Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Journal Of Big Data, Journal Year: 2021, Volume and Issue: 8(1)

Published: March 31, 2021

In the last few years, deep learning (DL) computing paradigm has been deemed Gold Standard in machine (ML) community. Moreover, it gradually become most widely used computational approach field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One benefits DL is ability to learn massive amounts data. The grown fast years and extensively successfully address a wide range traditional applications. More importantly, outperformed well-known ML techniques many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics control, medical information among others. Despite contributed works reviewing State-of-the-Art DL, all them only tackled one aspect which leads an overall lack knowledge about it. Therefore, this contribution, we propose using more holistic order provide suitable starting point from develop full understanding DL. Specifically, review attempts comprehensive survey important aspects including enhancements recently added field. particular, paper outlines importance presents types networks. It then convolutional neural networks (CNNs) utilized network type describes development CNNs architectures together with their main features, AlexNet closing High-Resolution (HR.Net). Finally, further present challenges suggested solutions help researchers understand existing research gaps. followed list major Computational tools FPGA, GPU, CPU are summarized along description influence ends evolution matrix, benchmark datasets, summary conclusion.

Language: Английский

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Machine Learning in Medicine

New England Journal of Medicine, Journal Year: 2019, Volume and Issue: 380(14), P. 1347 - 1358

Published: April 3, 2019

Interview with Dr. Isaac Kohane on machine learning in medicine. (16:31)Download In this view of the future medicine, patient–provider interactions are informed and supported by massive amounts data from similar patients. These collected curated to provide latest evidence-based assessment recommendations.

Language: Английский

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International evaluation of an AI system for breast cancer screening

Nature, Journal Year: 2020, Volume and Issue: 577(7788), P. 89 - 94

Published: Jan. 1, 2020

Language: Английский

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Key challenges for delivering clinical impact with artificial intelligence

BMC Medicine, Journal Year: 2019, Volume and Issue: 17(1)

Published: Oct. 29, 2019

Abstract Background Artificial intelligence (AI) research in healthcare is accelerating rapidly, with potential applications being demonstrated across various domains of medicine. However, there are currently limited examples such techniques successfully deployed into clinical practice. This article explores the main challenges and limitations AI healthcare, considers steps required to translate these potentially transformative technologies from Main body Key for translation systems include those intrinsic science machine learning, logistical difficulties implementation, consideration barriers adoption as well necessary sociocultural or pathway changes. Robust peer-reviewed evaluation part randomised controlled trials should be viewed gold standard evidence generation, but conducting practice may not always appropriate feasible. Performance metrics aim capture real applicability understandable intended users. Regulation that balances pace innovation harm, alongside thoughtful post-market surveillance, ensure patients exposed dangerous interventions nor deprived access beneficial innovations. Mechanisms enable direct comparisons must developed, including use independent, local representative test sets. Developers algorithms vigilant dangers, dataset shift, accidental fitting confounders, unintended discriminatory bias, generalisation new populations, negative consequences on health outcomes. Conclusion The safe timely clinically validated appropriately regulated can benefit everyone challenging. evaluation, using intuitive clinicians ideally go beyond measures technical accuracy quality care patient outcomes, essential. Further work (1) identify themes algorithmic bias unfairness while developing mitigations address these, (2) reduce brittleness improve generalisability, (3) develop methods improved interpretability learning predictions. If goals achieved, benefits likely transformational.

Language: Английский

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A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI

IEEE Transactions on Neural Networks and Learning Systems, Journal Year: 2020, Volume and Issue: 32(11), P. 4793 - 4813

Published: Oct. 21, 2020

Recently, artificial intelligence and machine learning in general have demonstrated remarkable performances many tasks, from image processing to natural language processing, especially with the advent of deep learning. Along research progress, they encroached upon different fields disciplines. Some them require high level accountability thus transparency, for example medical sector. Explanations decisions predictions are needed justify their reliability. This requires greater interpretability, which often means we need understand mechanism underlying algorithms. Unfortunately, blackbox nature is still unresolved, poorly understood. We provide a review on interpretabilities suggested by works categorize them. The categories show dimensions interpretability research, approaches that "obviously" interpretable information studies complex patterns. By applying same categorization it hoped (1) clinicians practitioners can subsequently approach these methods caution, (2) insights into will be born more considerations practices, (3) initiatives push forward data-based, mathematically- technically-grounded education encouraged.

Language: Английский

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A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis

The Lancet Digital Health, Journal Year: 2019, Volume and Issue: 1(6), P. e271 - e297

Published: Sept. 25, 2019

Deep learning offers considerable promise for medical diagnostics. We aimed to evaluate the diagnostic accuracy of deep algorithms versus health-care professionals in classifying diseases using imaging.In this systematic review and meta-analysis, we searched Ovid-MEDLINE, Embase, Science Citation Index, Conference Proceedings Index studies published from Jan 1, 2012, June 6, 2019. Studies comparing performance models based on imaging, any disease, were included. excluded that used waveform data graphics material or investigated image segmentation rather than disease classification. extracted binary constructed contingency tables derive outcomes interest: sensitivity specificity. undertaking an out-of-sample external validation included a unified hierarchical model. This study is registered with PROSPERO, CRD42018091176.Our search identified 31 587 studies, which 82 (describing 147 patient cohorts) 69 provided enough construct tables, enabling calculation test accuracy, ranging 9·7% 100·0% (mean 79·1%, SD 0·2) specificity 38·9% 88·3%, 0·1). An was done 25 14 made comparison between same sample. Comparison these when restricting analysis table each reporting highest found pooled 87·0% (95% CI 83·0-90·2) 86·4% (79·9-91·0) professionals, 92·5% 85·1-96·4) 90·5% (80·6-95·7) professionals.Our be equivalent professionals. However, major finding few presented externally validated results compared Additionally, poor prevalent limits reliable interpretation reported accuracy. New standards address specific challenges could improve future greater confidence evaluations promising technology.None.

Language: Английский

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Big data and machine learning algorithms for health-care delivery

The Lancet Oncology, Journal Year: 2019, Volume and Issue: 20(5), P. e262 - e273

Published: April 30, 2019

Language: Английский

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Revolutionizing healthcare: the role of artificial intelligence in clinical practice

BMC Medical Education, Journal Year: 2023, Volume and Issue: 23(1)

Published: Sept. 22, 2023

Abstract Introduction Healthcare systems are complex and challenging for all stakeholders, but artificial intelligence (AI) has transformed various fields, including healthcare, with the potential to improve patient care quality of life. Rapid AI advancements can revolutionize healthcare by integrating it into clinical practice. Reporting AI’s role in practice is crucial successful implementation equipping providers essential knowledge tools. Research Significance This review article provides a comprehensive up-to-date overview current state practice, its applications disease diagnosis, treatment recommendations, engagement. It also discusses associated challenges, covering ethical legal considerations need human expertise. By doing so, enhances understanding significance supports organizations effectively adopting technologies. Materials Methods The investigation analyzed use system relevant indexed literature, such as PubMed/Medline, Scopus, EMBASE, no time constraints limited articles published English. focused question explores impact applying settings outcomes this application. Results Integrating holds excellent improving selection, laboratory testing. tools leverage large datasets identify patterns surpass performance several aspects. offers increased accuracy, reduced costs, savings while minimizing errors. personalized medicine, optimize medication dosages, enhance population health management, establish guidelines, provide virtual assistants, support mental care, education, influence patient-physician trust. Conclusion be used diagnose diseases, develop plans, assist clinicians decision-making. Rather than simply automating tasks, about developing technologies that across settings. However, challenges related data privacy, bias, expertise must addressed responsible effective healthcare.

Language: Английский

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Precision Medicine, AI, and the Future of Personalized Health Care

Clinical and Translational Science, Journal Year: 2020, Volume and Issue: 14(1), P. 86 - 93

Published: Sept. 22, 2020

The convergence of artificial intelligence (AI) and precision medicine promises to revolutionize health care. Precision methods identify phenotypes patients with less‐common responses treatment or unique healthcare needs. AI leverages sophisticated computation inference generate insights, enables the system reason learn, empowers clinician decision making through augmented intelligence. Recent literature suggests that translational research exploring this will help solve most difficult challenges facing medicine, especially those in which nongenomic genomic determinants, combined information from patient symptoms, clinical history, lifestyles, facilitate personalized diagnosis prognostication.

Language: Английский

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IL-6 in inflammation, autoimmunity and cancer

International Immunology, Journal Year: 2020, Volume and Issue: 33(3), P. 127 - 148

Published: Dec. 15, 2020

IL-6 is involved both in immune responses and inflammation, hematopoiesis, bone metabolism embryonic development. plays roles chronic inflammation (closely related to inflammatory diseases, autoimmune diseases cancer) even the cytokine storm of corona virus disease 2019 (COVID-19). Acute during response wound healing a well-controlled response, whereas are uncontrolled responses. Non-immune cells, cytokines such as IL-1β, tumor necrosis factor alpha (TNFα) transcription factors nuclear factor-kappa B (NF-κB) signal transducer activator 3 (STAT3) play central inflammation. Synergistic interactions between NF-κB STAT3 induce hyper-activation followed by production various cytokines. Because an target, simultaneous activation non-immune cells triggers positive feedback loop IL-6-STAT3 axis. This called amplifier (IL-6 Amp) key player local initiation model, which states that initiators, senescence, obesity, stressors, infection, injury smoking, trigger promoting cells. model counters dogma holds autoimmunity oncogenesis triggered breakdown tissue-specific tolerance oncogenic mutations, respectively. The Amp activated variety demonstrating axis critical target for treating diseases.

Language: Английский

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