. Subanalysis based on the type of case and type of question was performed. Feedback was obtained from each participant regarding VR use. RESULTS There was an overall improvement in scores from pretest to posttest, which was also noted in the analysis based on the participants' seniority. This improvement was noted to be more for the vascular cases (15.89%) compared with the tumor cases (7.84%). Participants also fared better in questions related to surgical anatomy and surgical approach, compared with questions based on the diagnosis. There was overall positive feedback from participants regarding VR use, and most participants wanted VR to become a routine part of operative planning. CONCLUSIONS Our study shows that there is improvement in understanding of surgical aspects after use of this VR system.
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Affiliation(s)
- Jigish Ruparelia
- Department of Neurosurgery, All India Institute of Medical Sciences (AIIMS), New Delhi, India
| | - Niveditha Manjunath
- Department of Neurosurgery, All India Institute of Medical Sciences (AIIMS), New Delhi, India
| | | | - Amol Raheja
- Department of Neurosurgery, All India Institute of Medical Sciences (AIIMS), New Delhi, India
| | - Ashish Suri
- Department of Neurosurgery, All India Institute of Medical Sciences (AIIMS), New Delhi, India.
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Vo V, Chen G, Aquino YSJ, Carter SM, Do QN, Woode ME. Multi-stakeholder preferences for the use of artificial intelligence in healthcare: A systematic review and thematic analysis. Soc Sci Med 2023; 338:116357. [PMID: 37949020 DOI: 10.1016/j.socscimed.2023.116357] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 09/04/2023] [Accepted: 10/24/2023] [Indexed: 11/12/2023]
Abstract
INTRODUCTION Despite the proliferation of Artificial Intelligence (AI) technology over the last decade, clinician, patient, and public perceptions of its use in healthcare raise a number of ethical, legal and social questions. We systematically review the literature on attitudes towards the use of AI in healthcare from patients, the general public and health professionals' perspectives to understand these issues from multiple perspectives. METHODOLOGY A search for original research articles using qualitative, quantitative, and mixed methods published between 1 Jan 2001 to 24 Aug 2021 was conducted on six bibliographic databases. Data were extracted and classified into different themes representing views on: (i) knowledge and familiarity of AI, (ii) AI benefits, risks, and challenges, (iii) AI acceptability, (iv) AI development, (v) AI implementation, (vi) AI regulations, and (vii) Human - AI relationship. RESULTS The final search identified 7,490 different records of which 105 publications were selected based on predefined inclusion/exclusion criteria. While the majority of patients, the general public and health professionals generally had a positive attitude towards the use of AI in healthcare, all groups indicated some perceived risks and challenges. Commonly perceived risks included data privacy; reduced professional autonomy; algorithmic bias; healthcare inequities; and greater burnout to acquire AI-related skills. While patients had mixed opinions on whether healthcare workers suffer from job loss due to the use of AI, health professionals strongly indicated that AI would not be able to completely replace them in their professions. Both groups shared similar doubts about AI's ability to deliver empathic care. The need for AI validation, transparency, explainability, and patient and clinical involvement in the development of AI was emphasised. To help successfully implement AI in health care, most participants envisioned that an investment in training and education campaigns was necessary, especially for health professionals. Lack of familiarity, lack of trust, and regulatory uncertainties were identified as factors hindering AI implementation. Regarding AI regulations, key themes included data access and data privacy. While the general public and patients exhibited a willingness to share anonymised data for AI development, there remained concerns about sharing data with insurance or technology companies. One key domain under this theme was the question of who should be held accountable in the case of adverse events arising from using AI. CONCLUSIONS While overall positivity persists in attitudes and preferences toward AI use in healthcare, some prevalent problems require more attention. There is a need to go beyond addressing algorithm-related issues to look at the translation of legislation and guidelines into practice to ensure fairness, accountability, transparency, and ethics in AI.
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Affiliation(s)
- Vinh Vo
- Centre for Health Economics, Monash University, Australia.
| | - Gang Chen
- Centre for Health Economics, Monash University, Australia
| | - Yves Saint James Aquino
- Australian Centre for Health Engagement, Evidence and Values, School of Health and Soceity, University of Wollongong, Australia
| | - Stacy M Carter
- Australian Centre for Health Engagement, Evidence and Values, School of Health and Soceity, University of Wollongong, Australia
| | - Quynh Nga Do
- Department of Economics, Monash University, Australia
| | - Maame Esi Woode
- Centre for Health Economics, Monash University, Australia; Monash Data Futures Research Institute, Australia
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Buyck F, Vandemeulebroucke J, Ceranka J, Van Gestel F, Cornelius JF, Duerinck J, Bruneau M. Computer-vision based analysis of the neurosurgical scene - A systematic review. BRAIN & SPINE 2023; 3:102706. [PMID: 38020988 PMCID: PMC10668095 DOI: 10.1016/j.bas.2023.102706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 10/23/2023] [Accepted: 10/29/2023] [Indexed: 12/01/2023]
Abstract
Introduction With increasing use of robotic surgical adjuncts, artificial intelligence and augmented reality in neurosurgery, the automated analysis of digital images and videos acquired over various procedures becomes a subject of increased interest. While several computer vision (CV) methods have been developed and implemented for analyzing surgical scenes, few studies have been dedicated to neurosurgery. Research question In this work, we present a systematic literature review focusing on CV methodologies specifically applied to the analysis of neurosurgical procedures based on intra-operative images and videos. Additionally, we provide recommendations for the future developments of CV models in neurosurgery. Material and methods We conducted a systematic literature search in multiple databases until January 17, 2023, including Web of Science, PubMed, IEEE Xplore, Embase, and SpringerLink. Results We identified 17 studies employing CV algorithms on neurosurgical videos/images. The most common applications of CV were tool and neuroanatomical structure detection or characterization, and to a lesser extent, surgical workflow analysis. Convolutional neural networks (CNN) were the most frequently utilized architecture for CV models (65%), demonstrating superior performances in tool detection and segmentation. In particular, mask recurrent-CNN manifested most robust performance outcomes across different modalities. Discussion and conclusion Our systematic review demonstrates that CV models have been reported that can effectively detect and differentiate tools, surgical phases, neuroanatomical structures, as well as critical events in complex neurosurgical scenes with accuracies above 95%. Automated tool recognition contributes to objective characterization and assessment of surgical performance, with potential applications in neurosurgical training and intra-operative safety management.
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Affiliation(s)
- Félix Buyck
- Department of Neurosurgery, Universitair Ziekenhuis Brussel (UZ Brussel), 1090, Brussels, Belgium
- Vrije Universiteit Brussel (VUB), Research group Center For Neurosciences (C4N-NEUR), 1090, Brussels, Belgium
| | - Jef Vandemeulebroucke
- Vrije Universiteit Brussel (VUB), Department of Electronics and Informatics (ETRO), 1050, Brussels, Belgium
- Department of Radiology, Universitair Ziekenhuis Brussel (UZ Brussel), 1090, Brussels, Belgium
- imec, 3001, Leuven, Belgium
| | - Jakub Ceranka
- Vrije Universiteit Brussel (VUB), Department of Electronics and Informatics (ETRO), 1050, Brussels, Belgium
- imec, 3001, Leuven, Belgium
| | - Frederick Van Gestel
- Department of Neurosurgery, Universitair Ziekenhuis Brussel (UZ Brussel), 1090, Brussels, Belgium
- Vrije Universiteit Brussel (VUB), Research group Center For Neurosciences (C4N-NEUR), 1090, Brussels, Belgium
| | - Jan Frederick Cornelius
- Department of Neurosurgery, Medical Faculty, Heinrich-Heine-University, 40225, Düsseldorf, Germany
| | - Johnny Duerinck
- Department of Neurosurgery, Universitair Ziekenhuis Brussel (UZ Brussel), 1090, Brussels, Belgium
- Vrije Universiteit Brussel (VUB), Research group Center For Neurosciences (C4N-NEUR), 1090, Brussels, Belgium
| | - Michaël Bruneau
- Department of Neurosurgery, Universitair Ziekenhuis Brussel (UZ Brussel), 1090, Brussels, Belgium
- Vrije Universiteit Brussel (VUB), Research group Center For Neurosciences (C4N-NEUR), 1090, Brussels, Belgium
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12
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Chen TC, Multala E, Kearns P, Delashaw J, Dumont A, Maraganore D, Wang A. Assessment of ChatGPT's performance on neurology written board examination questions. BMJ Neurol Open 2023; 5:e000530. [PMID: 37936648 PMCID: PMC10626870 DOI: 10.1136/bmjno-2023-000530] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 10/19/2023] [Indexed: 11/09/2023] Open
Abstract
Background and objectives ChatGPT has shown promise in healthcare. To assess the utility of this novel tool in healthcare education, we evaluated ChatGPT's performance in answering neurology board exam questions. Methods Neurology board-style examination questions were accessed from BoardVitals, a commercial neurology question bank. ChatGPT was provided a full question prompt and multiple answer choices. First attempts and additional attempts up to three tries were given to ChatGPT to select the correct answer. A total of 560 questions (14 blocks of 40 questions) were used, although any image-based questions were disregarded due to ChatGPT's inability to process visual input. The artificial intelligence (AI) answers were then compared with human user data provided by the question bank to gauge its performance. Results Out of 509 eligible questions over 14 question blocks, ChatGPT correctly answered 335 questions (65.8%) on the first attempt/iteration and 383 (75.3%) over three attempts/iterations, scoring at approximately the 26th and 50th percentiles, respectively. The highest performing subjects were pain (100%), epilepsy & seizures (85%) and genetic (82%) while the lowest performing subjects were imaging/diagnostic studies (27%), critical care (41%) and cranial nerves (48%). Discussion This study found that ChatGPT performed similarly to its human counterparts. The accuracy of the AI increased with multiple attempts and performance fell within the expected range of neurology resident learners. This study demonstrates ChatGPT's potential in processing specialised medical information. Future studies would better define the scope to which AI would be able to integrate into medical decision making.
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Affiliation(s)
- Tse Chian Chen
- Neurology, Tulane University School of Medicine, New Orleans, Louisiana, USA
| | - Evan Multala
- Tulane University School of Medicine, New Orleans, Louisiana, USA
| | - Patrick Kearns
- Tulane University School of Medicine, New Orleans, Louisiana, USA
| | - Johnny Delashaw
- Neurosurgery, Tulane University School of Medicine, New Orleans, Louisiana, USA
| | - Aaron Dumont
- Neurosurgery, Tulane University School of Medicine, New Orleans, Louisiana, USA
| | | | - Arthur Wang
- Neurosurgery, Tulane University School of Medicine, New Orleans, Louisiana, USA
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Eastwood KW, May R, Andreou P, Abidi S, Abidi SSR, Loubani OM. Needs and expectations for artificial intelligence in emergency medicine according to Canadian physicians. BMC Health Serv Res 2023; 23:798. [PMID: 37491228 PMCID: PMC10369807 DOI: 10.1186/s12913-023-09740-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 06/22/2023] [Indexed: 07/27/2023] Open
Abstract
BACKGROUND Artificial Intelligence (AI) is recognized by emergency physicians (EPs) as an important technology that will affect clinical practice. Several AI-tools have already been developed to aid care delivery in emergency medicine (EM). However, many EM tools appear to have been developed without a cross-disciplinary needs assessment, making it difficult to understand their broader importance to general-practice. Clinician surveys about AI tools have been conducted within other medical specialties to help guide future design. This study aims to understand the needs of Canadian EPs for the apt use of AI-based tools. METHODS A national cross-sectional, two-stage, mixed-method electronic survey of Canadian EPs was conducted from January-May 2022. The survey includes demographic and physician practice-pattern data, clinicians' current use and perceptions of AI, and individual rankings of which EM work-activities most benefit from AI. RESULTS The primary outcome is a ranked list of high-priority AI-tools for EM that physicians want translated into general use within the next 10 years. When ranking specific AI examples, 'automated charting/report generation', 'clinical prediction rules' and 'monitoring vitals with early-warning detection' were the top items. When ranking by physician work-activities, 'AI-tools for documentation', 'AI-tools for computer use' and 'AI-tools for triaging patients' were the top items. For secondary outcomes, EPs indicated AI was 'likely' (43.1%) or 'extremely likely' (43.7%) to be able to complete the task of 'documentation' and indicated either 'a-great-deal' (32.8%) or 'quite-a-bit' (39.7%) of potential for AI in EM. Further, EPs were either 'strongly' (48.5%) or 'somewhat' (39.8%) interested in AI for EM. CONCLUSIONS Physician input on the design of AI is essential to ensure the uptake of this technology. Translation of AI-tools to facilitate documentation is considered a high-priority, and respondents had high confidence that AI could facilitate this task. This study will guide future directions regarding the use of AI for EM and help direct efforts to address prevailing technology-translation barriers such as access to high-quality application-specific data and developing reporting guidelines for specific AI-applications. With a prioritized list of high-need AI applications, decision-makers can develop focused strategies to address these larger obstacles.
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Affiliation(s)
- Kyle W Eastwood
- Department of Emergency Medicine, Dalhousie University, 1796 Summer Street, Halifax Infirmary, 4Th Floor Emergency Department Administration Office, Halifax, NS, B3H 2Y9, Canada.
| | - Ronald May
- Department of Emergency Medicine, Dalhousie University, 1796 Summer Street, Halifax Infirmary, 4Th Floor Emergency Department Administration Office, Halifax, NS, B3H 2Y9, Canada
| | - Pantelis Andreou
- Department of Community Health and Epidemiology, Dalhousie University, Halifax, Canada
| | - Samina Abidi
- Department of Community Health and Epidemiology, Dalhousie University, Halifax, Canada
| | - Syed Sibte Raza Abidi
- NICHE Research Group, Faculty of Computer Science, Dalhousie University, Halifax, Canada
| | - Osama M Loubani
- Department of Emergency Medicine, Dalhousie University, 1796 Summer Street, Halifax Infirmary, 4Th Floor Emergency Department Administration Office, Halifax, NS, B3H 2Y9, Canada
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14
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Orlova IA, Akopyan ZA, Plisyuk AG, Tarasova EV, Borisov EN, Dolgushin GO, Khvatova EI, Grigoryan MA, Gabbasova LA, Kamalov AA. Opinion research among Russian Physicians on the application of technologies using artificial intelligence in the field of medicine and health care. BMC Health Serv Res 2023; 23:749. [PMID: 37442981 DOI: 10.1186/s12913-023-09493-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 05/03/2023] [Indexed: 07/15/2023] Open
Abstract
BACKGROUND To date, no opinion surveys has been conducted among Russian physicians to study their awareness about artificial intelligence. With a survey, we aimed to evaluate the attitudes of stakeholders to the usage of technologies employing AI in the field of medicine and healthcare and identify challenges and perspectives to introducing AI. METHODS We conducted a 12-question online survey using Google Forms. The survey consisted of questions related to the recognition of AI and attitudes towards it, the direction of development of AI in medicine and the possible risks of using AI in medicine. RESULTS 301 doctors took part in the survey. 107 (35.6%) responded that they are familiar with AI. The vast majority of participants considered AI useful in the medical field (85%). The advantage of AI was associated with the ability to analyze huge volumes of clinically relevant data in real time (79%). Respondents highlighted areas where AI would be most useful-organizational optimization (74%), biopharmaceutical research (67%), and disease diagnosis (52%). Among the possible problems when using AI, they noted the lack of flexibility and limited application on controversial issues (64% and 60% of respondents). 56% believe that AI decision making will be difficult if inadequate information is presented for analysis. A third of doctors fear that specialists with little experience took part in the development of AI, and 89% of respondents believe that doctors should participate in the development of AI for medicine and healthcare. Only 20 participants (6.6%) responded that they agree that AI can replace them at work. At the same time, 76% of respondents believe that in the future, doctors using AI will replace those who do not. CONCLUSIONS Russian doctors are for AI in medicine. Most of the respondents believe that AI will not replace them in the future and will become a useful tool. First of all, for optimizing organizational processes, research and diagnostics of diseases. TRIAL REGISTRATION This study was approved by the Local Ethics Committee of the Lomonosov Moscow State University Medical Research and Education Center (IRB00010587).
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Affiliation(s)
- I A Orlova
- Medical Research and Education Center of Lomonosov, Moscow State University, 27/10 Lomonosov Prospect, Moscow, 119192, Russia
- Faculty of Fundamental Medicine, Lomonosov Moscow State University, 27/1 Lomonosov Prospect, Moscow, 119192, Russia
| | - Zh A Akopyan
- Medical Research and Education Center of Lomonosov, Moscow State University, 27/10 Lomonosov Prospect, Moscow, 119192, Russia
- Faculty of Fundamental Medicine, Lomonosov Moscow State University, 27/1 Lomonosov Prospect, Moscow, 119192, Russia
| | - A G Plisyuk
- Medical Research and Education Center of Lomonosov, Moscow State University, 27/10 Lomonosov Prospect, Moscow, 119192, Russia
- Faculty of Fundamental Medicine, Lomonosov Moscow State University, 27/1 Lomonosov Prospect, Moscow, 119192, Russia
| | - E V Tarasova
- Medical Research and Education Center of Lomonosov, Moscow State University, 27/10 Lomonosov Prospect, Moscow, 119192, Russia.
- Faculty of Fundamental Medicine, Lomonosov Moscow State University, 27/1 Lomonosov Prospect, Moscow, 119192, Russia.
| | - E N Borisov
- Medical Research and Education Center of Lomonosov, Moscow State University, 27/10 Lomonosov Prospect, Moscow, 119192, Russia
| | - G O Dolgushin
- Faculty of Fundamental Medicine, Lomonosov Moscow State University, 27/1 Lomonosov Prospect, Moscow, 119192, Russia
| | - E I Khvatova
- Faculty of Fundamental Medicine, Lomonosov Moscow State University, 27/1 Lomonosov Prospect, Moscow, 119192, Russia
| | - M A Grigoryan
- Faculty of Fundamental Medicine, Lomonosov Moscow State University, 27/1 Lomonosov Prospect, Moscow, 119192, Russia
| | - L A Gabbasova
- Medical Research and Education Center of Lomonosov, Moscow State University, 27/10 Lomonosov Prospect, Moscow, 119192, Russia
- Faculty of Fundamental Medicine, Lomonosov Moscow State University, 27/1 Lomonosov Prospect, Moscow, 119192, Russia
| | - A A Kamalov
- Medical Research and Education Center of Lomonosov, Moscow State University, 27/10 Lomonosov Prospect, Moscow, 119192, Russia
- Faculty of Fundamental Medicine, Lomonosov Moscow State University, 27/1 Lomonosov Prospect, Moscow, 119192, Russia
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15
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Lambert SI, Madi M, Sopka S, Lenes A, Stange H, Buszello CP, Stephan A. An integrative review on the acceptance of artificial intelligence among healthcare professionals in hospitals. NPJ Digit Med 2023; 6:111. [PMID: 37301946 DOI: 10.1038/s41746-023-00852-5] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 05/25/2023] [Indexed: 06/12/2023] Open
Abstract
Artificial intelligence (AI) in the domain of healthcare is increasing in prominence. Acceptance is an indispensable prerequisite for the widespread implementation of AI. The aim of this integrative review is to explore barriers and facilitators influencing healthcare professionals' acceptance of AI in the hospital setting. Forty-two articles met the inclusion criteria for this review. Pertinent elements to the study such as the type of AI, factors influencing acceptance, and the participants' profession were extracted from the included studies, and the studies were appraised for their quality. The data extraction and results were presented according to the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The included studies revealed a variety of facilitating and hindering factors for AI acceptance in the hospital setting. Clinical decision support systems (CDSS) were the AI form included in most studies (n = 21). Heterogeneous results with regard to the perceptions of the effects of AI on error occurrence, alert sensitivity and timely resources were reported. In contrast, fear of a loss of (professional) autonomy and difficulties in integrating AI into clinical workflows were unanimously reported to be hindering factors. On the other hand, training for the use of AI facilitated acceptance. Heterogeneous results may be explained by differences in the application and functioning of the different AI systems as well as inter-professional and interdisciplinary disparities. To conclude, in order to facilitate acceptance of AI among healthcare professionals it is advisable to integrate end-users in the early stages of AI development as well as to offer needs-adjusted training for the use of AI in healthcare and providing adequate infrastructure.
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Affiliation(s)
- Sophie Isabelle Lambert
- AIXTRA-Competence Center for Training and Patient Safety, Medical Faculty, RWTH Aachen University, Pauwelsstraße 30, 52074, Aachen, Germany.
- Department of Anesthesiology, Uniklinik RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany.
| | - Murielle Madi
- Department of Nursing Science, Uniklinik RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany.
| | - Saša Sopka
- AIXTRA-Competence Center for Training and Patient Safety, Medical Faculty, RWTH Aachen University, Pauwelsstraße 30, 52074, Aachen, Germany
- Department of Anesthesiology, Uniklinik RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Andrea Lenes
- AIXTRA-Competence Center for Training and Patient Safety, Medical Faculty, RWTH Aachen University, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Hendrik Stange
- Fraunhofer Society for the Advancement of Applied Research. Fraunhofer-Institute for Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven 1, 53757, Sankt Augustin, Bonn, Germany
| | - Claus-Peter Buszello
- Fraunhofer Society for the Advancement of Applied Research. Fraunhofer-Institute for Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven 1, 53757, Sankt Augustin, Bonn, Germany
| | - Astrid Stephan
- Department of Nursing Science, Uniklinik RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
- Fliedner University of Applied Sciences, Geschwister-Aufricht-Straße, 940489, Düsseldorf, Germany
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16
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Henckert D, Malorgio A, Schweiger G, Raimann FJ, Piekarski F, Zacharowski K, Hottenrott S, Meybohm P, Tscholl DW, Spahn DR, Roche TR. Attitudes of Anesthesiologists toward Artificial Intelligence in Anesthesia: A Multicenter, Mixed Qualitative-Quantitative Study. J Clin Med 2023; 12:jcm12062096. [PMID: 36983099 PMCID: PMC10054443 DOI: 10.3390/jcm12062096] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 02/16/2023] [Accepted: 03/01/2023] [Indexed: 03/30/2023] Open
Abstract
Artificial intelligence (AI) is predicted to play an increasingly important role in perioperative medicine in the very near future. However, little is known about what anesthesiologists know and think about AI in this context. This is important because the successful introduction of new technologies depends on the understanding and cooperation of end users. We sought to investigate how much anesthesiologists know about AI and what they think about the introduction of AI-based technologies into the clinical setting. In order to better understand what anesthesiologists think of AI, we recruited 21 anesthesiologists from 2 university hospitals for face-to-face structured interviews. The interview transcripts were subdivided sentence-by-sentence into discrete statements, and statements were then grouped into key themes. Subsequently, a survey of closed questions based on these themes was sent to 70 anesthesiologists from 3 university hospitals for rating. In the interviews, the base level of knowledge of AI was good at 86 of 90 statements (96%), although awareness of the potential applications of AI in anesthesia was poor at only 7 of 42 statements (17%). Regarding the implementation of AI in anesthesia, statements were split roughly evenly between pros (46 of 105, 44%) and cons (59 of 105, 56%). Interviewees considered that AI could usefully be used in diverse tasks such as risk stratification, the prediction of vital sign changes, or as a treatment guide. The validity of these themes was probed in a follow-up survey of 70 anesthesiologists with a response rate of 70%, which confirmed an overall positive view of AI in this group. Anesthesiologists hold a range of opinions, both positive and negative, regarding the application of AI in their field of work. Survey-based studies do not always uncover the full breadth of nuance of opinion amongst clinicians. Engagement with specific concerns, both technical and ethical, will prove important as this technology moves from research to the clinic.
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Affiliation(s)
- David Henckert
- Institute of Anaesthesiology, University and University Hospital of Zurich, 8091 Zurich, Switzerland
| | - Amos Malorgio
- Institute of Anaesthesiology, University and University Hospital of Zurich, 8091 Zurich, Switzerland
| | - Giovanna Schweiger
- Institute of Anaesthesiology, University and University Hospital of Zurich, 8091 Zurich, Switzerland
| | - Florian J Raimann
- Department of Anaesthesiology, Intensive Care and Pain Medicine, Frankfurt University Hospital, 60590 Frankfurt am Main, Germany
| | - Florian Piekarski
- Department of Anaesthesiology, Intensive Care and Pain Medicine, Frankfurt University Hospital, 60590 Frankfurt am Main, Germany
| | - Kai Zacharowski
- Department of Anaesthesiology, Intensive Care and Pain Medicine, Frankfurt University Hospital, 60590 Frankfurt am Main, Germany
| | - Sebastian Hottenrott
- Department of Anaesthesiology, Intensive Care, Emergency and Pain Medicine, University Hospital Wuerzburg, 97080 Wuerzburg, Germany
| | - Patrick Meybohm
- Department of Anaesthesiology, Intensive Care, Emergency and Pain Medicine, University Hospital Wuerzburg, 97080 Wuerzburg, Germany
| | - David W Tscholl
- Institute of Anaesthesiology, University and University Hospital of Zurich, 8091 Zurich, Switzerland
| | - Donat R Spahn
- Institute of Anaesthesiology, University and University Hospital of Zurich, 8091 Zurich, Switzerland
| | - Tadzio R Roche
- Institute of Anaesthesiology, University and University Hospital of Zurich, 8091 Zurich, Switzerland
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17
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Hantel A, Clancy DD, Kehl KL, Marron JM, Van Allen EM, Abel GA. A Process Framework for Ethically Deploying Artificial Intelligence in Oncology. J Clin Oncol 2022; 40:3907-3911. [PMID: 35849792 PMCID: PMC9746763 DOI: 10.1200/jco.22.01113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 06/01/2022] [Accepted: 06/21/2022] [Indexed: 12/24/2022] Open
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18
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Chen M, Zhang B, Cai Z, Seery S, Gonzalez MJ, Ali NM, Ren R, Qiao Y, Xue P, Jiang Y. Acceptance of clinical artificial intelligence among physicians and medical students: A systematic review with cross-sectional survey. Front Med (Lausanne) 2022; 9:990604. [PMID: 36117979 PMCID: PMC9472134 DOI: 10.3389/fmed.2022.990604] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Accepted: 08/01/2022] [Indexed: 11/13/2022] Open
Abstract
Background Artificial intelligence (AI) needs to be accepted and understood by physicians and medical students, but few have systematically assessed their attitudes. We investigated clinical AI acceptance among physicians and medical students around the world to provide implementation guidance. Materials and methods We conducted a two-stage study, involving a foundational systematic review of physician and medical student acceptance of clinical AI. This enabled us to design a suitable web-based questionnaire which was then distributed among practitioners and trainees around the world. Results Sixty studies were included in this systematic review, and 758 respondents from 39 countries completed the online questionnaire. Five (62.50%) of eight studies reported 65% or higher awareness regarding the application of clinical AI. Although, only 10–30% had actually used AI and 26 (74.28%) of 35 studies suggested there was a lack of AI knowledge. Our questionnaire uncovered 38% awareness rate and 20% utility rate of clinical AI, although 53% lacked basic knowledge of clinical AI. Forty-five studies mentioned attitudes toward clinical AI, and over 60% from 38 (84.44%) studies were positive about AI, although they were also concerned about the potential for unpredictable, incorrect results. Seventy-seven percent were optimistic about the prospect of clinical AI. The support rate for the statement that AI could replace physicians ranged from 6 to 78% across 40 studies which mentioned this topic. Five studies recommended that efforts should be made to increase collaboration. Our questionnaire showed 68% disagreed that AI would become a surrogate physician, but believed it should assist in clinical decision-making. Participants with different identities, experience and from different countries hold similar but subtly different attitudes. Conclusion Most physicians and medical students appear aware of the increasing application of clinical AI, but lack practical experience and related knowledge. Overall, participants have positive but reserved attitudes about AI. In spite of the mixed opinions around clinical AI becoming a surrogate physician, there was a consensus that collaborations between the two should be strengthened. Further education should be conducted to alleviate anxieties associated with change and adopting new technologies.
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Affiliation(s)
- Mingyang Chen
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Bo Zhang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ziting Cai
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Samuel Seery
- Faculty of Health and Medicine, Division of Health Research, Lancaster University, Lancaster, United Kingdom
| | | | - Nasra M. Ali
- The First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Ran Ren
- Global Health Research Center, Dalian Medical University, Dalian, China
| | - Youlin Qiao
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- *Correspondence: Youlin Qiao,
| | - Peng Xue
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Peng Xue,
| | - Yu Jiang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Yu Jiang,
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19
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Williams SC, Horsfall HL, Funnell JP, Hanrahan JG, Schaefer AT, Muirhead W, Marcus HJ. Neurosurgical Team Acceptability of Brain-Computer Interfaces: A Two-Stage International Cross-Sectional Survey. World Neurosurg 2022; 164:e884-e898. [PMID: 35623610 PMCID: PMC10444691 DOI: 10.1016/j.wneu.2022.05.062] [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: 04/10/2022] [Accepted: 05/15/2022] [Indexed: 11/29/2022]
Abstract
OBJECTIVE Invasive brain-computer interfaces (BCIs) require neurosurgical implantation, which confers a range of risks. Despite this situation, no studies have assessed the acceptability of invasive BCIs among the neurosurgical team. This study aims to establish baseline knowledge of BCIs within the neurosurgical team and identify attitudes toward different applications of invasive BCI. METHODS A 2-stage cross-sectional international survey of the neurosurgical team (neurosurgeons, anesthetists, and operating room nurses) was conducted. Results from the first, qualitative, survey were used to guide the second-stage quantitative survey, which assessed acceptability of invasive BCI applications. Five-part Likert scales were used to collect quantitative data. Surveys were distributed internationally via social media and collaborators. RESULTS A total of 108 qualitative responses were collected. Themes included the promise of BCIs positively affecting disease targets, concerns regarding stability, and an overall positive emotional reaction to BCI technology. The quantitative survey generated 538 responses from 32 countries. Baseline knowledge of BCI technology was poor, with 9% claiming to have a good or expert knowledge of BCIs. Acceptability of invasive BCI for rehabilitative purposes was >80%. Invasive BCI for augmentation in healthy populations divided opinion. CONCLUSIONS The neurosurgical team's view of the acceptability of invasive BCI was divided across a range of indications. Some applications (e.g., stroke rehabilitation) were viewed as more appropriate than other applications (e.g., augmentation for military use). This range in views highlights the need for stakeholder consultation on acceptable use cases along with regulation and guidance to govern initial BCI implantations if patients are to realize the potential benefits.
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Affiliation(s)
- Simon C Williams
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom; Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), London, United Kingdom.
| | - Hugo Layard Horsfall
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom; Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), London, United Kingdom
| | - Jonathan P Funnell
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom; Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), London, United Kingdom
| | - John G Hanrahan
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom; Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), London, United Kingdom
| | - Andreas T Schaefer
- The Francis Crick Institute, Sensory Circuits and Neurotechnology Laboratory, London, United Kingdom; Department of Neuroscience, Physiology & Pharmacology, University College London, London, United Kingdom
| | - William Muirhead
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom; Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), London, United Kingdom
| | - Hani J Marcus
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom; Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), London, United Kingdom
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20
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Khan DZ, Luengo I, Barbarisi S, Addis C, Culshaw L, Dorward NL, Haikka P, Jain A, Kerr K, Koh CH, Layard Horsfall H, Muirhead W, Palmisciano P, Vasey B, Stoyanov D, Marcus HJ. Automated operative workflow analysis of endoscopic pituitary surgery using machine learning: development and preclinical evaluation (IDEAL stage 0). J Neurosurg 2022; 137:51-58. [PMID: 34740198 PMCID: PMC10243668 DOI: 10.3171/2021.6.jns21923] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 06/15/2021] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Surgical workflow analysis involves systematically breaking down operations into key phases and steps. Automatic analysis of this workflow has potential uses for surgical training, preoperative planning, and outcome prediction. Recent advances in machine learning (ML) and computer vision have allowed accurate automated workflow analysis of operative videos. In this Idea, Development, Exploration, Assessment, Long-term study (IDEAL) stage 0 study, the authors sought to use Touch Surgery for the development and validation of an ML-powered analysis of phases and steps in the endoscopic transsphenoidal approach (eTSA) for pituitary adenoma resection, a first for neurosurgery. METHODS The surgical phases and steps of 50 anonymized eTSA operative videos were labeled by expert surgeons. Forty videos were used to train a combined convolutional and recurrent neural network model by Touch Surgery. Ten videos were used for model evaluation (accuracy, F1 score), comparing the phase and step recognition of surgeons to the automatic detection of the ML model. RESULTS The longest phase was the sellar phase (median 28 minutes), followed by the nasal phase (median 22 minutes) and the closure phase (median 14 minutes). The longest steps were step 5 (tumor identification and excision, median 17 minutes); step 3 (posterior septectomy and removal of sphenoid septations, median 14 minutes); and step 4 (anterior sellar wall removal, median 10 minutes). There were substantial variations within the recorded procedures in terms of video appearances, step duration, and step order, with only 50% of videos containing all 7 steps performed sequentially in numerical order. Despite this, the model was able to output accurate recognition of surgical phases (91% accuracy, 90% F1 score) and steps (76% accuracy, 75% F1 score). CONCLUSIONS In this IDEAL stage 0 study, ML techniques have been developed to automatically analyze operative videos of eTSA pituitary surgery. This technology has previously been shown to be acceptable to neurosurgical teams and patients. ML-based surgical workflow analysis has numerous potential uses-such as education (e.g., automatic indexing of contemporary operative videos for teaching), improved operative efficiency (e.g., orchestrating the entire surgical team to a common workflow), and improved patient outcomes (e.g., comparison of surgical techniques or early detection of adverse events). Future directions include the real-time integration of Touch Surgery into the live operative environment as an IDEAL stage 1 (first-in-human) study, and further development of underpinning ML models using larger data sets.
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Affiliation(s)
- Danyal Z. Khan
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London
| | - Imanol Luengo
- Digital Surgery Ltd., Medtronic, London, United Kingdom
| | | | - Carole Addis
- Digital Surgery Ltd., Medtronic, London, United Kingdom
| | - Lucy Culshaw
- Digital Surgery Ltd., Medtronic, London, United Kingdom
| | - Neil L. Dorward
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London
| | - Pinja Haikka
- Digital Surgery Ltd., Medtronic, London, United Kingdom
| | - Abhiney Jain
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London
| | - Karen Kerr
- Digital Surgery Ltd., Medtronic, London, United Kingdom
| | - Chan Hee Koh
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London
| | - Hugo Layard Horsfall
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London
| | - William Muirhead
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London
| | - Paolo Palmisciano
- Department of Neurosurgery, Trauma Center, Gamma Knife Center, Cannizzaro Hospital, Catania, Italy; and
| | - Baptiste Vasey
- Nuffield Department of Surgical Sciences, University of Oxford, United Kingdom
| | - Danail Stoyanov
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London
- Digital Surgery Ltd., Medtronic, London, United Kingdom
| | - Hani J. Marcus
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London
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21
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Williams S, Layard Horsfall H, Funnell JP, Hanrahan JG, Khan DZ, Muirhead W, Stoyanov D, Marcus HJ. Artificial Intelligence in Brain Tumour Surgery-An Emerging Paradigm. Cancers (Basel) 2021; 13:cancers13195010. [PMID: 34638495 PMCID: PMC8508169 DOI: 10.3390/cancers13195010] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 10/02/2021] [Accepted: 10/03/2021] [Indexed: 01/01/2023] Open
Abstract
Artificial intelligence (AI) platforms have the potential to cause a paradigm shift in brain tumour surgery. Brain tumour surgery augmented with AI can result in safer and more effective treatment. In this review article, we explore the current and future role of AI in patients undergoing brain tumour surgery, including aiding diagnosis, optimising the surgical plan, providing support during the operation, and better predicting the prognosis. Finally, we discuss barriers to the successful clinical implementation, the ethical concerns, and we provide our perspective on how the field could be advanced.
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Affiliation(s)
- Simon Williams
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK; (H.L.H.); (J.P.F.); (J.G.H.); (D.Z.K.); (W.M.); (H.J.M.)
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
- Correspondence:
| | - Hugo Layard Horsfall
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK; (H.L.H.); (J.P.F.); (J.G.H.); (D.Z.K.); (W.M.); (H.J.M.)
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
| | - Jonathan P. Funnell
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK; (H.L.H.); (J.P.F.); (J.G.H.); (D.Z.K.); (W.M.); (H.J.M.)
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
| | - John G. Hanrahan
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK; (H.L.H.); (J.P.F.); (J.G.H.); (D.Z.K.); (W.M.); (H.J.M.)
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
| | - Danyal Z. Khan
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK; (H.L.H.); (J.P.F.); (J.G.H.); (D.Z.K.); (W.M.); (H.J.M.)
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
| | - William Muirhead
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK; (H.L.H.); (J.P.F.); (J.G.H.); (D.Z.K.); (W.M.); (H.J.M.)
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
| | - Danail Stoyanov
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
| | - Hani J. Marcus
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK; (H.L.H.); (J.P.F.); (J.G.H.); (D.Z.K.); (W.M.); (H.J.M.)
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
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22
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Tariciotti L, Palmisciano P, Giordano M, Remoli G, Lacorte E, Bertani G, Locatelli M, Dimeco F, Caccavella VM, Prada F. Artificial intelligence-enhanced intraoperative neurosurgical workflow: state of the art and future perspectives. J Neurosurg Sci 2021; 66:139-150. [PMID: 34545735 DOI: 10.23736/s0390-5616.21.05483-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
BACKGROUND Artificial Intelligence (AI) and Machine Learning (ML) augment decision-making processes and productivity by supporting surgeons over a range of clinical activities: from diagnosis and preoperative planning to intraoperative surgical assistance. We reviewed the literature to identify current AI platforms applied to neurosurgical perioperative and intraoperative settings and describe their role in multiple subspecialties. METHODS A systematic review of the literature was conducted following the PRISMA guidelines. PubMed, EMBASE, and Scopus databases were searched from inception to December 31, 2020. Original articles were included if they: presented AI platforms implemented in perioperative, intraoperative settings and reported ML models' performance metrics. Due to the heterogeneity in neurosurgical applications, a qualitative synthesis was deemed appropriate. The risk of bias and applicability of predicted outcomes were assessed using the PROBAST tool. RESULTS 41 articles were included. All studies evaluated a supervised learning algorithm. A total of 10 ML models were described; the most frequent were neural networks (n = 15) and tree-based models (n = 13). Overall, the risk of bias was medium-high, but applicability was considered positive for all studies. Articles were grouped into 4 categories according to the subspecialty of interest: neuro-oncology, spine, functional and other. For each category, different prediction tasks were identified. CONCLUSIONS In this review, we summarize the state-of-art applications of AI for the intraoperative augmentation of neurosurgical workflows across multiple subspecialties. ML models may boost surgical team performances by reducing human errors and providing patient-tailored surgical plans, but further and higher-quality studies need to be conducted.
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Affiliation(s)
- Leonardo Tariciotti
- Unit of Neurosurgery, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Milan, Italy.,Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy.,NEVRALIS, Milan, Italy
| | - Paolo Palmisciano
- NEVRALIS, Milan, Italy.,Department of Neurosurgery, Trauma, Gamma Knife Center Cannizzaro Hospital, Catania, Italy
| | - Martina Giordano
- NEVRALIS, Milan, Italy.,Department of Neurosurgery, Fondazione Policlinico Universitario A Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Giulia Remoli
- NEVRALIS, Milan, Italy.,National Center for Disease Prevention and Health Promotion, Italian National Institute of Health, Rome, Italy
| | - Eleonora Lacorte
- National Center for Disease Prevention and Health Promotion, Italian National Institute of Health, Rome, Italy
| | - Giulio Bertani
- Unit of Neurosurgery, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Marco Locatelli
- Unit of Neurosurgery, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Milan, Italy.,Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy.,Aldo Ravelli Research Center for Neurotechnology and Experimental Brain Therapeutics, University of Milan, Milan, Italy
| | - Francesco Dimeco
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico C. Besta, Milan, Italy
| | - Valerio M Caccavella
- NEVRALIS, Milan, Italy - .,Department of Neurosurgery, Fondazione Policlinico Universitario A Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Francesco Prada
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico C. Besta, Milan, Italy.,Department of Neurological Surgery, University of Virginia Health Science Center, Charlottesville, VA, USA
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23
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Abstract
Neurosurgeons receive extensive and lengthy training to equip themselves with various technical skills, and neurosurgery require a great deal of pre-, intra- and postoperative clinical data collection, decision making, care and recovery. The last decade has seen a significant increase in the importance of artificial intelligence (AI) in neurosurgery. AI can provide a great promise in neurosurgery by complementing neurosurgeons' skills to provide the best possible interventional and noninterventional care for patients by enhancing diagnostic and prognostic outcomes in clinical treatment and help neurosurgeons with decision making during surgical interventions to improve patient outcomes. Furthermore, AI is playing a pivotal role in the production, processing and storage of clinical and experimental data. AI usage in neurosurgery can also reduce the costs associated with surgical care and provide high-quality healthcare to a broader population. Additionally, AI and neurosurgery can build a symbiotic relationship where AI helps to push the boundaries of neurosurgery, and neurosurgery can help AI to develop better and more robust algorithms. This review explores the role of AI in interventional and noninterventional aspects of neurosurgery during pre-, intra- and postoperative care, such as diagnosis, clinical decision making, surgical operation, prognosis, data acquisition, and research within the neurosurgical arena.
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Affiliation(s)
- Mohammad Mofatteh
- Sir William Dunn School of Pathology, Medical Sciences Division, University of Oxford, South Parks Road, Oxford OX1 3RE, United Kingdom
- Lincoln College, University of Oxford, Turl Street, Oxford OX1 3DR, United Kingdom
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24
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Dagi TF, Barker FG, Glass J. Machine Learning and Artificial Intelligence in Neurosurgery: Status, Prospects, and Challenges. Neurosurgery 2021; 89:133-142. [PMID: 34015816 DOI: 10.1093/neuros/nyab170] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Affiliation(s)
- T Forcht Dagi
- Queen's University Belfast and The William J. Clinton Leadership Institute, Belfast, UK
- Mayo Clinic College of Medicine and Science, Rochester, Minnesota, USA
| | - Fred G Barker
- Department of Neurosurgery, Harvard Medical School, Boston, Massachusetts, USA
- The Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Jacob Glass
- Center for Epigenetics Research, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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