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Chowdhury AT, Salam A, Naznine M, Abdalla D, Erdman L, Chowdhury MEH, Abbas TO. Artificial Intelligence Tools in Pediatric Urology: A Comprehensive Review of Recent Advances. Diagnostics (Basel) 2024; 14:2059. [PMID: 39335738 PMCID: PMC11431426 DOI: 10.3390/diagnostics14182059] [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: 08/12/2024] [Revised: 09/07/2024] [Accepted: 09/09/2024] [Indexed: 09/30/2024] Open
Abstract
Artificial intelligence (AI) is providing novel answers to long-standing clinical problems, and it is quickly changing pediatric urology. This thorough analysis focuses on current developments in AI technologies that improve pediatric urology diagnosis, treatment planning, and surgery results. Deep learning algorithms help detect problems with previously unheard-of precision in disorders including hydronephrosis, pyeloplasty, and vesicoureteral reflux, where AI-powered prediction models have demonstrated promising outcomes in boosting diagnostic accuracy. AI-enhanced image processing methods have significantly improved the quality and interpretation of medical images. Examples of these methods are deep-learning-based segmentation and contrast limited adaptive histogram equalization (CLAHE). These methods guarantee higher precision in the identification and classification of pediatric urological disorders, and AI-driven ground truth construction approaches aid in the standardization of and improvement in training data, resulting in more resilient and consistent segmentation models. AI is being used for surgical support as well. AI-assisted navigation devices help with difficult operations like pyeloplasty by decreasing complications and increasing surgical accuracy. AI also helps with long-term patient monitoring, predictive analytics, and customized treatment strategies, all of which improve results for younger patients. However, there are practical, ethical, and legal issues with AI integration in pediatric urology that need to be carefully navigated. To close knowledge gaps, more investigation is required, especially in the areas of AI-driven surgical methods and standardized ground truth datasets for pediatric radiologic image segmentation. In the end, AI has the potential to completely transform pediatric urology by enhancing patient care, increasing the effectiveness of treatments, and spurring more advancements in this exciting area.
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Affiliation(s)
- Adiba Tabassum Chowdhury
- Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka 1000, Bangladesh
| | - Abdus Salam
- Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshashi 6204, Bangladesh
| | - Mansura Naznine
- Department of Computer Science & Engineering, Rajshahi University of Engineering & Technology, Rajshashi 6204, Bangladesh
| | - Da'ad Abdalla
- Faculty of Medicine, University of Khartoum, Khartoum 11115, Sudan
| | - Lauren Erdman
- James M. Anderson Center for Health Systems Excellence, Cincinnati, OH 45255, USA
- School of Medicine, University of Cincinnati, Cincinnati, OH 45267, USA
| | | | - Tariq O Abbas
- Pediatric Urology Section, Sidra Medicine, Doha 26999, Qatar
- College of Medicine, Qatar University, Doha 2713, Qatar
- Weil Cornell Medicine Qatar, Doha 24144, Qatar
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2
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Schouten JS, Kalden MACM, van Twist E, Reiss IKM, Gommers DAMPJ, van Genderen ME, Taal HR. From bytes to bedside: a systematic review on the use and readiness of artificial intelligence in the neonatal and pediatric intensive care unit. Intensive Care Med 2024:10.1007/s00134-024-07629-8. [PMID: 39264415 DOI: 10.1007/s00134-024-07629-8] [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: 06/14/2024] [Accepted: 08/10/2024] [Indexed: 09/13/2024]
Abstract
PURPOSE Despite its promise to enhance patient outcomes and support clinical decision making, clinical use of artificial intelligence (AI) models at the bedside remains limited. Translation of advancements in AI research into tangible clinical benefits is necessary to improve neonatal and pediatric care for critically ill patients. This systematic review seeks to assess the maturity of AI models in neonatal and pediatric intensive care unit (NICU and PICU) treatment, and their risk of bias and objectives. METHODS We conducted a systematic search in Medline ALL, Embase, Web of Science Core Collection, Cochrane Central Register of Controlled Trials, and Google Scholar. Studies using AI models during NICU or PICU stay were eligible for inclusion. Study design, objective, dataset size, level of validation, risk of bias, and technological readiness of the models were extracted. RESULTS Out of the 1257 identified studies 262 were included. The majority of studies was conducted in the NICU (66%) and most had a high risk of bias (77%). An insufficient sample size was the main cause for this high risk of bias. No studies were identified that integrated an AI model in routine clinical practice and the majority of the studies remained in the prototyping and model development phase. CONCLUSION The majority of AI models remain within the testing and prototyping phase and have a high risk of bias. Bridging the gap between designing and clinical implementation of AI models is needed to warrant safe and trustworthy AI models. Specific guidelines and approaches can help improve clinical outcome with usage of AI.
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Affiliation(s)
- Janno S Schouten
- Department of Neonatal and Pediatric Intensive Care, Division of Neonatology, Erasmus MC, University Medical Center-Sophia Children's Hospital, Rotterdam, The Netherlands
- Datahub, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Melissa A C M Kalden
- Department of Neonatal and Pediatric Intensive Care, Division of Neonatology, Erasmus MC, University Medical Center-Sophia Children's Hospital, Rotterdam, The Netherlands
- Datahub, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
- Department of Information and (Medical) Technology, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Eris van Twist
- Department of Neonatal and Pediatric Intensive Care, Division of Pediatrics, Erasmus MC, University Medical Center-Sophia Children's Hospital, Rotterdam, The Netherlands
| | - Irwin K M Reiss
- Department of Neonatal and Pediatric Intensive Care, Division of Neonatology, Erasmus MC, University Medical Center-Sophia Children's Hospital, Rotterdam, The Netherlands
| | - Diederik A M P J Gommers
- Datahub, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
- Department of Adult Intensive Care, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Michel E van Genderen
- Datahub, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
- Department of Adult Intensive Care, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - H Rob Taal
- Department of Neonatal and Pediatric Intensive Care, Division of Neonatology, Erasmus MC, University Medical Center-Sophia Children's Hospital, Rotterdam, The Netherlands.
- Datahub, Erasmus MC, University Medical Center, Rotterdam, The Netherlands.
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Suresh S, Misra SM. Large Language Models in Pediatric Education: Current Uses and Future Potential. Pediatrics 2024; 154:e2023064683. [PMID: 39108227 DOI: 10.1542/peds.2023-064683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 06/03/2024] [Accepted: 06/05/2024] [Indexed: 09/02/2024] Open
Abstract
Generative artificial intelligence, especially large language models (LLMs), has the potential to affect every level of pediatric education and training. Demonstrating speed and adaptability, LLMs can aid educators, trainees, and practicing pediatricians with tasks such as enhancing curriculum design through the creation of cases, videos, and assessments; creating individualized study plans and providing real-time feedback for trainees; and supporting pediatricians by enhancing information searches, clinic efficiency, and bedside teaching. LLMs can refine patient education materials to address patients' specific needs. The current versions of LLMs sometimes provide "hallucinations" or incorrect information but are likely to improve. There are ethical concerns related to bias in the output of LLMs, the potential for plagiarism, and the possibility of the overuse of an online tool at the expense of in-person learning. The potential benefits of LLMs in pediatric education can outweigh the potential risks if employed judiciously by content experts who conscientiously review the output. All stakeholders must firmly establish rules and policies to provide rigorous guidance and assure the safe and proper use of this transformative tool in the care of the child. In this article, we outline the history, current uses, and challenges with generative artificial intelligence in pediatrics education. We provide examples of LLM output, including performance on a pediatrics examination guide and the creation of patient care instructions. Future directions to establish a safe and appropriate path for the use of LLMs will be discussed.
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Affiliation(s)
- Srinivasan Suresh
- Divisions of Health Informatics & Emergency Medicine, Department of Pediatrics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
- UPMC Children's Hospital of Pittsburgh, Pittsburgh, Pennsylvania
| | - Sanghamitra M Misra
- Division of Academic General Pediatrics, Department of Pediatrics, Baylor College of Medicine, Houston, Texas
- Texas Children's Hospital, Houston, Texas
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Muralidharan V, Schamroth J, Youssef A, Celi LA, Daneshjou R. Applied artificial intelligence for global child health: Addressing biases and barriers. PLOS DIGITAL HEALTH 2024; 3:e0000583. [PMID: 39172772 PMCID: PMC11340888 DOI: 10.1371/journal.pdig.0000583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2024]
Abstract
Given the potential benefits of artificial intelligence and machine learning (AI/ML) within healthcare, it is critical to consider how these technologies can be deployed in pediatric research and practice. Currently, healthcare AI/ML has not yet adapted to the specific technical considerations related to pediatric data nor adequately addressed the specific vulnerabilities of children and young people (CYP) in relation to AI. While the greatest burden of disease in CYP is firmly concentrated in lower and middle-income countries (LMICs), existing applied pediatric AI/ML efforts are concentrated in a small number of high-income countries (HICs). In LMICs, use-cases remain primarily in the proof-of-concept stage. This narrative review identifies a number of intersecting challenges that pose barriers to effective AI/ML for CYP globally and explores the shifts needed to make progress across multiple domains. Child-specific technical considerations throughout the AI/ML lifecycle have been largely overlooked thus far, yet these can be critical to model effectiveness. Governance concerns are paramount, with suitable national and international frameworks and guidance required to enable the safe and responsible deployment of advanced technologies impacting the care of CYP and using their data. An ambitious vision for child health demands that the potential benefits of AI/Ml are realized universally through greater international collaboration, capacity building, strong oversight, and ultimately diffusing the AI/ML locus of power to empower researchers and clinicians globally. In order that AI/ML systems that do not exacerbate inequalities in pediatric care, teams researching and developing these technologies in LMICs must ensure that AI/ML research is inclusive of the needs and concerns of CYP and their caregivers. A broad, interdisciplinary, and human-centered approach to AI/ML is essential for developing tools for healthcare workers delivering care, such that the creation and deployment of ML is grounded in local systems, cultures, and clinical practice. Decisions to invest in developing and testing pediatric AI/ML in resource-constrained settings must always be part of a broader evaluation of the overall needs of a healthcare system, considering the critical building blocks underpinning effective, sustainable, and cost-efficient healthcare delivery for CYP.
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Affiliation(s)
- Vijaytha Muralidharan
- Department of Dermatology, Stanford University, Stanford, California, United States of America
| | - Joel Schamroth
- Faculty of Population Health Sciences, University College London, London, United Kingdom
| | - Alaa Youssef
- Stanford Center for Artificial Intelligence in Medicine and Imaging, Department of Radiology, Stanford University, Stanford, California, United States of America
| | - Leo A. Celi
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Roxana Daneshjou
- Department of Dermatology, Stanford University, Stanford, California, United States of America
- Department of Biomedical Data Science, Stanford University, Stanford, California, United States of America
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5
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Pudjiadi AH, Alatas FS, Faizi M, Rusdi, Sulistijono E, Nency YM, Julia M, Baso AJA, Hartoyo E, Susanah S, Wilar R, Nugroho HW, Indrayady, Lubis BM, Haris S, Suparyatha IBG, Amarassaphira D, Monica E, Ongko L. Integration of Artificial Intelligence in Pediatric Education: Perspectives from Pediatric Medical Educators and Residents. Healthc Inform Res 2024; 30:244-252. [PMID: 39160783 PMCID: PMC11333820 DOI: 10.4258/hir.2024.30.3.244] [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: 07/11/2023] [Revised: 05/11/2024] [Accepted: 07/18/2024] [Indexed: 08/21/2024] Open
Abstract
OBJECTIVES The use of technology has rapidly increased in the past century. Artificial intelligence (AI) and information technology (IT) are now applied in healthcare and medical education. The purpose of this study was to assess the readiness of Indonesian teaching staff and pediatric residents for AI integration into the curriculum. METHODS An anonymous online survey was distributed among teaching staff and pediatric residents from 15 national universities. The questionnaire consisted of two sections: demographic information and questions regarding the use of IT and AI in child health education. Responses were collected using a 5-point Likert scale: strongly disagree, disagree, neutral, agree, and highly agree. RESULTS A total of 728 pediatric residents and 196 teaching staff from 15 national universities participated in the survey. Over half of the respondents were familiar with the terms IT and AI. The majority agreed that IT and AI have simplified the process of learning theories and skills. All participants were in favor of sharing data to facilitate the development of AI and expressed readiness to incorporate IT and AI into their teaching tools. CONCLUSIONS The findings of our study indicate that pediatric residents and teaching staff are ready to implement AI in medical education.
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Affiliation(s)
- Antonius Hocky Pudjiadi
- Department of Child Health, Faculty of Medicine, Universitas Indonesia, Cipto Mangunkusumo Hospital, Jakarta,
Indonesia
| | - Fatima Safira Alatas
- Department of Child Health, Faculty of Medicine, Universitas Indonesia, Cipto Mangunkusumo Hospital, Jakarta,
Indonesia
| | - Muhammad Faizi
- Department of Child Health, Faculty of Medicine, Universitas Airlangga, Surabaya,
Indonesia
| | - Rusdi
- Department of Child Health, Faculty of Medicine, Universitas Andalas, Padang,
Indonesia
| | - Eko Sulistijono
- Department of Child Health, Faculty of Medicine, Universitas Brawijaya, Malang,
Indonesia
| | - Yetty Movieta Nency
- Department of Child Health, Faculty of Medicine, Universitas Diponegoro, Semarang,
Indonesia
| | - Madarina Julia
- Department of Child Health, Faculty of Medicine, Universitas Gadjah Mada, Yogyakarta,
Indonesia
| | | | - Edi Hartoyo
- Department of Child Health, Faculty of Medicine, Universitas Lambung Mangkurat, Banjarmasin,
Indonesia
| | - Susi Susanah
- Department of Child Health, Faculty of Medicine, Universitas Padjadjaran, Sumedang,
Indonesia
| | - Rocky Wilar
- Department of Child Health, Faculty of Medicine, Universitas Sam Ratulangi, Manado,
Indonesia
| | - Hari Wahyu Nugroho
- Department of Child Health, Faculty of Medicine, Universitas Sebelas Maret, Surakarta,
Indonesia
| | - Indrayady
- Department of Child Health, Faculty of Medicine, Universitas Sriwijaya, Palembang,
Indonesia
| | - Bugis Mardina Lubis
- Department of Child Health, Faculty of Medicine, Universitas Sumatera Utara, Medan,
Indonesia
| | - Syafruddin Haris
- Department of Child Health, Faculty of Medicine, Universitas Syiah Kuala, Aceh,
Indonesia
| | | | - Daniar Amarassaphira
- Department of Child Health, Faculty of Medicine, Universitas Indonesia, Cipto Mangunkusumo Hospital, Jakarta,
Indonesia
| | - Ervin Monica
- Department of Child Health, Faculty of Medicine, Universitas Indonesia, Cipto Mangunkusumo Hospital, Jakarta,
Indonesia
| | - Lukito Ongko
- Department of Child Health, Faculty of Medicine, Universitas Indonesia, Cipto Mangunkusumo Hospital, Jakarta,
Indonesia
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Khondker A, Kwong JCC, Rickard M, Erdman L, Kim JK, Ahmad I, Weaver J, Fernandez N, Tasian GE, Kulkarni GS, Lorenzo AJ. Application of STREAM-URO and APPRAISE-AI reporting standards for artificial intelligence studies in pediatric urology: A case example with pediatric hydronephrosis. J Pediatr Urol 2024; 20:455-467. [PMID: 38331659 DOI: 10.1016/j.jpurol.2024.01.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Revised: 12/20/2023] [Accepted: 01/20/2024] [Indexed: 02/10/2024]
Abstract
INTRODUCTION Artificial intelligence (AI) and machine learning (ML) in pediatric urology is gaining increased popularity and credibility. However, the literature lacks standardization in reporting and there are areas for methodological improvement, which incurs difficulty in comparison between studies and may ultimately hurt clinical implementation of these models. The "STandardized REporting of Applications of Machine learning in UROlogy" (STREAM-URO) framework provides methodological instructions to improve transparent reporting in urology and APPRAISE-AI in a critical appraisal tool which provides quantitative measures for the quality of AI studies. The adoption of these will allow urologists and developers to ensure consistency in reporting, improve comparison, develop better models, and hopefully inspire clinical translation. METHODS In this article, we have applied STREAM-URO framework and APPRAISE-AI tool to the pediatric hydronephrosis literature. By doing this, we aim to describe best practices on ML reporting in urology with STREAM-URO and provide readers with a critical appraisal tool for ML quality with APPRAISE-AI. By applying these to the pediatric hydronephrosis literature, we provide some tutorial for other readers to employ these in developing and appraising ML models. We also present itemized recommendations for adequate reporting, and critically appraise the quality of ML in pediatric hydronephrosis insofar. We provide examples of strong reporting and highlight areas for improvement. RESULTS There were 8 ML models applied to pediatric hydronephrosis. The 26-item STREAM-URO framework is provided in Appendix A and 24-item APPRAISE-AI tool is provided in Appendix B. Across the 8 studies, the median compliance with STREAM-URO was 67 % and overall study quality was moderate. The highest scoring APPRAISE-AI domains in pediatric hydronephrosis were clinical relevance and reporting quality, while the worst were methodological conduct, robustness of results, and reproducibility. CONCLUSIONS If properly conducted and reported, ML has the potential to impact the care we provide to patients in pediatric urology. While AI is exciting, the paucity of strong evidence limits our ability to translate models to practice. The first step toward this goal is adequate reporting and ensuring high quality models, and STREAM-URO and APPRAISE-AI can facilitate better reporting and critical appraisal, respectively.
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Affiliation(s)
- Adree Khondker
- Division of Urology, Department of Surgery, The Hospital for Sick Children, Toronto, ON, Canada; Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada; Temerty Center for AI Research and Education in Medicine, University of Toronto, Toronto, ON, Canada
| | - Jethro C C Kwong
- Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada; Temerty Center for AI Research and Education in Medicine, University of Toronto, Toronto, ON, Canada
| | - Mandy Rickard
- Division of Urology, Department of Surgery, The Hospital for Sick Children, Toronto, ON, Canada
| | - Lauren Erdman
- Temerty Center for AI Research and Education in Medicine, University of Toronto, Toronto, ON, Canada; Center for Computational Medicine, Hospital for Sick Children, Toronto, ON, Canada; Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | - Jin K Kim
- Division of Urology, Department of Surgery, The Hospital for Sick Children, Toronto, ON, Canada; Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Ihtisham Ahmad
- Division of Urology, Department of Surgery, The Hospital for Sick Children, Toronto, ON, Canada
| | - John Weaver
- Division of Urology, Cleveland Clinic Children's, Cleveland, OH, USA
| | - Nicolas Fernandez
- Division of Urology, Seattle Children's Hospital, University of Washington, Seattle, WA, USA
| | - Gregory E Tasian
- Division of Urology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Girish S Kulkarni
- Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada; Princess Margaret Cancer Center, University Health Network, University of Toronto, Toronto, ON, Canada
| | - Armando J Lorenzo
- Division of Urology, Department of Surgery, The Hospital for Sick Children, Toronto, ON, Canada; Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada.
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Dimitri P, Savage MO. Artificial intelligence in paediatric endocrinology: conflict or cooperation. J Pediatr Endocrinol Metab 2024; 37:209-221. [PMID: 38183676 DOI: 10.1515/jpem-2023-0554] [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: 12/17/2023] [Accepted: 12/18/2023] [Indexed: 01/08/2024]
Abstract
Artificial intelligence (AI) in medicine is transforming healthcare by automating system tasks, assisting in diagnostics, predicting patient outcomes and personalising patient care, founded on the ability to analyse vast datasets. In paediatric endocrinology, AI has been developed for diabetes, for insulin dose adjustment, detection of hypoglycaemia and retinopathy screening; bone age assessment and thyroid nodule screening; the identification of growth disorders; the diagnosis of precocious puberty; and the use of facial recognition algorithms in conditions such as Cushing syndrome, acromegaly, congenital adrenal hyperplasia and Turner syndrome. AI can also predict those most at risk from childhood obesity by stratifying future interventions to modify lifestyle. AI will facilitate personalised healthcare by integrating data from 'omics' analysis, lifestyle tracking, medical history, laboratory and imaging, therapy response and treatment adherence from multiple sources. As data acquisition and processing becomes fundamental, data privacy and protecting children's health data is crucial. Minimising algorithmic bias generated by AI analysis for rare conditions seen in paediatric endocrinology is an important determinant of AI validity in clinical practice. AI cannot create the patient-doctor relationship or assess the wider holistic determinants of care. Children have individual needs and vulnerabilities and are considered in the context of family relationships and dynamics. Importantly, whilst AI provides value through augmenting efficiency and accuracy, it must not be used to replace clinical skills.
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Affiliation(s)
- Paul Dimitri
- Department of Paediatric Endocrinology, Sheffield Children's NHS Foundation Trust, Sheffield, UK
| | - Martin O Savage
- Centre for Endocrinology, William Harvey Research Institute, Barts and the London School of Medicine & Dentistry, Queen Mary University of London, London, UK
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Nijman J, Zoodsma RS, Koomen E. A Strategy for Artificial Intelligence With Clinical Impact-Eyes on the Prize. JAMA Pediatr 2024; 178:219-220. [PMID: 38285473 DOI: 10.1001/jamapediatrics.2023.6259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/30/2024]
Abstract
This Viewpoint describes a strategy for addressing major challenges in artificial intelligence in pediatrics to maximize clinical impact.
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Affiliation(s)
- Joppe Nijman
- Department of Pediatric Intensive Care, University Medical Center Utrecht, Utrecht, the Netherlands
- Data Science Working Group, European Society of Pediatric and Neonatal Intensive Care, Geneva, Switzerland
| | - Ruben S Zoodsma
- Department of Pediatric Intensive Care, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Erik Koomen
- Department of Pediatric Intensive Care, University Medical Center Utrecht, Utrecht, the Netherlands
- Data Science Working Group, European Society of Pediatric and Neonatal Intensive Care, Geneva, Switzerland
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Muralidharan V, Tran MM, Barrios L, Beams B, Ko JM, Siegel DH, Bailenson J. Best Practices for Research in Virtual and Augmented Reality in Dermatology. J Invest Dermatol 2024; 144:17-23. [PMID: 38105083 DOI: 10.1016/j.jid.2023.10.014] [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: 06/27/2023] [Revised: 10/25/2023] [Accepted: 10/27/2023] [Indexed: 12/19/2023]
Abstract
Virtual reality (VR) and augmented reality (AR) technologies have advanced rapidly in recent years. These cutting-edge technologies provide dermatology researchers, educators, proceduralists, and patients with opportunities in new scientific horizons. VR is a technology that facilitates immersive human experiences by allowing users to connect with various simulated environments through natural head and hand movements, whereas AR supplements a user's perception of their real environment with virtual elements. Despite technological advancements, there is limited literature on the methodological steps for conducting rigorous VR and AR research in dermatology. Effective storyboarding, user-driven design, and interdisciplinary teamwork play a central role in ensuring that VR/AR applications meet the specific needs of dermatology clinical and research teams. We present a step-by-step approach for their design, team composition, and evaluation in dermatology research, medical education, procedures, and habit formation strategies. We also discuss current VR and AR dermatology applications and the importance of ethical and safety considerations in deploying this new technology.
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Affiliation(s)
- Vijaytha Muralidharan
- Department of Dermatology, Stanford University School of Medicine, Redwood City, California, USA.
| | - Megan M Tran
- The Warren Alpert Medical School, Brown University, Providence, Rhode Island, USA
| | - Laurel Barrios
- School of Medicine, University of California Davis, Davis, California
| | - Brian Beams
- Stanford Virtual Human Interaction Lab, Stanford, California, USA
| | - Justin M Ko
- Department of Dermatology, Stanford University School of Medicine, Redwood City, California, USA
| | - Dawn H Siegel
- Department of Dermatology, Stanford University School of Medicine, Redwood City, California, USA
| | - Jeremy Bailenson
- Stanford Virtual Human Interaction Lab, Stanford, California, USA
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Moodley K. Artificial intelligence (AI) or augmented intelligence? How big data and AI are transforming healthcare: Challenges and opportunities. S Afr Med J 2023; 114:22-26. [PMID: 38525617 PMCID: PMC11296939 DOI: 10.7196/samj.2024.v114i1.1631] [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: 10/19/2023] [Indexed: 03/26/2024] Open
Abstract
The sanctity of the doctor-patient relationship is deeply embedded in tradition - the Hippocratic oath, medical ethics, professional codes of conduct, and legislation - all of which are being disrupted by big data and 'artificial' intelligence (AI). The transition from paper-based records to electronic health records, wearables, mobile health applications and mobile phone data has created new opportunities to scale up data collection. Databases of unimaginable magnitude can be harnessed to develop algorithms for AI and to refine machine learning. Complex neural networks now lie at the core of ubiquitous AI systems in healthcare. A transformed healthcare environment enhanced by innovation, robotics, digital technology, and improved diagnostics and therapeutics is plagued by ethical, legal and social challenges. Global guidelines are emerging to ensure governance in AI, but many low- and middle-income countries have yet to develop context- specific frameworks. Legislation must be developed to frame liability and account for negligence due to robotics in the same way human healthcare providers are held accountable. The digital divide between high- and low-income settings is significant and has the potential to exacerbate health inequities globally.
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Affiliation(s)
- K Moodley
- Division of Medical Ethics and Law, Department of Medicine, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.
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