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Ahmed MI, Spooner B, Isherwood J, Lane M, Orrock E, Dennison A. A Systematic Review of the Barriers to the Implementation of Artificial Intelligence in Healthcare. Cureus 2023; 15:e46454. [PMID: 37927664 PMCID: PMC10623210 DOI: 10.7759/cureus.46454] [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: 10/03/2023] [Indexed: 11/07/2023] Open
Abstract
Artificial intelligence (AI) is expected to improve healthcare outcomes by facilitating early diagnosis, reducing the medical administrative burden, aiding drug development, personalising medical and oncological management, monitoring healthcare parameters on an individual basis, and allowing clinicians to spend more time with their patients. In the post-pandemic world where there is a drive for efficient delivery of healthcare and manage long waiting times for patients to access care, AI has an important role in supporting clinicians and healthcare systems to streamline the care pathways and provide timely and high-quality care for the patients. Despite AI technologies being used in healthcare for some decades, and all the theoretical potential of AI, the uptake in healthcare has been uneven and slower than anticipated and there remain a number of barriers, both overt and covert, which have limited its incorporation. This literature review highlighted barriers in six key areas: ethical, technological, liability and regulatory, workforce, social, and patient safety barriers. Defining and understanding the barriers preventing the acceptance and implementation of AI in the setting of healthcare will enable clinical staff and healthcare leaders to overcome the identified hurdles and incorporate AI technologies for the benefit of patients and clinical staff.
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Affiliation(s)
- Molla Imaduddin Ahmed
- Paediatric Respiratory Medicine, University Hospitals of Leicester NHS Trust, Leicester, GBR
| | - Brendan Spooner
- Intensive Care and Anaesthesia, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, GBR
| | - John Isherwood
- Hepatobiliary and Pancreatic Surgery, University Hospitals of Leicester NHS Trust, Leicester, GBR
| | - Mark Lane
- Ophthalmology, Birmingham and Midland Eye Centre, Birmingham, GBR
| | - Emma Orrock
- Head of Clinical Senates, East and West Midlands Clinical Senate, Leicester, GBR
| | - Ashley Dennison
- Hepatobiliary and Pancreatic Surgery, University Hospitals of Leicester NHS Trust, Leicester, GBR
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2
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Current Role of Delta Radiomics in Head and Neck Oncology. Int J Mol Sci 2023; 24:ijms24032214. [PMID: 36768535 PMCID: PMC9916410 DOI: 10.3390/ijms24032214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Accepted: 01/19/2023] [Indexed: 01/26/2023] Open
Abstract
The latest developments in the management of head and neck cancer show an increasing trend in the implementation of novel approaches using artificial intelligence for better patient stratification and treatment-related risk evaluation. Radiomics, or the extraction of data from various imaging modalities, is a tool often used to evaluate specific features related to the tumour or normal tissue that are not identifiable by the naked eye and which can add value to existing clinical data. Furthermore, the assessment of feature variations from one time point to another based on subsequent images, known as delta radiomics, was shown to have even higher value for treatment-outcome prediction or patient stratification into risk categories. The information gathered from delta radiomics can, further, be used for decision making regarding treatment adaptation or other interventions found to be beneficial to the patient. The aim of this work is to collate the existing studies on delta radiomics in head and neck cancer and evaluate its role in tumour response and normal-tissue toxicity predictions alike. Moreover, this work also highlights the role of holomics, which brings under the same umbrella clinical and radiomic features, for a more complex patient characterization and treatment optimisation.
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Ahn G, Choi BS, Ko S, Jo C, Han HS, Lee MC, Ro DH. High-resolution knee plain radiography image synthesis using style generative adversarial network adaptive discriminator augmentation. J Orthop Res 2023; 41:84-93. [PMID: 35293648 DOI: 10.1002/jor.25325] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 02/21/2022] [Accepted: 03/11/2022] [Indexed: 02/04/2023]
Abstract
In this retrospective study, 10,000 anteroposterior (AP) radiography of the knee from a single institution was used to create medical data set that are more balanced and cheaper to create. Two types of convolutional networks were used, deep convolutional GAN (DCGAN) and Style GAN Adaptive Discriminator Augmentation (StyleGAN2-ADA). To verify the quality of generated images from StyleGAN2-ADA compared to real ones, the Visual Turing test was conducted by two computer vision experts, two orthopedic surgeons, and a musculoskeletal radiologist. For quantitative analysis, the Fréchet inception distance (FID), and principal component analysis (PCA) were used. Generated images reproduced the features of osteophytes, joint space narrowing, and sclerosis. Classification accuracy of the experts was 34%, 43%, 44%, 57%, and 50%. FID between the generated images and real ones was 2.96, which is significantly smaller than another medical data set (BreCaHAD = 15.1). PCA showed that no significant difference existed between the PCs of the real and generated images (p > 0.05). At least 2000 images were required to make reliable images optimally. By performing PCA in latent space, we were able to control the desired PC that show a progression of arthritis. Using a GAN, we were able to generate knee X-ray images that accurately reflected the characteristics of the arthritis progression stage, which neither human experts nor artificial intelligence could discern apart from the real images. In summary, our research opens up the potential to adopt a generative model to synthesize realistic anonymous images that can also solve data scarcity and class inequalities.
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Affiliation(s)
- Gun Ahn
- Interdisciplinary Program of Bioengineering, Seoul National University, Seoul, Korea.,Department of Orthopedic Surgery, Seoul National University Hospital, Jongno-gu, Korea
| | - Byung Sun Choi
- Department of Orthopedic Surgery, Seoul National University Hospital, Jongno-gu, Korea
| | - Sunho Ko
- Department of Medicine, Seoul National University, Seoul, Korea
| | - Changwung Jo
- Department of Medicine, Seoul National University, Seoul, Korea
| | - Hyuk-Soo Han
- Department of Orthopedic Surgery, Seoul National University Hospital, Jongno-gu, Korea
| | - Myung Chul Lee
- Department of Orthopedic Surgery, Seoul National University Hospital, Jongno-gu, Korea
| | - Du Hyun Ro
- Department of Orthopedic Surgery, Seoul National University Hospital, Jongno-gu, Korea.,CONNECTEVE Co., Ltd, Seoul, Korea
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Holloway L, Bezak E, Baldock C. Artificial intelligence (AI) will enable improved diagnosis and treatment outcomes. Phys Eng Sci Med 2021; 44:603-606. [PMID: 34370272 DOI: 10.1007/s13246-021-01034-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/12/2021] [Indexed: 10/20/2022]
Affiliation(s)
- Lois Holloway
- Liverpool and Macarthur Cancer Therapy Centres and Ingham Institute, Liverpool, NSW, Australia.,South Western Sydney Clinical School, University of New South Wales, Sydney, NSW, Australia.,Institute of Medical Physics, University of Sydney, Sydney, NSW, Australia.,Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW, 2522, Australia
| | - Eva Bezak
- Cancer Research Institute and School of Health Sciences, University of South Australia, GPO BOX 2471, Adelaide, SA, 5001, Australia.,School of Physical Sciences, University of Adelaide, North Terrace, Adelaide, South Australia, 5005, Australia
| | - Clive Baldock
- Research and Innovation Division, University of Wollongong, Wollongong, NSW, 2522, Australia.
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Marcu LG. The Ever-Changing Role of Medical Physicists in the Era of Personalized Medicine. J Med Phys 2021; 45:197-198. [PMID: 33953493 PMCID: PMC8074720 DOI: 10.4103/jmp.jmp_113_20] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 11/24/2020] [Indexed: 12/03/2022] Open
Affiliation(s)
- Loredana G Marcu
- Department of Physics, Faculty of Informatics and Science, University of Oradea, Oradea, Romania.,Cancer Research Institute, University of South Australia, Adelaide, SA, Australia
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Heydari F, Rafsanjani MK. A Review on Lung Cancer Diagnosis Using Data Mining Algorithms. Curr Med Imaging 2021; 17:16-26. [PMID: 32586255 DOI: 10.2174/1573405616666200625153017] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 05/01/2020] [Accepted: 05/11/2020] [Indexed: 11/22/2022]
Abstract
Due to the serious consequences of lung cancer, medical associations use computer-aided diagnostic procedures to diagnose this disease more accurately. Despite the damaging effects of lung cancer on the body, the lifetime of cancer patients can be extended by early diagnosis. Data mining techniques are practical in diagnosing lung cancer in its first stages. This paper surveys a number of leading data mining-based cancer diagnosis approaches. Moreover, this review draws a comparison between data mining approaches in terms of selection criteria and presents the advantages and disadvantages of each method.
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Affiliation(s)
- Farzad Heydari
- Department of Computer Science, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Marjan Kuchaki Rafsanjani
- Department of Computer Science, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran
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Ahmad HM, Khan MJ, Yousaf A, Ghuffar S, Khurshid K. Deep Learning: A Breakthrough in Medical Imaging. Curr Med Imaging 2020; 16:946-956. [PMID: 33081657 DOI: 10.2174/1573405615666191219100824] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Revised: 11/25/2019] [Accepted: 12/06/2019] [Indexed: 02/08/2023]
Abstract
Deep learning has attracted great attention in the medical imaging community as a promising solution for automated, fast and accurate medical image analysis, which is mandatory for quality healthcare. Convolutional neural networks and its variants have become the most preferred and widely used deep learning models in medical image analysis. In this paper, concise overviews of the modern deep learning models applied in medical image analysis are provided and the key tasks performed by deep learning models, i.e. classification, segmentation, retrieval, detection, and registration are reviewed in detail. Some recent researches have shown that deep learning models can outperform medical experts in certain tasks. With the significant breakthroughs made by deep learning methods, it is expected that patients will soon be able to safely and conveniently interact with AI-based medical systems and such intelligent systems will actually improve patient healthcare. There are various complexities and challenges involved in deep learning-based medical image analysis, such as limited datasets. But researchers are actively working in this area to mitigate these challenges and further improve health care with AI.
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Affiliation(s)
- Hafiz Mughees Ahmad
- Artificial Intelligence and Computer Vision (iVision) Lab, Department of Electrical Engineering, Institute of Space
Technology, Islamabad, Pakistan
| | - Muhammad Jaleed Khan
- Artificial Intelligence and Computer Vision (iVision) Lab, Department of Electrical Engineering, Institute of Space
Technology, Islamabad, Pakistan
| | - Adeel Yousaf
- Artificial Intelligence and Computer Vision (iVision) Lab, Department of Electrical Engineering, Institute of Space
Technology, Islamabad, Pakistan,Department of Avionics Engineering, Institute of Space Technology, Islamabad, Pakistan
| | - Sajid Ghuffar
- Artificial Intelligence and Computer Vision (iVision) Lab, Department of Electrical Engineering, Institute of Space
Technology, Islamabad, Pakistan,Department of Space Science, Institute of Space Technology, Islamabad, Pakistan
| | - Khurram Khurshid
- Artificial Intelligence and Computer Vision (iVision) Lab, Department of Electrical Engineering, Institute of Space
Technology, Islamabad, Pakistan
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Gadag S, Sinha S, Nayak Y, Garg S, Nayak UY. Combination Therapy and Nanoparticulate Systems: Smart Approaches for the Effective Treatment of Breast Cancer. Pharmaceutics 2020; 12:E524. [PMID: 32521684 PMCID: PMC7355786 DOI: 10.3390/pharmaceutics12060524] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 06/02/2020] [Accepted: 06/04/2020] [Indexed: 12/14/2022] Open
Abstract
Breast cancer has become one of the biggest concerns for oncologists in the past few decades because of its unpredictable etiopathology and nonavailability of personalized translational medicine. The number of women getting affected by breast cancer has increased dramatically, owing to lifestyle and environmental changes. Besides, the development of multidrug resistance has become a challenge in the therapeutic management of breast cancer. Studies reveal that the use of monotherapy is not effective in the management of breast cancer due to high toxicity and the development of resistance. Combination therapies, such as radiation therapy with adjuvant therapy, endocrine therapy with chemotherapy, and targeted therapy with immunotherapy, are found to be effective. Thus, multimodal and combination treatments, along with nanomedicine, have emerged as a promising strategy with minimum side effects and drug resistance. In this review, we emphasize the multimodal approaches and recent advancements in breast cancer treatment modalities, giving importance to the current data on clinical trials. The novel treatment approach by targeted therapy, according to type, such as luminal, HER2 positive, and triple-negative breast cancer, are discussed. Further, passive and active targeting technologies, including nanoparticles, bioconjugate systems, stimuli-responsive, and nucleic acid delivery systems, including siRNA and aptamer, are explained. The recent research exploring the role of nanomedicine in combination therapy and the possible use of artificial intelligence in breast cancer therapy is also discussed herein. The complexity and dynamism of disease changes require the constant upgrading of knowledge, and innovation is essential for future drug development for treating breast cancer.
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Affiliation(s)
- Shivaprasad Gadag
- Department of Pharmaceutics, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal 576104, India; (S.G.); (S.S.)
| | - Shristi Sinha
- Department of Pharmaceutics, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal 576104, India; (S.G.); (S.S.)
| | - Yogendra Nayak
- Department of Pharmacology, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal 576104, India;
| | - Sanjay Garg
- UniSA: Clinical and Health Sciences, University of South Australia, Adelaide, SA 5000, Australia;
| | - Usha Y. Nayak
- Department of Pharmaceutics, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal 576104, India; (S.G.); (S.S.)
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