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Rojas-Carabali W, Cifuentes-González C, Gutierrez-Sinisterra L, Heng LY, Tsui E, Gangaputra S, Sadda S, Nguyen QD, Kempen JH, Pavesio CE, Gupta V, Raman R, Miao C, Lee B, de-la-Torre A, Agrawal R. Managing a patient with uveitis in the era of artificial intelligence: Current approaches, emerging trends, and future perspectives. Asia Pac J Ophthalmol (Phila) 2024:100082. [PMID: 39019261 DOI: 10.1016/j.apjo.2024.100082] [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: 05/11/2024] [Revised: 06/30/2024] [Accepted: 07/04/2024] [Indexed: 07/19/2024] Open
Abstract
The integration of artificial intelligence (AI) with healthcare has opened new avenues for diagnosing, treating, and managing medical conditions with remarkable precision. Uveitis, a diverse group of rare eye conditions characterized by inflammation of the uveal tract, exemplifies the complexities in ophthalmology due to its varied causes, clinical presentations, and responses to treatments. Uveitis, if not managed promptly and effectively, can lead to significant visual impairment. However, its management requires specialized knowledge, which is often lacking, particularly in regions with limited access to health services. AI's capabilities in pattern recognition, data analysis, and predictive modelling offer significant potential to revolutionize uveitis management. AI can classify disease etiologies, analyze multimodal imaging data, predict outcomes, and identify new therapeutic targets. However, transforming these AI models into clinical applications and meeting patient expectations involves overcoming challenges like acquiring extensive, annotated datasets, ensuring algorithmic transparency, and validating these models in real-world settings. This review delves into the complexities of uveitis and the current AI landscape, discussing the development, opportunities, and challenges of AI from theoretical models to bedside application. It also examines the epidemiology of uveitis, the global shortage of uveitis specialists, and the disease's socioeconomic impacts, underlining the critical need for AI-driven approaches. Furthermore, it explores the integration of AI in diagnostic imaging and future directions in ophthalmology, aiming to highlight emerging trends that could transform management of a patient with uveitis and suggesting collaborative efforts to enhance AI applications in clinical practice.
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Affiliation(s)
- William Rojas-Carabali
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; Department of Ophthalmology, Tan Tock Seng Hospital, National Healthcare Group Eye Institute, Singapore.
| | - Carlos Cifuentes-González
- Department of Ophthalmology, Tan Tock Seng Hospital, National Healthcare Group Eye Institute, Singapore.
| | - Laura Gutierrez-Sinisterra
- Department of Ophthalmology, Tan Tock Seng Hospital, National Healthcare Group Eye Institute, Singapore.
| | - Lim Yuan Heng
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore.
| | - Edmund Tsui
- Stein Eye Institute, David Geffen of Medicine at UCLA, Los Angeles, CA, USA.
| | - Sapna Gangaputra
- Vanderbilt Eye Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Srinivas Sadda
- Doheny Eye Institute, David Geffen of Medicine at UCLA, Los Angeles, CA, USA.
| | | | - John H Kempen
- Department of Ophthalmology, Massachusetts Eye and Ear/Harvard Medical School; and Schepens Eye Research Institute; Boston, MA, USA; Department of Ophthalmology, Myungsung Medical College/MCM Comprehensive Specialized Hospital, Addis Abeba, Ethiopia; Sight for Souls, Bellevue, WA, USA.
| | | | - Vishali Gupta
- Advanced Eye Centre, Post, graduate Institute of Medical Education and Research (PGIMER), Chandigarh, India.
| | - Rajiv Raman
- Department of Ophthalmology, Sankara Nethralaya, Chennai, India.
| | - Chunyan Miao
- School of Computer Science and Engineering at Nanyang Technological University, Singapore.
| | - Bernett Lee
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore.
| | - Alejandra de-la-Torre
- Neuroscience Research Group (NEUROS), Neurovitae Center for Neuroscience, Institute of Translational Medicine (IMT), Escuela de Medicina y Ciencias de la Salud, Universidad del Rosario, Bogotá, Colombia.
| | - Rupesh Agrawal
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; Department of Ophthalmology, Tan Tock Seng Hospital, National Healthcare Group Eye Institute, Singapore; Singapore Eye Research Institute, Singapore; Duke NUS Medical School, Singapore.
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2
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Napolitano G, Has C, Schwerk A, Yuan JH, Ullrich C. Potential of Artificial Intelligence to Accelerate Drug Development for Rare Diseases. Pharmaceut Med 2024; 38:79-86. [PMID: 38315404 DOI: 10.1007/s40290-023-00504-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/16/2023] [Indexed: 02/07/2024]
Abstract
The growth in breadth and depth of artificial intelligence (AI) applications has been fast, running hand in hand with the increasing amount of digital data available. Here, we comment on the application of AI in the field of drug development, with a strong focus on the specific achievements and challenges posed by rare diseases. Data paucity and high costs make drug development for rare diseases especially hard. AI can enable otherwise inaccessible approaches based on the large-scale integration of heterogeneous datasets and knowledge bases, guided by expert biological understanding. Obstacles still exist for the routine use of AI in the usually conservative pharmaceutical domain, which can easily become disillusioned. It is crucial to acknowledge that AI is a powerful, supportive tool that can assist but not replace human expertise in the various phases and aspects of drug discovery and development.
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Affiliation(s)
| | - Canan Has
- Centogene GmbH, Alboinstraße 36-42, 12103, Berlin, Germany
| | - Anne Schwerk
- Beriln Institute of Health Center for Regenerative Therapies (BCRT), Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Jui-Hung Yuan
- Beriln Institute of Health Center for Regenerative Therapies (BCRT), Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Carsten Ullrich
- Beriln Institute of Health Center for Regenerative Therapies (BCRT), Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Berlin, Germany
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Willingham TB, Stowell J, Collier G, Backus D. Leveraging Emerging Technologies to Expand Accessibility and Improve Precision in Rehabilitation and Exercise for People with Disabilities. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2024; 21:79. [PMID: 38248542 PMCID: PMC10815484 DOI: 10.3390/ijerph21010079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 12/20/2023] [Accepted: 12/28/2023] [Indexed: 01/23/2024]
Abstract
Physical rehabilitation and exercise training have emerged as promising solutions for improving health, restoring function, and preserving quality of life in populations that face disparate health challenges related to disability. Despite the immense potential for rehabilitation and exercise to help people with disabilities live longer, healthier, and more independent lives, people with disabilities can experience physical, psychosocial, environmental, and economic barriers that limit their ability to participate in rehabilitation, exercise, and other physical activities. Together, these barriers contribute to health inequities in people with disabilities, by disproportionately limiting their ability to participate in health-promoting physical activities, relative to people without disabilities. Therefore, there is great need for research and innovation focusing on the development of strategies to expand accessibility and promote participation in rehabilitation and exercise programs for people with disabilities. Here, we discuss how cutting-edge technologies related to telecommunications, wearables, virtual and augmented reality, artificial intelligence, and cloud computing are providing new opportunities to improve accessibility in rehabilitation and exercise for people with disabilities. In addition, we highlight new frontiers in digital health technology and emerging lines of scientific research that will shape the future of precision care strategies for people with disabilities.
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Affiliation(s)
- T. Bradley Willingham
- Shepherd Center, Virginia C. Crawford Research Institute, Atlanta, GA 30309, USA (D.B.)
- Department of Physical Therapy, Georgia State University, Atlanta, GA 30302, USA
| | - Julie Stowell
- Shepherd Center, Virginia C. Crawford Research Institute, Atlanta, GA 30309, USA (D.B.)
- Department of Physical Therapy, Georgia State University, Atlanta, GA 30302, USA
| | - George Collier
- Shepherd Center, Virginia C. Crawford Research Institute, Atlanta, GA 30309, USA (D.B.)
| | - Deborah Backus
- Shepherd Center, Virginia C. Crawford Research Institute, Atlanta, GA 30309, USA (D.B.)
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4
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Gallifant J, Fiske A, Levites Strekalova YA, Osorio-Valencia JS, Parke R, Mwavu R, Martinez N, Gichoya JW, Ghassemi M, Demner-Fushman D, McCoy LG, Celi LA, Pierce R. Peer review of GPT-4 technical report and systems card. PLOS DIGITAL HEALTH 2024; 3:e0000417. [PMID: 38236824 PMCID: PMC10795998 DOI: 10.1371/journal.pdig.0000417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/22/2024]
Abstract
The study provides a comprehensive review of OpenAI's Generative Pre-trained Transformer 4 (GPT-4) technical report, with an emphasis on applications in high-risk settings like healthcare. A diverse team, including experts in artificial intelligence (AI), natural language processing, public health, law, policy, social science, healthcare research, and bioethics, analyzed the report against established peer review guidelines. The GPT-4 report shows a significant commitment to transparent AI research, particularly in creating a systems card for risk assessment and mitigation. However, it reveals limitations such as restricted access to training data, inadequate confidence and uncertainty estimations, and concerns over privacy and intellectual property rights. Key strengths identified include the considerable time and economic investment in transparent AI research and the creation of a comprehensive systems card. On the other hand, the lack of clarity in training processes and data raises concerns about encoded biases and interests in GPT-4. The report also lacks confidence and uncertainty estimations, crucial in high-risk areas like healthcare, and fails to address potential privacy and intellectual property issues. Furthermore, this study emphasizes the need for diverse, global involvement in developing and evaluating large language models (LLMs) to ensure broad societal benefits and mitigate risks. The paper presents recommendations such as improving data transparency, developing accountability frameworks, establishing confidence standards for LLM outputs in high-risk settings, and enhancing industry research review processes. It concludes that while GPT-4's report is a step towards open discussions on LLMs, more extensive interdisciplinary reviews are essential for addressing bias, harm, and risk concerns, especially in high-risk domains. The review aims to expand the understanding of LLMs in general and highlights the need for new reflection forms on how LLMs are reviewed, the data required for effective evaluation, and addressing critical issues like bias and risk.
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Affiliation(s)
- Jack Gallifant
- Department of Critical Care, Guy’s & St Thomas’ NHS Trust, London, United Kingdom
- Massachusetts Institute of Technology, Laboratory for Computational Physiology, Cambridge, Massachusetts, United States of America
| | - Amelia Fiske
- Institute of History and Ethics in Medicine, Department of Clinical Medicine, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Yulia A. Levites Strekalova
- Department of Health Services Research, Management, and Policy, College of Public Health and Health Professions, University of Florida, Gainesville, Florida, United States of America
| | - Juan S. Osorio-Valencia
- A.I. and Innovation Committee, Colombian Radiology Association, Medellin, Colombia
- ScienteLab, Bogota, Colombia
- Be4tech, Medellin, Colombia
| | - Rachael Parke
- Cardiothoracic and Vascular Intensive Care Unit, Auckland City Hospital, Auckland, New Zealand
- School of Nursing, The University of Auckland, Auckland, New Zealand
| | - Rogers Mwavu
- Faculty of Computing and Informatics, Mbarara University of Science and Technology, Mbarara, Uganda
| | - Nicole Martinez
- Center for Biomedical Ethics, Stanford University, Stanford, California, United States of America
| | - Judy Wawira Gichoya
- Department of Radiology, Emory University School of Medicine, Atlanta, Georgia, United States of America
| | - Marzyeh Ghassemi
- Massachusetts Institute of Technology, Electrical Engineering and Computer Science (EECS), Cambridge, Massachusetts, United States of America
| | - Dina Demner-Fushman
- National Library of Medicine, NIH, HHS, Bethesda, Maryland, United States of America
| | - Liam G. McCoy
- Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, Canada
| | - Leo Anthony Celi
- Massachusetts Institute of Technology, Laboratory for Computational Physiology, 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
| | - Robin Pierce
- The Law School, Faculty of Humanities, Arts, and Social Sciences, University of Exeter, Exeter, United Kingdom
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Zhang J, Morley J, Gallifant J, Oddy C, Teo JT, Ashrafian H, Delaney B, Darzi A. Mapping and evaluating national data flows: transparency, privacy, and guiding infrastructural transformation. Lancet Digit Health 2023; 5:e737-e748. [PMID: 37775190 DOI: 10.1016/s2589-7500(23)00157-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 06/07/2023] [Accepted: 08/02/2023] [Indexed: 10/01/2023]
Abstract
The importance of big health data is recognised worldwide. Most UK National Health Service (NHS) care interactions are recorded in electronic health records, resulting in an unmatched potential for population-level datasets. However, policy reviews have highlighted challenges from a complex data-sharing landscape relating to transparency, privacy, and analysis capabilities. In response, we used public information sources to map all electronic patient data flows across England, from providers to more than 460 subsequent academic, commercial, and public data consumers. Although NHS data support a global research ecosystem, we found that multistage data flow chains limit transparency and risk public trust, most data interactions do not fulfil recommended best practices for safe data access, and existing infrastructure produces aggregation of duplicate data assets, thus limiting diversity of data and added value to end users. We provide recommendations to support data infrastructure transformation and have produced a website (https://DataInsights.uk) to promote transparency and showcase NHS data assets.
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Affiliation(s)
- Joe Zhang
- Institute of Global Health Innovation, Imperial College London, London, UK; Department of Critical Care, Guy's and St Thomas' NHS Foundation Trust, London, UK.
| | - Jess Morley
- Oxford Internet Institute, University of Oxford, Oxford, UK
| | - Jack Gallifant
- Department of Intensive Care, Imperial College Healthcare NHS Trust, London, UK; Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Chris Oddy
- Department of Anaesthesia, Critical Care and Pain, St George's Healthcare NHS Trust, London, UK
| | - James T Teo
- London Medical Imaging and AI Centre, Guy's and St Thomas' NHS Foundation Trust, London, UK; Department of Neurology, King's College Hospital NHS Foundation Trust, London, UK
| | - Hutan Ashrafian
- Institute of Global Health Innovation, Imperial College London, London, UK; Leeds University Business School, Leeds, UK
| | - Brendan Delaney
- Institute of Global Health Innovation, Imperial College London, London, UK
| | - Ara Darzi
- Institute of Global Health Innovation, Imperial College London, London, UK
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Iqbal S, N. Qureshi A, Li J, Mahmood T. On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2023; 30:3173-3233. [PMID: 37260910 PMCID: PMC10071480 DOI: 10.1007/s11831-023-09899-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 02/19/2023] [Indexed: 06/02/2023]
Abstract
Convolutional neural network (CNN) has shown dissuasive accomplishment on different areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information Retrieval, Medical Image Registration, Multi-lingual translation, Local language Processing, Anomaly Detection on video and Speech Recognition. CNN is a special type of Neural Network, which has compelling and effective learning ability to learn features at several steps during augmentation of the data. Recently, different interesting and inspiring ideas of Deep Learning (DL) such as different activation functions, hyperparameter optimization, regularization, momentum and loss functions has improved the performance, operation and execution of CNN Different internal architecture innovation of CNN and different representational style of CNN has significantly improved the performance. This survey focuses on internal taxonomy of deep learning, different models of vonvolutional neural network, especially depth and width of models and in addition CNN components, applications and current challenges of deep learning.
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Affiliation(s)
- Saeed Iqbal
- Department of Computer Science, Faculty of Information Technology & Computer Science, University of Central Punjab, Lahore, Punjab 54000 Pakistan
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124 Beijing China
| | - Adnan N. Qureshi
- Department of Computer Science, Faculty of Information Technology & Computer Science, University of Central Punjab, Lahore, Punjab 54000 Pakistan
| | - Jianqiang Li
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124 Beijing China
- Beijing Engineering Research Center for IoT Software and Systems, Beijing University of Technology, Beijing, 100124 Beijing China
| | - Tariq Mahmood
- Artificial Intelligence and Data Analytics (AIDA) Lab, College of Computer & Information Sciences (CCIS), Prince Sultan University, Riyadh, 11586 Kingdom of Saudi Arabia
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7
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Miller R, Farnebo S, Horwitz MD. Insights and trends review: artificial intelligence in hand surgery. J Hand Surg Eur Vol 2023; 48:396-403. [PMID: 36756841 DOI: 10.1177/17531934231152592] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
Artificial intelligence (AI) in hand surgery is an emerging and evolving field that will likely play a large role in the future care of our patients. However, there remain several challenges to makes this technology meaningful, acceptable and usable at scale. In this review article, we discuss basic concepts in AI, including challenges and key considerations, provide an update on how AI is being used in hand and wrist surgery and propose potential future applications. The aims are to equip clinicians and researchers with the basic knowledge needed to understand and explore the incorporation of AI in hand surgery within their own practice and recommends further reading to develop knowledge in this emerging field.
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Affiliation(s)
- Robert Miller
- Department of Hand and Plastic surgery department, Chelsea and Westminster Hospital, London, UK.,Fellow in Clinical Artificial Intelligence, The London Medical Imaging & AI Centre for Value Based Healthcare, London, UK
| | - Simon Farnebo
- Department of Plastic Surgery, Hand Surgery, and Burns, Department of Biomedical and Clinical Sciences, Faculty of Medicine and Health Sciences, Linköping University, Linköping, Sweden
| | - Maxim D Horwitz
- Department of Hand and Plastic surgery department, Chelsea and Westminster Hospital, London, UK
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