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Chen S, Lobo BC. Regulatory and Implementation Considerations for Artificial Intelligence. Otolaryngol Clin North Am 2024; 57:871-886. [PMID: 38839554 DOI: 10.1016/j.otc.2024.04.007] [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: 06/07/2024]
Abstract
Successful artificial intelligence (AI) implementation is predicated on the trust of clinicians and patients, and is achieved through a culture of responsible use, focusing on regulations, standards, and education. Otolaryngologists can overcome barriers in AI implementation by promoting data standardization through professional societies, engaging in institutional efforts to integrate AI, and developing otolaryngology-specific AI education for both trainees and practitioners.
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Affiliation(s)
- Si Chen
- Department of Otolaryngology - Head and Neck Surgery, University of Florida College of Medicine, 1345 Center Drive, PO Box 100264, Gainesville, FL 32610, USA.
| | - Brian C Lobo
- Department of Otolaryngology - Head and Neck Surgery, University of Florida College of Medicine, 1345 Center Drive, PO Box 100264, Gainesville, FL 32610, USA
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Mirzaei T, Amini L, Esmaeilzadeh P. Clinician voices on ethics of LLM integration in healthcare: a thematic analysis of ethical concerns and implications. BMC Med Inform Decis Mak 2024; 24:250. [PMID: 39252056 PMCID: PMC11382443 DOI: 10.1186/s12911-024-02656-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Accepted: 08/27/2024] [Indexed: 09/11/2024] Open
Abstract
OBJECTIVES This study aimed to explain and categorize key ethical concerns about integrating large language models (LLMs) in healthcare, drawing particularly from the perspectives of clinicians in online discussions. MATERIALS AND METHODS We analyzed 3049 posts and comments extracted from a self-identified clinician subreddit using unsupervised machine learning via Latent Dirichlet Allocation and a structured qualitative analysis methodology. RESULTS Analysis uncovered 14 salient themes of ethical implications, which we further consolidated into 4 overarching domains reflecting ethical issues around various clinical applications of LLM in healthcare, LLM coding, algorithm, and data governance, LLM's role in health equity and the distribution of public health services, and the relationship between users (human) and LLM systems (machine). DISCUSSION Mapping themes to ethical frameworks in literature illustrated multifaceted issues covering transparent LLM decisions, fairness, privacy, access disparities, user experiences, and reliability. CONCLUSION This study emphasizes the need for ongoing ethical review from stakeholders to ensure responsible innovation and advocates for tailored governance to enhance LLM use in healthcare, aiming to improve clinical outcomes ethically and effectively.
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Affiliation(s)
- Tala Mirzaei
- Information Systems & Business Analytics, College of Business, Florida International University, 11200 S.W. 8th St., Room RB 250, Miami, FL, 33199, USA.
| | - Leila Amini
- Information Systems & Business Analytics, College of Business, Florida International University, 11200 S.W. 8th St., Room RB 250, Miami, FL, 33199, USA
| | - Pouyan Esmaeilzadeh
- Information Systems & Business Analytics, College of Business, Florida International University, 11200 S.W. 8th St., Room RB 250, Miami, FL, 33199, USA
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Thavanesan N, Farahi A, Parfitt C, Belkhatir Z, Azim T, Vallejos EP, Walters Z, Ramchurn S, Underwood TJ, Vigneswaran G. Insights from explainable AI in oesophageal cancer team decisions. Comput Biol Med 2024; 180:108978. [PMID: 39106674 DOI: 10.1016/j.compbiomed.2024.108978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 07/31/2024] [Accepted: 07/31/2024] [Indexed: 08/09/2024]
Abstract
BACKGROUND Clinician-led quality control into oncological decision-making is crucial for optimising patient care. Explainable artificial intelligence (XAI) techniques provide data-driven approaches to unravel how clinical variables influence this decision-making. We applied global XAI techniques to examine the impact of key clinical decision-drivers when mapped by a machine learning (ML) model, on the likelihood of receiving different oesophageal cancer (OC) treatment modalities by the multidisciplinary team (MDT). METHODS Retrospective analysis of 893 OC patients managed between 2010 and 2022 at our tertiary unit, used a random forests (RF) classifier to predict four possible treatment pathways as determined by the MDT: neoadjuvant chemotherapy followed by surgery (NACT + S), neoadjuvant chemoradiotherapy followed by surgery (NACRT + S), surgery-alone, and palliative management. Variable importance and partial dependence (PD) analyses then examined the influence of targeted high-ranking clinical variables within the ML model on treatment decisions as a surrogate model of the MDT decision-making dynamic. RESULTS Amongst guideline-variables known to determine treatments, such as Tumour-Node-Metastasis (TNM) staging, age also proved highly important to the RF model (16.1 % of total importance) on variable importance analysis. PD subsequently revealed that predicted probabilities for all treatment modalities change significantly after 75 years (p < 0.001). Likelihood of surgery-alone and palliative therapies increased for patients aged 75-85yrs but lowered for NACT/NACRT. Performance status divided patients into two clusters which influenced all predicted outcomes in conjunction with age. CONCLUSION XAI techniques delineate the relationship between clinical factors and OC treatment decisions. These techniques identify advanced age as heavily influencing decisions based on our model with a greater role in patients with specific tumour characteristics. This study methodology provides the means for exploring conscious/subconscious bias and interrogating inconsistencies in team-based decision-making within the era of AI-driven decision support.
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Affiliation(s)
| | - Arya Farahi
- Department of Statistics and Data Science, University of Texas at Austin, United States
| | | | - Zehor Belkhatir
- School of Electronics and Computer Science, University of Southampton, UK
| | - Tayyaba Azim
- School of Electronics and Computer Science, University of Southampton, UK
| | - Elvira Perez Vallejos
- School of Computer Science, Horizon Digital Economy Research, University of Nottingham, UK
| | - Zoë Walters
- School of Cancer Sciences, Faculty of Medicine, University of Southampton, UK
| | - Sarvapali Ramchurn
- School of Electronics and Computer Science, University of Southampton, UK
| | - Timothy J Underwood
- School of Cancer Sciences, Faculty of Medicine, University of Southampton, UK. https://twitter.com/TimTheSurgeon
| | - Ganesh Vigneswaran
- School of Cancer Sciences, Faculty of Medicine, University of Southampton, UK. https://twitter.com/ganesh_vignes
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Almansour M, Alfhaid FM. Generative artificial intelligence and the personalization of health professional education: A narrative review. Medicine (Baltimore) 2024; 103:e38955. [PMID: 39093806 PMCID: PMC11296413 DOI: 10.1097/md.0000000000038955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Accepted: 06/26/2024] [Indexed: 08/04/2024] Open
Abstract
This narrative review examined the intersection of generative artificial intelligence (GAI) and the personalization of health professional education (PHE). This review aims to the elucidate the current condition of GAI technologies and their particular uses in the field of PHE. Data were extracted and analyzed from studies focusing on the demographics and professional development preferences of healthcare workers, the competencies required for personalized precision medicine, and the current and potential applications of artificial intelligence (AI) in PHE. The review also addressed the ethical implications of AI implementation in this context. Findings indicated a gender-balanced healthcare workforce with a predisposition toward continuous professional development and digital tool utilization. A need for a comprehensive educational framework was identified to include a spectrum of skills crucial for precision medicine, emphasizing the importance of patient involvement and bioethics. AI was found to enhance educational experiences and research in PHE, with an increasing trend in AI applications, particularly in surgical education since 2018. Ethical challenges associated with AI integration in PHE were highlighted, with an emphasis on the need for ethical design and diverse development teams. Core concepts in AI research were established, with a spotlight on emerging areas such as data science and learning analytics. The application of AI in PHE was recognized for its current benefits and potential for future advancements, with a call for ethical vigilance. GAI holds significant promise for personalizing PHE, with an identified need for ethical frameworks and diverse developer teams to address bias and equity in educational AI applications.
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Affiliation(s)
- Mohammed Almansour
- Department of Medical Education, College of Medicine, King Saud University, Riyadh, Saudi Arabia
| | - Fahad Mohammad Alfhaid
- Department of family and community medicine, College of medicine, Majmaah University, Majmaah, Saudi Arabia
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Zhui L, Fenghe L, Xuehu W, Qining F, Wei R. Ethical Considerations and Fundamental Principles of Large Language Models in Medical Education: Viewpoint. J Med Internet Res 2024; 26:e60083. [PMID: 38971715 PMCID: PMC11327620 DOI: 10.2196/60083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Accepted: 07/06/2024] [Indexed: 07/08/2024] Open
Abstract
This viewpoint article first explores the ethical challenges associated with the future application of large language models (LLMs) in the context of medical education. These challenges include not only ethical concerns related to the development of LLMs, such as artificial intelligence (AI) hallucinations, information bias, privacy and data risks, and deficiencies in terms of transparency and interpretability but also issues concerning the application of LLMs, including deficiencies in emotional intelligence, educational inequities, problems with academic integrity, and questions of responsibility and copyright ownership. This paper then analyzes existing AI-related legal and ethical frameworks and highlights their limitations with regard to the application of LLMs in the context of medical education. To ensure that LLMs are integrated in a responsible and safe manner, the authors recommend the development of a unified ethical framework that is specifically tailored for LLMs in this field. This framework should be based on 8 fundamental principles: quality control and supervision mechanisms; privacy and data protection; transparency and interpretability; fairness and equal treatment; academic integrity and moral norms; accountability and traceability; protection and respect for intellectual property; and the promotion of educational research and innovation. The authors further discuss specific measures that can be taken to implement these principles, thereby laying a solid foundation for the development of a comprehensive and actionable ethical framework. Such a unified ethical framework based on these 8 fundamental principles can provide clear guidance and support for the application of LLMs in the context of medical education. This approach can help establish a balance between technological advancement and ethical safeguards, thereby ensuring that medical education can progress without compromising the principles of fairness, justice, or patient safety and establishing a more equitable, safer, and more efficient environment for medical education.
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Affiliation(s)
- Li Zhui
- Department of Vascular Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Li Fenghe
- Department of Vascular Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Wang Xuehu
- Department of Vascular Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Fu Qining
- Department of Vascular Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Ren Wei
- Department of Vascular Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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Sorte SR, Rawekar A, Rathod SB. Understanding AI in Healthcare: Perspectives of Future Healthcare Professionals. Cureus 2024; 16:e66285. [PMID: 39238760 PMCID: PMC11376319 DOI: 10.7759/cureus.66285] [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: 06/04/2024] [Accepted: 08/06/2024] [Indexed: 09/07/2024] Open
Abstract
Introduction The current medical curriculum lacks comprehensive artificial intelligence (AI)-focused training, potentially impacting future healthcare delivery. This study addresses the critical gap in AI training within medical education, particularly in India, by assessing medical students' awareness, perceptions, readiness, confidence, and ethical considerations regarding AI in healthcare. Our findings underscore the necessity of integrating AI competencies into medical education to prepare future healthcare professionals for an AI-driven landscape. Method After obtaining ethics approval, we conducted a cross-sectional study on Bachelor of Medicine and Bachelor of Surgery (MBBS) students from the 2019-2023 batch. An exploratory survey using a validated questionnaire was employed to obtain medical students' current understanding and awareness of artificial intelligence (AI) in healthcare, perceptions, readiness, confidence, and ethical considerations in utilizing AI technologies in clinical practice. Results The survey received 217 responses from 2019-2023 MBBS students. We found a mean percentage of awareness score of 44.74%, a mean percentage perception score of 68.96%, a mean percentage readiness score of 91.32%, a mean percentage confidence score of 58.48%, and a mean percentage ethics importance score of 69.27%. Males had higher awareness, confidence, and readiness scores. Conversely, females scored slightly higher in perception and the importance of ethics consideration, although not statistically significant. Junior batches outperform senior batches in perception, confidence, and readiness scores; in contrast, the awareness and ethics importance scores do not show significant differences between the two groups. Conclusion Our study indicates a generally positive outlook toward AI's potential to enhance healthcare delivery and patient outcomes. The study suggests a strong inclination toward further education and practical training focused on AI in healthcare, considering a solid recognition of the significance of ethical implications related to AI in healthcare. These findings highlight the importance of fostering AI literacy within medical education curricula and underscore the necessity for ongoing evaluation and adaptation to ensure that future healthcare professionals are equipped to navigate the complexities of AI in healthcare delivery while upholding ethical standards.
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Affiliation(s)
- Smita R Sorte
- Physiology, All India Institute of Medical Sciences, Nagpur, Nagpur, IND
| | - Alka Rawekar
- Physiology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Medical Sciences (Deemed to be University), Wardha, IND
| | - Sachin B Rathod
- Physiology, All India Institute of Medical Sciences, Nagpur, Nagpur, IND
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Tolentino R, Baradaran A, Gore G, Pluye P, Abbasgholizadeh-Rahimi S. Curriculum Frameworks and Educational Programs in AI for Medical Students, Residents, and Practicing Physicians: Scoping Review. JMIR MEDICAL EDUCATION 2024; 10:e54793. [PMID: 39023999 PMCID: PMC11294785 DOI: 10.2196/54793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 03/26/2024] [Accepted: 04/29/2024] [Indexed: 07/20/2024]
Abstract
BACKGROUND The successful integration of artificial intelligence (AI) into clinical practice is contingent upon physicians' comprehension of AI principles and its applications. Therefore, it is essential for medical education curricula to incorporate AI topics and concepts, providing future physicians with the foundational knowledge and skills needed. However, there is a knowledge gap in the current understanding and availability of structured AI curriculum frameworks tailored for medical education, which serve as vital guides for instructing and facilitating the learning process. OBJECTIVE The overall aim of this study is to synthesize knowledge from the literature on curriculum frameworks and current educational programs that focus on the teaching and learning of AI for medical students, residents, and practicing physicians. METHODS We followed a validated framework and the Joanna Briggs Institute methodological guidance for scoping reviews. An information specialist performed a comprehensive search from 2000 to May 2023 in the following bibliographic databases: MEDLINE (Ovid), Embase (Ovid), CENTRAL (Cochrane Library), CINAHL (EBSCOhost), and Scopus as well as the gray literature. Papers were limited to English and French languages. This review included papers that describe curriculum frameworks for teaching and learning AI in medicine, irrespective of country. All types of papers and study designs were included, except conference abstracts and protocols. Two reviewers independently screened the titles and abstracts, read the full texts, and extracted data using a validated data extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. We adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist for reporting the results. RESULTS Of the 5104 papers screened, 21 papers relevant to our eligibility criteria were identified. In total, 90% (19/21) of the papers altogether described 30 current or previously offered educational programs, and 10% (2/21) of the papers described elements of a curriculum framework. One framework describes a general approach to integrating AI curricula throughout the medical learning continuum and another describes a core curriculum for AI in ophthalmology. No papers described a theory, pedagogy, or framework that guided the educational programs. CONCLUSIONS This review synthesizes recent advancements in AI curriculum frameworks and educational programs within the domain of medical education. To build on this foundation, future researchers are encouraged to engage in a multidisciplinary approach to curriculum redesign. In addition, it is encouraged to initiate dialogues on the integration of AI into medical curriculum planning and to investigate the development, deployment, and appraisal of these innovative educational programs. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-10.11124/JBIES-22-00374.
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Affiliation(s)
- Raymond Tolentino
- Department of Family Medicine, McGill University, Montreal, QC, Canada
| | - Ashkan Baradaran
- Department of Family Medicine, McGill University, Montreal, QC, Canada
| | - Genevieve Gore
- Schulich Library of Physical Sciences, Life Sciences, and Engineering, McGill University, Montreal, QC, Canada
| | - Pierre Pluye
- Department of Family Medicine, McGill University, Montreal, QC, Canada
| | - Samira Abbasgholizadeh-Rahimi
- Department of Family Medicine, McGill University, Montreal, QC, Canada
- Mila - Quebec AI Institute, Montreal, QC, Canada
- Lady Davis Institute for Medical Research, Herzl Family Practice Centre, Jewish General Hospital, Montreal, QC, Canada
- Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, QC, Canada
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8
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Kommuru S, Adekunle F, Niño S, Arefin S, Thalvayapati SP, Kuriakose D, Ahmadi Y, Vinyak S, Nazir Z. Role of Artificial Intelligence in the Diagnosis of Gastroesophageal Reflux Disease. Cureus 2024; 16:e62206. [PMID: 39006681 PMCID: PMC11240074 DOI: 10.7759/cureus.62206] [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: 06/09/2024] [Indexed: 07/16/2024] Open
Abstract
Gastroesophageal reflux disease (GERD) is a disorder that usually presents with heartburn. GERD is diagnosed clinically, but most patients are misdiagnosed due to atypical presentations. The increased use of artificial intelligence (AI) in healthcare has provided multiple ways of diagnosing and treating patients accurately. In this review, multiple studies in which AI models were used to diagnose GERD are discussed. According to the studies, using AI models helped to diagnose GERD in patients accurately. AI, although considered one of the most potent emerging aspects of medicine with its accuracy in patient diagnosis, presents limitations of its own, which explains why healthcare providers may hesitate to use AI in patient care. The challenges and limitations should be addressed before AI is fully incorporated into the healthcare system.
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Affiliation(s)
- Sravani Kommuru
- Medical School, Dr. Pinnamaneni Siddhartha Institute of Medical Sciences & Research Foundation, Vijayawada, IND
| | - Faith Adekunle
- Medical School, American University of the Carribbean, Cupecoy, SXM
| | - Santiago Niño
- Surgery, Colegio Mayor de Nuestra Señora del Rosario, Bogota, COL
| | - Shamsul Arefin
- Internal Medicine, Nottingham University Hospitals NHS Trust, Nottingham, GBR
| | | | - Dona Kuriakose
- Internal Medicine, Petre Shotadze Tbilisi Medical Academy, Tbilisi, GEO
| | - Yasmin Ahmadi
- Medical School, Royal College of Surgeons in Ireland - Medical University of Bahrain, Busaiteen, BHR
| | - Suprada Vinyak
- Internal Medicine, Wellmont Health System/Norton Community Hospital, Norton, USA
| | - Zahra Nazir
- Internal Medicine, Combined Military Hospital, Quetta, PAK
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Meşe İ, Altıntaş Taşlıçay C, Kuzan BN, Kuzan TY, Sivrioğlu AK. Educating the next generation of radiologists: a comparative report of ChatGPT and e-learning resources. Diagn Interv Radiol 2024; 30:163-174. [PMID: 38145370 PMCID: PMC11095068 DOI: 10.4274/dir.2023.232496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 11/29/2023] [Indexed: 12/26/2023]
Abstract
Rapid technological advances have transformed medical education, particularly in radiology, which depends on advanced imaging and visual data. Traditional electronic learning (e-learning) platforms have long served as a cornerstone in radiology education, offering rich visual content, interactive sessions, and peer-reviewed materials. They excel in teaching intricate concepts and techniques that necessitate visual aids, such as image interpretation and procedural demonstrations. However, Chat Generative Pre-Trained Transformer (ChatGPT), an artificial intelligence (AI)-powered language model, has made its mark in radiology education. It can generate learning assessments, create lesson plans, act as a round-the-clock virtual tutor, enhance critical thinking, translate materials for broader accessibility, summarize vast amounts of information, and provide real-time feedback for any subject, including radiology. Concerns have arisen regarding ChatGPT's data accuracy, currency, and potential biases, especially in specialized fields such as radiology. However, the quality, accessibility, and currency of e-learning content can also be imperfect. To enhance the educational journey for radiology residents, the integration of ChatGPT with expert-curated e-learning resources is imperative for ensuring accuracy and reliability and addressing ethical concerns. While AI is unlikely to entirely supplant traditional radiology study methods, the synergistic combination of AI with traditional e-learning can create a holistic educational experience.
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Affiliation(s)
- İsmail Meşe
- University of Health Sciences Türkiye, Erenköy Mental Health and Neurology Training and Research Hospital, Clinic of Radiology, İstanbul, Türkiye
| | | | - Beyza Nur Kuzan
- Kartal Dr. Lütfi Kırdar City Hospital, Clinic of Radiology, İstanbul, Türkiye
| | - Taha Yusuf Kuzan
- Sancaktepe Şehit Prof. Dr. İlhan Varank Training and Research Hospital, Clinic of Radiology, İstanbul, Türkiye
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Esmaeilzadeh P. Challenges and strategies for wide-scale artificial intelligence (AI) deployment in healthcare practices: A perspective for healthcare organizations. Artif Intell Med 2024; 151:102861. [PMID: 38555850 DOI: 10.1016/j.artmed.2024.102861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 03/19/2024] [Accepted: 03/25/2024] [Indexed: 04/02/2024]
Abstract
Healthcare organizations have realized that Artificial intelligence (AI) can provide a competitive edge through personalized patient experiences, improved patient outcomes, early diagnosis, augmented clinician capabilities, enhanced operational efficiencies, or improved medical service accessibility. However, deploying AI-driven tools in the healthcare ecosystem could be challenging. This paper categorizes AI applications in healthcare and comprehensively examines the challenges associated with deploying AI in medical practices at scale. As AI continues to make strides in healthcare, its integration presents various challenges, including production timelines, trust generation, privacy concerns, algorithmic biases, and data scarcity. The paper highlights that flawed business models and wrong workflows in healthcare practices cannot be rectified merely by deploying AI-driven tools. Healthcare organizations should re-evaluate root problems such as misaligned financial incentives (e.g., fee-for-service models), dysfunctional medical workflows (e.g., high rates of patient readmissions), poor care coordination between different providers, fragmented electronic health records systems, and inadequate patient education and engagement models in tandem with AI adoption. This study also explores the need for a cultural shift in viewing AI not as a threat but as an enabler that can enhance healthcare delivery and create new employment opportunities while emphasizing the importance of addressing underlying operational issues. The necessity of investments beyond finance is discussed, emphasizing the importance of human capital, continuous learning, and a supportive environment for AI integration. The paper also highlights the crucial role of clear regulations in building trust, ensuring safety, and guiding the ethical use of AI, calling for coherent frameworks addressing transparency, model accuracy, data quality control, liability, and ethics. Furthermore, this paper underscores the importance of advancing AI literacy within academia to prepare future healthcare professionals for an AI-driven landscape. Through careful navigation and proactive measures addressing these challenges, the healthcare community can harness AI's transformative power responsibly and effectively, revolutionizing healthcare delivery and patient care. The paper concludes with a vision and strategic suggestions for the future of healthcare with AI, emphasizing thoughtful, responsible, and innovative engagement as the pathway to realizing its full potential to unlock immense benefits for healthcare organizations, physicians, nurses, and patients while proactively mitigating risks.
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Affiliation(s)
- Pouyan Esmaeilzadeh
- Department of Information Systems and Business Analytics, College of Business, Florida International University (FIU), Modesto A. Maidique Campus, 11200 S.W. 8th St, RB 261B, Miami, FL 33199, United States.
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11
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Doherty G, McLaughlin L, Hughes C, McConnell J, Bond R, McFadden S. A scoping review of educational programmes on artificial intelligence (AI) available to medical imaging staff. Radiography (Lond) 2024; 30:474-482. [PMID: 38217933 DOI: 10.1016/j.radi.2023.12.019] [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: 10/26/2023] [Revised: 12/29/2023] [Accepted: 12/30/2023] [Indexed: 01/15/2024]
Abstract
INTRODUCTION Medical imaging is arguably the most technologically advanced field in healthcare, encompassing a range of technologies which continually evolve as computing power and human knowledge expand. Artificial Intelligence (AI) is the next frontier which medical imaging is pioneering. The rapid development and implementation of AI has the potential to revolutionise healthcare, however, to do so, staff must be competent and confident in its application, hence AI readiness is an important precursor to AI adoption. Research to ascertain the best way to deliver this AI-enabled healthcare training is in its infancy. The aim of this scoping review is to compare existing studies which investigate and evaluate the efficacy of AI educational interventions for medical imaging staff. METHODS Following the creation of a search strategy and keyword searches, screening was conducted to determine study eligibility. This consisted of a title and abstract scan, then subsequently a full-text review. Articles were included if they were empirical studies wherein an educational intervention on AI for medical imaging staff was created, delivered, and evaluated. RESULTS Of the initial 1309 records returned, n = 5 (∼0.4 %) of studies met the eligibility criteria of the review. The curricula and delivery in each of the five studies shared similar aims and a 'flipped classroom' delivery was the most utilised method. However, the depth of content covered in the curricula of each varied and measured outcomes differed greatly. CONCLUSION The findings of this review will provide insights into the evaluation of existing AI educational interventions, which will be valuable when planning AI education for healthcare staff. IMPLICATIONS FOR PRACTICE This review highlights the need for standardised and comprehensive AI training programs for imaging staff.
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Affiliation(s)
- G Doherty
- Ulster University, School of Health Sciences, Faculty of Life and Health Sciences, Shore Road, Newtownabbey, Northern Ireland, United Kingdom.
| | - L McLaughlin
- Ulster University, School of Health Sciences, Faculty of Life and Health Sciences, Shore Road, Newtownabbey, Northern Ireland, United Kingdom
| | - C Hughes
- Ulster University, School of Health Sciences, Faculty of Life and Health Sciences, Shore Road, Newtownabbey, Northern Ireland, United Kingdom
| | - J McConnell
- Leeds Teaching Hospitals NHS Trust, United Kingdom
| | - R Bond
- Ulster University, School of Computing, Faculty of Computing, Engineering and the Built Environment, Shore Road, Newtownabbey, Northern Ireland, United Kingdom
| | - S McFadden
- Ulster University, School of Health Sciences, Faculty of Life and Health Sciences, Shore Road, Newtownabbey, Northern Ireland, United Kingdom
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12
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Scandiffio J, Zhang M, Karsan I, Charow R, Anderson M, Salhia M, Wiljer D. The role of mentoring and coaching of healthcare professionals for digital technology adoption and implementation: A scoping review. Digit Health 2024; 10:20552076241238075. [PMID: 38465291 PMCID: PMC10924557 DOI: 10.1177/20552076241238075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 02/22/2024] [Indexed: 03/12/2024] Open
Abstract
Objective Mentoring and coaching practices have supported the career and skill development of healthcare professionals (HCPs); however, their role in digital technology adoption and implementation for HCPs is unknown. The objective of this scoping review was to summarize information on healthcare education programs that have integrated mentoring or coaching as a key component. Methods The search strategy and keyword searches were developed by the project team and a research librarian. A two-stage screening process consisting of a title/abstract scan and a full-text review was conducted by two independent reviewers to determine study eligibility. Articles were included if they: (1) discussed the mentoring and/or coaching of HCPs on digital technology, including artificial intelligence, (2) described a population of HCPs at any stage of their career, and (3) were published in English. Results A total of 9473 unique citations were screened, identifying 19 eligible articles. 11 articles described mentoring and/or coaching programs for digital technology adoption, while eigth described mentoring and/or coaching for digital technology implementation. Program participants represented a diverse range of industries (i.e., clinical, academic, education, business, and information technology). Digital technologies taught within programs included electronic health records (EHRs), ultrasound imaging, digital health informatics, and computer skills. Conclusions This review provided a summary of the role of mentoring and/or coaching practices within digital technology education for HCPs. Future training initiatives for HCPs should consider appropriate resources, program design, mentor-learner relationship, security concerns and setting clear expectations for program participants. Future research could explore mentor/coach characteristics that would facilitate successful skill transfer.
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Affiliation(s)
| | | | | | - Rebecca Charow
- University Health Network, Toronto, ON, Canada
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | | | - Mohammad Salhia
- Michener Institute of Education at University Health Network, Toronto, ON, Canada
| | - David Wiljer
- University Health Network, Toronto, ON, Canada
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Faculty of Medicine, University of Toronto, Toronto, ON, Canada
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13
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Abdel Aziz MH, Rowe C, Southwood R, Nogid A, Berman S, Gustafson K. A scoping review of artificial intelligence within pharmacy education. AMERICAN JOURNAL OF PHARMACEUTICAL EDUCATION 2024; 88:100615. [PMID: 37914030 DOI: 10.1016/j.ajpe.2023.100615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 10/19/2023] [Accepted: 10/25/2023] [Indexed: 11/03/2023]
Abstract
OBJECTIVES This scoping review aimed to summarize the available literature on the use of artificial intelligence (AI) in pharmacy education and identify gaps where additional research is needed. FINDINGS Seven studies specifically addressing the use of AI in pharmacy education were identified. Of these 7 studies, 5 focused on AI use in the context of teaching and learning, 1 on the prediction of academic performance for admissions, and the final study focused on using AI text generation to elucidate the benefits and limitations of ChatGPT use in pharmacy education. SUMMARY There are currently a limited number of available publications that describe AI use in pharmacy education. Several challenges exist regarding the use of AI in pharmacy education, including the need for faculty expertise and time, limited generalizability of tools, limited outcomes data, and several legal and ethical concerns. As AI use increases and implementation becomes more standardized, opportunities will be created for the inclusion of AI in pharmacy education.
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Affiliation(s)
- May H Abdel Aziz
- University of Texas at Tyler, Ben and Maytee Fisch College of Pharmacy, Department of Pharmaceutical Sciences and Health Outcomes, Tyler, TX, USA.
| | - Casey Rowe
- University of Florida College of Pharmacy, Department of Pharmacotherapy and Translational Research, Orlando, FL, USA
| | - Robin Southwood
- University of Georgia, College of Pharmacy, Department of Clinical and Administrative Pharmacy, Athens, GA, USA
| | - Anna Nogid
- Fairleigh Dickinson University, School of Pharmacy and Health Sciences, Department of Pharmacy Practice, Florham Park, NJ, USA
| | - Sarah Berman
- University of the Incarnate Word, Feik School of Pharmacy, Department of Pharmacy Practice, San Antonio, TX, USA
| | - Kyle Gustafson
- Northeast Ohio Medical University, Department of Pharmacy Practice, Rootstown, OH, USA
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14
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Alanzi T, Alanazi F, Mashhour B, Altalhi R, Alghamdi A, Al Shubbar M, Alamro S, Alshammari M, Almusmili L, Alanazi L, Alzahrani S, Alalouni R, Alanzi N, Alsharifa A. Surveying Hematologists' Perceptions and Readiness to Embrace Artificial Intelligence in Diagnosis and Treatment Decision-Making. Cureus 2023; 15:e49462. [PMID: 38152821 PMCID: PMC10751460 DOI: 10.7759/cureus.49462] [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: 11/23/2023] [Indexed: 12/29/2023] Open
Abstract
AIM This study aims to explore the critical dimension of assessing the perceptions and readiness of hematologists to embrace artificial intelligence (AI) technologies in their diagnostic and treatment decision-making processes. METHODS This study used a cross-sectional design for collecting data related to the perceptions and readiness of hematologists using a validated online questionnaire-based survey. Both hematologists (MD) and postgraduate MD students in hematology were included in the study. A total of 188 participants, including 35 hematologists (MD) and 153 MD hematology students, completed the survey. RESULTS Major challenges include "AI's level of autonomy" and "the complexity in the field of medicine." Major barriers and risks identified include "lack of trust," "management's level of understanding," "dehumanization of healthcare," and "reduction in physicians' skills." Statistically significant differences in perceptions of benefits including resources (p=0.0326, p<0.05) and knowledge (p=0.0262, p<0.05) were observed between genders. Older physicians were observed to be more concerned about the use of AI compared to younger physicians (p<0.05). CONCLUSION While AI use in hematology diagnosis and treatment decision-making is positively perceived, issues such as lack of trust, transparency, regulations, and poor AI awareness can affect the adoption of AI.
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Affiliation(s)
- Turki Alanzi
- Department of Health Information Management and Technology, College of Public Health, Imam Abdulrahman Bin Faisal University, Dammam, SAU
| | - Fehaid Alanazi
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Jouf University, Sakakah, SAU
| | | | | | | | | | - Saud Alamro
- College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, SAU
| | | | | | - Lena Alanazi
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Jouf University, Sakakah, SAU
| | | | - Raneem Alalouni
- College of Public Health, Imam Abdulrahman Bin Faisal University, Dammam, SAU
| | - Nouf Alanzi
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Jouf University, Sakakah, SAU
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15
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Pupic N, Ghaffari-Zadeh A, Hu R, Singla R, Darras K, Karwowska A, Forster BB. An evidence-based approach to artificial intelligence education for medical students: A systematic review. PLOS DIGITAL HEALTH 2023; 2:e0000255. [PMID: 38011214 PMCID: PMC10681314 DOI: 10.1371/journal.pdig.0000255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 09/14/2023] [Indexed: 11/29/2023]
Abstract
The exponential growth of artificial intelligence (AI) in the last two decades has been recognized by many as an opportunity to improve the quality of patient care. However, medical education systems have been slow to adapt to the age of AI, resulting in a paucity of AI-specific education in medical schools. The purpose of this systematic review is to evaluate the current evidence-based recommendations for the inclusion of an AI education curriculum in undergraduate medicine. Six databases were searched from inception to April 23, 2022 for cross sectional and cohort studies of fair quality or higher on the Newcastle-Ottawa scale, systematic, scoping, and integrative reviews, randomized controlled trials, and Delphi studies about AI education in undergraduate medical programs. The search yielded 991 results, of which 27 met all the criteria and seven more were included using reference mining. Despite the limitations of a high degree of heterogeneity among the study types and a lack of follow-up studies evaluating the impacts of current AI strategies, a thematic analysis of the key AI principles identified six themes needed for a successful implementation of AI in medical school curricula. These themes include ethics, theory and application, communication, collaboration, quality improvement, and perception and attitude. The themes of ethics, theory and application, and communication were further divided into subthemes, including patient-centric and data-centric ethics; knowledge for practice and knowledge for communication; and communication for clinical decision-making, communication for implementation, and communication for knowledge dissemination. Based on the survey studies, medical professionals and students, who generally have a low baseline knowledge of AI, have been strong supporters of adding formal AI education into medical curricula, suggesting more research needs to be done to push this agenda forward.
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Affiliation(s)
- Nikola Pupic
- Faculty of Medicine, University of British Columbia, British Columbia, Vancouver, Canada
| | - Aryan Ghaffari-Zadeh
- Faculty of Medicine, University of British Columbia, British Columbia, Vancouver, Canada
| | - Ricky Hu
- Faculty of Medicine, Queen's University, Ontario, Kingston, Canada
| | - Rohit Singla
- Faculty of Medicine, University of British Columbia, British Columbia, Vancouver, Canada
| | - Kathryn Darras
- Faculty of Medicine, Department of Radiology, University of British Columbia, British Columbia, Vancouver, Canada
| | - Anna Karwowska
- Association of Faculties of Medicine of Canada, Ontario, Ottawa, Canada
- Faculty of Medicine, Department of Pediatrics, University of Ottawa, Ontario, Ottawa, Canada
| | - Bruce B Forster
- Faculty of Medicine, Department of Radiology, University of British Columbia, British Columbia, Vancouver, Canada
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16
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Yelne S, Chaudhary M, Dod K, Sayyad A, Sharma R. Harnessing the Power of AI: A Comprehensive Review of Its Impact and Challenges in Nursing Science and Healthcare. Cureus 2023; 15:e49252. [PMID: 38143615 PMCID: PMC10744168 DOI: 10.7759/cureus.49252] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Accepted: 11/22/2023] [Indexed: 12/26/2023] Open
Abstract
This comprehensive review delves into the impact and challenges of Artificial Intelligence (AI) in nursing science and healthcare. AI has already demonstrated its transformative potential in these fields, with applications spanning from personalized care and diagnostic accuracy to predictive analytics and telemedicine. However, the integration of AI has its complexities, including concerns related to data privacy, ethical considerations, and biases in algorithms and datasets. The future of healthcare appears promising, with AI poised to advance diagnostics, treatment, and healthcare practices. Nevertheless, it is crucial to remember that AI should complement, not replace, healthcare professionals, preserving the essential human element of care. To maximize AI's potential in healthcare, interdisciplinary collaboration, ethical guidelines, and the protection of patient rights are essential. This review concludes with a call to action, emphasizing the need for ongoing research and collective efforts to ensure that AI contributes to improved healthcare outcomes while upholding the highest standards of ethics and patient-centered care.
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Affiliation(s)
- Seema Yelne
- Nursing, Shalinitai Meghe College of Nursing, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Minakshi Chaudhary
- Nursing, Shalinitai Meghe College of Nursing, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Karishma Dod
- Nursing, Shalinitai Meghe College of Nursing, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Akhtaribano Sayyad
- Nursing, Shalinitai Meghe College of Nursing, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Ranjana Sharma
- Nursing, Shalinitai Meghe College of Nursing, Datta Meghe Institute of Higher Education and Research, Wardha, IND
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17
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Hesso I, Kayyali R, Dolton DR, Joo K, Zacharias L, Charalambous A, Lavdaniti M, Stalika E, Ajami T, Acampa W, Boban J, Nabhani-Gebara S. Cancer care at the time of the fourth industrial revolution: an insight to healthcare professionals' perspectives on cancer care and artificial intelligence. Radiat Oncol 2023; 18:167. [PMID: 37814325 PMCID: PMC10561443 DOI: 10.1186/s13014-023-02351-z] [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: 02/22/2023] [Accepted: 09/13/2023] [Indexed: 10/11/2023] Open
Abstract
BACKGROUND The integration of Artificial Intelligence (AI) technology in cancer care has gained unprecedented global attention over the past few decades. This has impacted the way that cancer care is practiced and delivered across settings. The purpose of this study was to explore the perspectives and experiences of healthcare professionals (HCPs) on cancer treatment and the need for AI. This study is a part of the INCISIVE European Union H2020 project's development of user requirements, which aims to fully explore the potential of AI-based cancer imaging technologies. METHODS A mixed-methods research design was employed. HCPs participating in cancer care in the UK, Greece, Italy, Spain, Cyprus, and Serbia were first surveyed anonymously online. Twenty-seven HCPs then participated in semi-structured interviews. Appropriate statistical method was adopted to report the survey results by using SPSS. The interviews were audio recorded, verbatim transcribed, and then thematically analysed supported by NVIVO. RESULTS The survey drew responses from 95 HCPs. The occurrence of diagnostic delay was reported by 56% (n = 28/50) for breast cancer, 64% (n = 27/42) for lung cancer, 76% (n = 34/45) for colorectal cancer and 42% (n = 16/38) for prostate cancer. A proportion of participants reported the occurrence of false positives in the accuracy of the current imaging techniques used: 64% (n = 32/50) reported this for breast cancer, 60% (n = 25/42) for lung cancer, 51% (n = 23/45) for colorectal cancer and 45% (n = 17/38) for prostate cancer. All participants agreed that the use of technology would enhance the care pathway for cancer patients. Despite the positive perspectives toward AI, certain limitations were also recorded. The majority (73%) of respondents (n = 69/95) reported they had never utilised technology in the care pathway which necessitates the need for education and training in the qualitative finding; compared to 27% (n = 26/95) who had and were still using it. Most, 89% of respondents (n = 85/95) said they would be opened to providing AI-based services in the future to improve medical imaging for cancer care. Interviews with HCPs revealed lack of widespread preparedness for AI in oncology, several barriers to introducing AI, and a need for education and training. Provision of AI training, increasing public awareness of AI, using evidence-based technology, and developing AI based interventions that will not replace HCPs were some of the recommendations. CONCLUSION HCPs reported favourable opinions of AI-based cancer imaging technologies and noted a number of care pathway concerns where AI can be useful. For the future design and execution of the INCISIVE project and other comparable AI-based projects, the characteristics and recommendations offered in the current research can serve as a reference.
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Affiliation(s)
- Iman Hesso
- School of Life Sciences, Pharmacy and Chemistry, Kingston University London, Penrhyn Road, Kingston Upon Thames, KT1 2EE, UK
| | - Reem Kayyali
- School of Life Sciences, Pharmacy and Chemistry, Kingston University London, Penrhyn Road, Kingston Upon Thames, KT1 2EE, UK
| | - Debbie-Rose Dolton
- School of Life Sciences, Pharmacy and Chemistry, Kingston University London, Penrhyn Road, Kingston Upon Thames, KT1 2EE, UK
| | - Kwanyoung Joo
- School of Life Sciences, Pharmacy and Chemistry, Kingston University London, Penrhyn Road, Kingston Upon Thames, KT1 2EE, UK
| | - Lithin Zacharias
- School of Life Sciences, Pharmacy and Chemistry, Kingston University London, Penrhyn Road, Kingston Upon Thames, KT1 2EE, UK
| | - Andreas Charalambous
- Cyprus University of Technology, Limassol, Cyprus
- University of Turku, Turku, Finland
| | | | - Evangelia Stalika
- International Hellenic University, Thessaloniki, Greece
- Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Tarek Ajami
- Urology Department, Hospital Clinic de Barcelona, Barcelona, Spain
| | - Wanda Acampa
- Department of Advanced Biomedical Science, University of Naples Federico II, Naples, Italy
| | - Jasmina Boban
- Department of Radiology, Faculty of Medicine, University of Novi Sad, Hajduk Veljkova 3, 21000, Novi Sad, Serbia
- Diagnostic Imaging Center, Oncology Institute of Vojvodine, Put Dr Goldmana 4, 21204, Sremska Kamenica, Serbia
| | - Shereen Nabhani-Gebara
- School of Life Sciences, Pharmacy and Chemistry, Kingston University London, Penrhyn Road, Kingston Upon Thames, KT1 2EE, UK.
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18
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Olisova K, Sao CH, Lussier EC, Sung CY, Wang PH, Yeh CC, Chang TY. Ultrasonographic cervical length screening at 20-24 weeks of gestation in twin pregnancies for prediction of spontaneous preterm birth: A 10-year Taiwanese cohort. PLoS One 2023; 18:e0292533. [PMID: 37797073 PMCID: PMC10553282 DOI: 10.1371/journal.pone.0292533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 09/21/2023] [Indexed: 10/07/2023] Open
Abstract
BACKGROUND Shortened cervical length is one of the primary predictors for spontaneous preterm deliveries in twin pregnancies. However, there is lack of consensus regarding cut-off values. Recent evidence highlights that established cut-offs for cervical length screening might not always apply across different populations. This study aims to present the distribution of cervical length in Taiwanese twin pregnancies and to assess its predictive value for spontaneous preterm birth during mid-trimester screening. MATERIALS AND METHODS This retrospective analysis of cervical length screening in Taiwan evaluated 469 twin pregnancies between 20-24 weeks of gestation. Outcome data were obtained directly from the medical records of the delivery hospital. The study explored the predictive value of cervical length screening for spontaneous preterm birth and the characteristics of cervical length distribution in Taiwanese twin pregnancies. RESULTS The average gestational age at screening was 22.7 weeks. Cervical length values displayed a non-normal distribution (p-value <0.001). The median, 5th and 95th centiles were 37.5 mm 25.1 mm, and 47.9 mm, respectively. Various cut-off values were assessed using different methods, yielding positive [negative] likelihood ratios for spontaneous preterm births between 32-37 weeks of gestational age (GA) (1.3-30.1 and [0.51-0.92]) and for very preterm births between 28-32 weeks GA (5.6-51.1 and [0.45-0.64]). CONCLUSIONS The findings from our analysis of Taiwanese twin pregnancies uphold the moderate predictive potential of cervical length screening, consistent with prior investigations. The presented likelihood ratios for predicting preterm birth at different gestational ages equip clinicians with valuable tools to enhance their diagnostic rationale and resource utilization. By fine-tuning screening parameters according to the spontaneous preterm birth prevalence and clinical priorities of the particular population, healthcare providers can enhance patient care. Our data implies that a cervical length below 20 mm might provide an optimal balance between minimizing false negatives and managing false positives when predicting spontaneous preterm birth.
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Affiliation(s)
- Ksenia Olisova
- Department of Medical Research, Taiji Clinic, Taipei, Taiwan
| | - Chih-Hsuan Sao
- Department of Obstetrics and Gynecology, Taipei Tzu Chi Hospital, Taipei, Taiwan
- Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Eric C. Lussier
- Department of Medical Research, Taiji Clinic, Taipei, Taiwan
| | - Chan-Yu Sung
- Department of Medical Research, Taiji Clinic, Taipei, Taiwan
| | - Peng-Hui Wang
- Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Obstetrics and Gynecology, Taipei Veterans General Hospital, Taipei, Taiwan
- Department of Obstetrics and Gynecology, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Female Cancer Foundation, Taipei, Taiwan
- Department of Medical Research, China Medical University Hospital, Taichung, Taiwan
| | - Chang-Ching Yeh
- Department of Obstetrics and Gynecology, Taipei Veterans General Hospital, Taipei, Taiwan
- Department of Obstetrics and Gynecology, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Tung-Yao Chang
- Department of Medical Research, Taiji Clinic, Taipei, Taiwan
- Department of Fetal Medicine, Taiji Clinic, Taipei, Taiwan
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19
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Busch F, Adams LC, Bressem KK. Biomedical Ethical Aspects Towards the Implementation of Artificial Intelligence in Medical Education. MEDICAL SCIENCE EDUCATOR 2023; 33:1007-1012. [PMID: 37546190 PMCID: PMC10403458 DOI: 10.1007/s40670-023-01815-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 05/31/2023] [Indexed: 08/08/2023]
Abstract
The increasing use of artificial intelligence (AI) in medicine is associated with new ethical challenges and responsibilities. However, special considerations and concerns should be addressed when integrating AI applications into medical education, where healthcare, AI, and education ethics collide. This commentary explores the biomedical ethical responsibilities of medical institutions in incorporating AI applications into medical education by identifying potential concerns and limitations, with the goal of implementing applicable recommendations. The recommendations presented are intended to assist in developing institutional guidelines for the ethical use of AI for medical educators and students.
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Affiliation(s)
- Felix Busch
- Department of Radiology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
- Department of Anesthesiology, Division of Operative Intensive Care Medicine, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
| | - Lisa C. Adams
- Department of Radiology, Stanford University School of Medicine, Stanford, CA USA
| | - Keno K. Bressem
- Department of Radiology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany
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Eckert C. Beyond the Spreadsheet: Data Management for Physicians in the Era of Big Data. Surg Clin North Am 2023; 103:335-346. [PMID: 36948722 DOI: 10.1016/j.suc.2022.11.007] [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] [Indexed: 03/24/2023]
Abstract
Big Data is transforming health care. Characteristics of Big Data require data management strategies to effectively use, analyze, and apply the data. Clinicians are not typically learned in the fundamentals of these strategies which may cause a divide between collected data and data used. This article introduces the fundamentals of Big Data management and encourages clinicians to work with their information technology partners to further understand these processes and to identify opportunities for collaboration.
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Affiliation(s)
- Carly Eckert
- Department of Epidemiology, University of Washington, 1023 Cleland Drive, Chapel Hill, NC 27517, USA.
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21
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Chen Y, Clayton EW, Novak LL, Anders S, Malin B. Human-Centered Design to Address Biases in Artificial Intelligence. J Med Internet Res 2023; 25:e43251. [PMID: 36961506 PMCID: PMC10132017 DOI: 10.2196/43251] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 01/30/2023] [Accepted: 02/28/2023] [Indexed: 03/25/2023] Open
Abstract
The potential of artificial intelligence (AI) to reduce health care disparities and inequities is recognized, but it can also exacerbate these issues if not implemented in an equitable manner. This perspective identifies potential biases in each stage of the AI life cycle, including data collection, annotation, machine learning model development, evaluation, deployment, operationalization, monitoring, and feedback integration. To mitigate these biases, we suggest involving a diverse group of stakeholders, using human-centered AI principles. Human-centered AI can help ensure that AI systems are designed and used in a way that benefits patients and society, which can reduce health disparities and inequities. By recognizing and addressing biases at each stage of the AI life cycle, AI can achieve its potential in health care.
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Affiliation(s)
- You Chen
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Computer Science, Vanderbilt University, Nashville, TN, United States
| | - Ellen Wright Clayton
- Law School, Vanderbilt University, Nashville, TN, United States
- Center for Biomedical Ethics and Society, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Laurie Lovett Novak
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Shilo Anders
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Computer Science, Vanderbilt University, Nashville, TN, United States
- Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Bradley Malin
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Computer Science, Vanderbilt University, Nashville, TN, United States
- Center for Biomedical Ethics and Society, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, United States
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22
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Wagner G, Raymond L, Paré G. Understanding Prospective Physicians' Intention to Use Artificial Intelligence in Their Future Medical Practice: Configurational Analysis. JMIR MEDICAL EDUCATION 2023; 9:e45631. [PMID: 36947121 PMCID: PMC10131981 DOI: 10.2196/45631] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 02/20/2023] [Accepted: 02/24/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Prospective physicians are expected to find artificial intelligence (AI) to be a key technology in their future practice. This transformative change has caught the attention of scientists, educators, and policy makers alike, with substantive efforts dedicated to the selection and delivery of AI topics and competencies in the medical curriculum. Less is known about the behavioral perspective or the necessary and sufficient preconditions for medical students' intention to use AI in the first place. OBJECTIVE Our study focused on medical students' knowledge, experience, attitude, and beliefs related to AI and aimed to understand whether they are necessary conditions and form sufficient configurations of conditions associated with behavioral intentions to use AI in their future medical practice. METHODS We administered a 2-staged questionnaire operationalizing the variables of interest (ie, knowledge, experience, attitude, and beliefs related to AI, as well as intention to use AI) and recorded 184 responses at t0 (February 2020, before the COVID-19 pandemic) and 138 responses at t1 (January 2021, during the COVID-19 pandemic). Following established guidelines, we applied necessary condition analysis and fuzzy-set qualitative comparative analysis to analyze the data. RESULTS Findings from the fuzzy-set qualitative comparative analysis show that the intention to use AI is only observed when students have a strong belief in the role of AI (individually necessary condition); certain AI profiles, that is, combinations of knowledge and experience, attitudes and beliefs, and academic level and gender, are always associated with high intentions to use AI (equifinal and sufficient configurations); and profiles associated with nonhigh intentions cannot be inferred from profiles associated with high intentions (causal asymmetry). CONCLUSIONS Our work contributes to prior knowledge by showing that a strong belief in the role of AI in the future of medical professions is a necessary condition for behavioral intentions to use AI. Moreover, we suggest that the preparation of medical students should go beyond teaching AI competencies and that educators need to account for the different AI profiles associated with high or nonhigh intentions to adopt AI.
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Affiliation(s)
- Gerit Wagner
- Faculty Information Systems and Applied Computer Sciences, University of Bamberg, Bamberg, Germany
| | - Louis Raymond
- Université du Québec à Trois-Rivières, Trois-Rivières, QC, Canada
| | - Guy Paré
- Department of Information Technologies, École des Hautes Études commerciales Montréal, Montréal, QC, Canada
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Jeyakumar T, Younus S, Zhang M, Clare M, Charow R, Karsan I, Dhalla A, Al-Mouaswas D, Scandiffio J, Aling J, Salhia M, Lalani N, Overholt S, Wiljer D. Preparing for an Artificial Intelligence-Enabled Future: Patient Perspectives on Engagement and Health Care Professional Training for Adopting Artificial Intelligence Technologies in Health Care Settings. JMIR AI 2023; 2:e40973. [PMID: 38875561 PMCID: PMC11041489 DOI: 10.2196/40973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 11/29/2022] [Accepted: 12/29/2022] [Indexed: 06/16/2024]
Abstract
BACKGROUND As new technologies emerge, there is a significant shift in the way care is delivered on a global scale. Artificial intelligence (AI) technologies have been rapidly and inexorably used to optimize patient outcomes, reduce health system costs, improve workflow efficiency, and enhance population health. Despite the widespread adoption of AI technologies, the literature on patient engagement and their perspectives on how AI will affect clinical care is scarce. Minimal patient engagement can limit the optimization of these novel technologies and contribute to suboptimal use in care settings. OBJECTIVE We aimed to explore patients' views on what skills they believe health care professionals should have in preparation for this AI-enabled future and how we can better engage patients when adopting and deploying AI technologies in health care settings. METHODS Semistructured interviews were conducted from August 2020 to December 2021 with 12 individuals who were a patient in any Canadian health care setting. Interviews were conducted until thematic saturation occurred. A thematic analysis approach outlined by Braun and Clarke was used to inductively analyze the data and identify overarching themes. RESULTS Among the 12 patients interviewed, 8 (67%) were from urban settings and 4 (33%) were from rural settings. A majority of the participants were very comfortable with technology (n=6, 50%) and somewhat familiar with AI (n=7, 58%). In total, 3 themes emerged: cultivating patients' trust, fostering patient engagement, and establishing data governance and validation of AI technologies. CONCLUSIONS With the rapid surge of AI solutions, there is a critical need to understand patient values in advancing the quality of care and contributing to an equitable health system. Our study demonstrated that health care professionals play a synergetic role in the future of AI and digital technologies. Patient engagement is vital in addressing underlying health inequities and fostering an optimal care experience. Future research is warranted to understand and capture the diverse perspectives of patients with various racial, ethnic, and socioeconomic backgrounds.
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Affiliation(s)
| | | | | | - Megan Clare
- Michener Institute of Education, University Health Network, Toronto, ON, Canada
| | - Rebecca Charow
- University Health Network, Toronto, ON, Canada
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Inaara Karsan
- University Health Network, Toronto, ON, Canada
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | | | - Dalia Al-Mouaswas
- Michener Institute of Education, University Health Network, Toronto, ON, Canada
| | | | - Justin Aling
- Patient Partner Program, University Health Network, Toronto, ON, Canada
| | - Mohammad Salhia
- Michener Institute of Education, University Health Network, Toronto, ON, Canada
| | | | - Scott Overholt
- Patient Partner Program, University Health Network, Toronto, ON, Canada
| | - David Wiljer
- University Health Network, Toronto, ON, Canada
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Office of Education, Centre for Addiction and Mental Health, Toronto, ON, Canada
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Tsopra R, Peiffer-Smadja N, Charlier C, Campeotto F, Lemogne C, Ruszniewski P, Vivien B, Burgun A. Putting undergraduate medical students in AI-CDSS designers' shoes: An innovative teaching method to develop digital health critical thinking. Int J Med Inform 2023; 171:104980. [PMID: 36681042 DOI: 10.1016/j.ijmedinf.2022.104980] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 12/26/2022] [Accepted: 12/27/2022] [Indexed: 12/31/2022]
Abstract
INTRODUCTION Digital health programs are urgently needed to accelerate the adoption of Artificial Intelligence and Clinical Decision Support Systems (AI-CDSS) in clinical settings. However, such programs are still lacking for undergraduate medical students, and new approaches are required to prepare them for the arrival of new and unknown technologies. At University Paris Cité medical school, we designed an innovative program to develop the digital health critical thinking of undergraduate medical students that consisted of putting medical students in AI-CDSS designers' shoes. METHODS We followed the six steps of Kern's approach for curriculum development: identification of needs, definition of objectives, design of an educational strategy, implementation, development of an assessment and design of program evaluation. RESULTS A stand-alone and elective AI-CDSS program was implemented for fourth-year medical students. Each session was designed from an AI-CDSS designer viewpoint, with theoretical and practical teaching and brainstorming time on a project that consisted of designing an AI-CDSS in small groups. From 2021 to 2022, 15 students were enrolled: they rated the program 4.4/5, and 80% recommended it. Seventy-four percent considered that they had acquired new skills useful for clinical practice, and 66% felt more confident with technologies. The AI-CDSS program aroused great enthusiasm and strong engagement of students: 8 designed an AI-CDSS and wrote two scientific 5-page articles presented at the Medical Informatics Europe conference; 4 students were involved in a CDSS research project; 2 students asked for a hospital internship in digital health; and 1 decided to pursue PhD training. DISCUSSION Putting students in AI-CDSS designers' shoes seemed to be a fruitful and innovative strategy to develop digital health skills and critical thinking toward AI technologies. We expect that such programs could help future doctors work in rapidly evolving digitalized environments and position themselves as key leaders in digital health.
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Affiliation(s)
- Rosy Tsopra
- Université Paris Cité, UFR de Médecine, Digital Health Program, Paris, France; Université Paris Cité, Sorbonne Université, Inserm, Centre de Recherche des Cordeliers, F-75006 Paris, France; Inria, HeKA, PariSanté Campus Paris, France; Department of Medical Informatics, AP-HP, Hôpital Européen Georges-Pompidou, F-75015 Paris, France
| | - Nathan Peiffer-Smadja
- Université Paris Cité, UFR de Médecine, Paris, France; Université Paris Cité, INSERM, IAME, F-75018 Paris, France; Infectious Diseases Department, Bichat-Claude Bernard Hospital, AP-HP, F-75018 Paris, France
| | - Caroline Charlier
- Université Paris Cité, UFR de Médecine, Paris, France; Cochin University Hospital, Division of Infectious Diseases and Tropical Medicine, AP-HP, Paris, France; Institut Pasteur, National Reference Center and WHO Collaborating Center Listeria, Paris, France; Institut Pasteur, Inserm U1117, Biology of Infection Unit, Paris, France
| | - Florence Campeotto
- Université Paris Cité, UFR de Médecine, Paris, France; Régulation Régionale Pédiatrique, SAMU de Paris, AP-HP, Hôpital Necker - Enfants Malades, Paris, France; Gastro-entérologie pédiatrique, AP-HP, Hôpital Necker - Enfants Malades, Paris, France; Faculté de Pharmacie, Université Paris Cité, Inserm UMR S1139, Paris, France
| | - Cédric Lemogne
- Université Paris Cité, UFR de Médecine, Paris, France; Université Paris Cité, INSERM U1266, Institut de Psychiatrie et Neurosciences de Paris, F-75014 Paris, France; Service de Psychiatrie de l'adulte, AP-HP, Hôpital Hôtel-Dieu, F-75004 Paris, France
| | - Philippe Ruszniewski
- Université Paris Cité, UFR de Médecine, Paris, France; Université de Paris, Centre of Research on Inflammation, INSERM U1149, Paris, France; Service de gastro-entérologie et pancréatologie, Hôpital Beaujon AP-HP, Paris, France
| | - Benoît Vivien
- Université Paris Cité, UFR de Médecine, Paris, France; Régulation Régionale Pédiatrique, SAMU de Paris, AP-HP, Hôpital Necker - Enfants Malades, Paris, France
| | - Anita Burgun
- Université Paris Cité, UFR de Médecine, Digital Health Program, Paris, France; Université Paris Cité, Sorbonne Université, Inserm, Centre de Recherche des Cordeliers, F-75006 Paris, France; Inria, HeKA, PariSanté Campus Paris, France; Department of Medical Informatics, AP-HP, Hôpital Européen Georges-Pompidou, F-75015 Paris, France
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khan B, Fatima H, Qureshi A, Kumar S, Hanan A, Hussain J, Abdullah S. Drawbacks of Artificial Intelligence and Their Potential Solutions in the Healthcare Sector. BIOMEDICAL MATERIALS & DEVICES (NEW YORK, N.Y.) 2023; 1:1-8. [PMID: 36785697 PMCID: PMC9908503 DOI: 10.1007/s44174-023-00063-2] [Citation(s) in RCA: 57] [Impact Index Per Article: 57.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 01/19/2023] [Indexed: 02/10/2023]
Abstract
Artificial intelligence (AI) has the potential to make substantial progress toward the goal of making healthcare more personalized, predictive, preventative, and interactive. We believe AI will continue its present path and ultimately become a mature and effective tool for the healthcare sector. Besides this AI-based systems raise concerns regarding data security and privacy. Because health records are important and vulnerable, hackers often target them during data breaches. The absence of standard guidelines for the moral use of AI and ML in healthcare has only served to worsen the situation. There is debate about how far artificial intelligence (AI) may be utilized ethically in healthcare settings since there are no universal guidelines for its use. Therefore, maintaining the confidentiality of medical records is crucial. This study enlightens the possible drawbacks of AI in the implementation of healthcare sector and their solutions to overcome these situations. Graphical Abstract
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Affiliation(s)
- Bangul khan
- Hong Kong Centre for Cerebro-Caradiovasular Health Engineering (COCHE), Shatin, Hong Kong
- Riphah International University, Lahore, Pakistan
| | - Hajira Fatima
- Mehran University of Engineering and Technology, Jamshoro, Pakistan
| | | | | | - Abdul Hanan
- Mehran University of Engineering and Technology, Jamshoro, Pakistan
| | | | - Saad Abdullah
- Riphah International University, Lahore, Pakistan
- Mälardalen University, Västerås, Sweden
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Artificial intelligence education for radiographers, an evaluation of a UK postgraduate educational intervention using participatory action research: a pilot study. Insights Imaging 2023; 14:25. [PMID: 36735172 PMCID: PMC9897152 DOI: 10.1186/s13244-023-01372-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 01/15/2023] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI)-enabled applications are increasingly being used in providing healthcare services, such as medical imaging support. Sufficient and appropriate education for medical imaging professionals is required for successful AI adoption. Although, currently, there are AI training programmes for radiologists, formal AI education for radiographers is lacking. Therefore, this study aimed to evaluate and discuss a postgraduate-level module on AI developed in the UK for radiographers. METHODOLOGY A participatory action research methodology was applied, with participants recruited from the first cohort of students enrolled in this module and faculty members. Data were collected using online, semi-structured, individual interviews and focus group discussions. Textual data were processed using data-driven thematic analysis. RESULTS Seven students and six faculty members participated in this evaluation. Results can be summarised in the following four themes: a. participants' professional and educational backgrounds influenced their experiences, b. participants found the learning experience meaningful concerning module design, organisation, and pedagogical approaches, c. some module design and delivery aspects were identified as barriers to learning, and d. participants suggested how the ideal AI course could look like based on their experiences. CONCLUSIONS The findings of our work show that an AI module can assist educators/academics in developing similar AI education provisions for radiographers and other medical imaging and radiation sciences professionals. A blended learning delivery format, combined with customisable and contextualised content, using an interprofessional faculty approach is recommended for future similar courses.
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Vulpoi RA, Luca M, Ciobanu A, Olteanu A, Barboi OB, Drug VL. Artificial Intelligence in Digestive Endoscopy—Where Are We and Where Are We Going? Diagnostics (Basel) 2022; 12:diagnostics12040927. [PMID: 35453975 PMCID: PMC9029251 DOI: 10.3390/diagnostics12040927] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 03/30/2022] [Accepted: 04/06/2022] [Indexed: 02/04/2023] Open
Abstract
Artificial intelligence, a computer-based concept that tries to mimic human thinking, is slowly becoming part of the endoscopy lab. It has developed considerably since the first attempt at developing an automated medical diagnostic tool, today being adopted in almost all medical fields, digestive endoscopy included. The detection rate of preneoplastic lesions (i.e., polyps) during colonoscopy may be increased with artificial intelligence assistance. It has also proven useful in detecting signs of ulcerative colitis activity. In upper digestive endoscopy, deep learning models may prove to be useful in the diagnosis and management of upper digestive tract diseases, such as gastroesophageal reflux disease, Barrett’s esophagus, and gastric cancer. As is the case with all new medical devices, there are challenges in the implementation in daily medical practice. The regulatory, economic, organizational culture, and language barriers between humans and machines are a few of them. Even so, many devices have been approved for use by their respective regulators. Future studies are currently striving to develop deep learning models that can replicate a growing amount of human brain activity. In conclusion, artificial intelligence may become an indispensable tool in digestive endoscopy.
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Affiliation(s)
- Radu-Alexandru Vulpoi
- Institute of Gastroenterology and Hepatology, Saint Spiridon Hospital, “Grigore T. Popa” University of Medicine and Pharmacy, 700111 Iași, Romania; (R.-A.V.); (A.O.); (V.L.D.)
| | - Mihaela Luca
- Institute of Computer Science, Romanian Academy—Iași Branch, 700481 Iași, Romania; (M.L.); (A.C.)
| | - Adrian Ciobanu
- Institute of Computer Science, Romanian Academy—Iași Branch, 700481 Iași, Romania; (M.L.); (A.C.)
| | - Andrei Olteanu
- Institute of Gastroenterology and Hepatology, Saint Spiridon Hospital, “Grigore T. Popa” University of Medicine and Pharmacy, 700111 Iași, Romania; (R.-A.V.); (A.O.); (V.L.D.)
| | - Oana-Bogdana Barboi
- Institute of Gastroenterology and Hepatology, Saint Spiridon Hospital, “Grigore T. Popa” University of Medicine and Pharmacy, 700111 Iași, Romania; (R.-A.V.); (A.O.); (V.L.D.)
- Correspondence: ; Tel.: +40-74-345-5012
| | - Vasile Liviu Drug
- Institute of Gastroenterology and Hepatology, Saint Spiridon Hospital, “Grigore T. Popa” University of Medicine and Pharmacy, 700111 Iași, Romania; (R.-A.V.); (A.O.); (V.L.D.)
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Wang JJ, Singh RK, Miselis HH, Stapleton SN. Technology Literacy in Undergraduate Medical Education: Review and Survey of the US Medical School Innovation and Technology Programs. JMIR MEDICAL EDUCATION 2022; 8:e32183. [PMID: 35357319 PMCID: PMC9015763 DOI: 10.2196/32183] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2021] [Revised: 01/14/2022] [Accepted: 02/22/2022] [Indexed: 05/06/2023]
Abstract
BACKGROUND Modern innovations, like machine learning, genomics, and digital health, are being integrated into medical practice at a rapid pace. Physicians in training receive little exposure to the implications, drawbacks, and methodologies of upcoming technologies prior to their deployment. As a result, there is an increasing need for the incorporation of innovation and technology (I&T) training, starting in medical school. OBJECTIVE We aimed to identify and describe curricular and extracurricular opportunities for innovation in medical technology in US undergraduate medical education to highlight challenges and develop insights for future directions of program development. METHODS A review of publicly available I&T program information on the official websites of US allopathic medical schools was conducted in June 2020. Programs were categorized by structure and implementation. The geographic distribution of these categories across US regions was analyzed. A survey was administered to school-affiliated student organizations with a focus on I&T and publicly available contact information. The data collected included the founding year, thematic focus, target audience, activities offered, and participant turnout rate. RESULTS A total of 103 I&T opportunities at 69 distinct Liaison Committee on Medical Education-accredited medical schools were identified and characterized into the following six categories: (1) integrative 4-year curricula, (2) facilitated doctor of medicine/master of science dual degree programs in a related field, (3) interdisciplinary collaborations, (4) areas of concentration, (5) preclinical electives, and (6) student-run clubs. The presence of interdisciplinary collaboration is significantly associated with the presence of student-led initiatives (P=.001). "Starting and running a business in healthcare" and "medical devices" were the most popular thematic focuses of student-led I&T groups, representing 87% (13/15) and 80% (12/15) of respondents, respectively. "Career pathways exploration for students" was the only type of activity that was significantly associated with a high event turnout rate of >26 students per event (P=.03). CONCLUSIONS Existing school-led and student-driven opportunities in medical I&T indicate growing national interest and reflect challenges in implementation. The greater visibility of opportunities, collaboration among schools, and development of a centralized network can be considered to better prepare students for the changing landscape of medical practice.
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Affiliation(s)
- Judy Jiaqi Wang
- Department of Medicine, Boston University School of Medicine, Boston, MA, United States
| | - Rishabh K Singh
- Department of Medicine, Boston University School of Medicine, Boston, MA, United States
| | - Heather Hough Miselis
- Department of Family Medicine, Boston University School of Medicine, Boston Medical Center, Boston, MA, United States
| | - Stephanie Nicole Stapleton
- Department of Emergency Medicine, Boston University School of Medicine, Boston Medical Center, Boston, MA, United States
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