Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction

JHEP Reports, Journal Year: 2022, Volume and Issue: 4(4), P. 100443 - 100443

Published: Feb. 2, 2022

Clinical routine in hepatology involves the diagnosis and treatment of a wide spectrum metabolic, infectious, autoimmune neoplastic diseases. Clinicians integrate qualitative quantitative information from multiple data sources to make diagnosis, prognosticate disease course, recommend treatment. In last 5 years, advances artificial intelligence (AI), particularly deep learning, have made it possible extract clinically relevant complex diverse clinical datasets. particular, histopathology radiology image contain diagnostic, prognostic predictive which AI can extract. Ultimately, such systems could be implemented as decision support tools. However, context hepatology, this requires further large-scale validation regulatory approval. Herein, we summarise state art with particular focus on data. We present roadmap for development novel biomarkers outline critical obstacles need overcome.

Language: Английский

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Evaluation framework to guide implementation of AI systems into healthcare settings

BMJ Health & Care Informatics, Journal Year: 2021, Volume and Issue: 28(1), P. e100444 - e100444

Published: Oct. 1, 2021

To date, many artificial intelligence (AI) systems have been developed in healthcare, but adoption has limited. This may be due to inappropriate or incomplete evaluation and a lack of internationally recognised AI standards on evaluation. confidence the generalisability healthcare enable their integration into workflows, there is need for practical yet comprehensive instrument assess translational aspects available systems. Currently frameworks focus reporting regulatory little guidance regarding assessment like functional, utility ethical components.To address this gap create framework that assesses real-world systems, an international team translationally focused termed 'Translational Evaluation Healthcare (TEHAI)'. A critical review literature assessed existing gaps. Next, using health technology principles, components were identified consideration. These independently reviewed consensus inclusion final by panel eight expert.TEHAI includes three main components: capability, adoption. The emphasis features model development deployment distinguishes TEHAI from other instruments. In specific, can applied at any stage system.One major limitation narrow focus. TEHAI, because its strong foundation translation research models safety, value generalisability, not only theoretical basis also application assessing systems.The theoretic approach used develop should see it having just clinical settings, more broadly guide working

Language: Английский

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Checklist for Evaluation of Image-Based Artificial Intelligence Reports in Dermatology

JAMA Dermatology, Journal Year: 2021, Volume and Issue: 158(1), P. 90 - 90

Published: Dec. 1, 2021

The use of artificial intelligence (AI) is accelerating in all aspects medicine and has the potential to transform clinical care dermatology workflows. However, develop image-based algorithms for applications, comprehensive criteria establishing development performance evaluation standards are required ensure product fairness, reliability, safety.

Language: Английский

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Developing, implementing and governing artificial intelligence in medicine: a step-by-step approach to prevent an artificial intelligence winter

BMJ Health & Care Informatics, Journal Year: 2022, Volume and Issue: 29(1), P. e100495 - e100495

Published: Feb. 1, 2022

Objective Although the role of artificial intelligence (AI) in medicine is increasingly studied, most patients do not benefit because majority AI models remain testing and prototyping environment. The development implementation trajectory clinical are complex a structured overview missing. We therefore propose step-by-step to enhance clinicians’ understanding promote quality medical research. Methods summarised key elements (such as current guidelines, challenges, regulatory documents good practices) that needed develop safely implement medicine. Conclusion This complements other frameworks way it accessible stakeholders without prior knowledge such provides approach incorporating all guidelines essential for implementation, can thereby help move from bytes bedside.

Language: Английский

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Methods for Clinical Evaluation of Artificial Intelligence Algorithms for Medical Diagnosis

Radiology, Journal Year: 2022, Volume and Issue: 306(1), P. 20 - 31

Published: Nov. 8, 2022

Adequate clinical evaluation of artificial intelligence (AI) algorithms before adoption in practice is critical. Clinical aims to confirm acceptable AI performance through adequate external testing and the benefits AI-assisted care compared with conventional appropriately designed conducted studies, for which prospective studies are desirable. This article explains some fundamental methodological points that should be considered when designing appraising medical diagnosis. The specific topics addressed include following: (a) importance strategies conducting effectively, (b) various metrics graphical methods evaluating as well essential note using interpreting them, (c) paired study designs primarily comparative diagnoses, (d) parallel effect intervention an emphasis on randomized trials, (e) up-to-date guidelines reporting AI, registered EQUATOR Network library. Sound knowledge these will aid design, execution, reporting, appraisal AI.

Language: Английский

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Environmental Sustainability and AI in Radiology: A Double-Edged Sword

Radiology, Journal Year: 2024, Volume and Issue: 310(2)

Published: Feb. 1, 2024

According to the World Health Organization, climate change is single biggest health threat facing humanity. The global care system, including medical imaging, must manage effects of while at same time addressing large amount greenhouse gas (GHG) emissions generated in delivery care. Data centers and computational efforts are increasingly contributors GHG radiology. This due explosive increase big data artificial intelligence (AI) applications that have resulted energy requirements for developing deploying AI models. However, also has potential improve environmental sustainability imaging. For example, use can shorten MRI scan times with accelerated acquisition times, scheduling efficiency scanners, optimize decision-support tools reduce low-value purpose this

Language: Английский

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Developing, purchasing, implementing and monitoring AI tools in radiology: practical considerations. A multi-society statement from the ACR, CAR, ESR, RANZCR & RSNA

Insights into Imaging, Journal Year: 2024, Volume and Issue: 15(1)

Published: Jan. 22, 2024

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI radiology holds to revolutionize healthcare practices by advancing diagnosis, quantification, management multiple medical conditions. Nevertheless, ever-growing availability tools highlights an increasing need critically evaluate claims its utility differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones.This multi-society paper, presenting views Radiology Societies USA, Canada, Europe, Australia, New Zealand, defines practical problems ethical issues surrounding incorporation into radiological practice. In addition delineating main points concern that developers, regulators, purchasers should consider prior their introduction clinical practice, this statement also suggests methods monitor stability safety use, suitability autonomous function. This is intended serve as a useful summary which be considered all parties involved development resources, implementation tools.Key • artificial intelligence practice demands increased monitoring safety.• Cooperation between clinicians, regulators will allow address performance.• can fulfil promise advance patient well-being if steps are rigorously evaluated.

Language: Английский

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Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement from the ACR, CAR, ESR, RANZCR and RSNA

Radiology Artificial Intelligence, Journal Year: 2024, Volume and Issue: 6(1)

Published: Jan. 1, 2024

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI radiology holds to revolutionize healthcare practices by advancing diagnosis, quantification, management multiple medical conditions. Nevertheless, ever-growing availability tools highlights an increasing need critically evaluate claims its utility differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi-society paper, presenting views Radiology Societies USA, Canada, Europe, Australia, New Zealand, defines practical problems ethical issues surrounding incorporation into radiological practice. In addition delineating main points concern that developers, regulators, purchasers should consider prior their introduction clinical practice, this statement also suggests methods monitor stability safety use, suitability autonomous function. is intended serve as a useful summary which be considered all parties involved development resources, implementation tools. article simultaneously published Insights Imaging (DOI 10.1186/s13244-023-01541-3), Journal Medical Radiation Oncology 10.1111/1754-9485.13612), Canadian Association Radiologists 10.1177/08465371231222229), American College 10.1016/j.jacr.2023.12.005), Radiology: 10.1148/ryai.230513). Keywords: Intelligence, Radiology, Automation, Machine Learning Published under CC BY 4.0 license. ©The Author(s) 2024. Editor's Note: RSNA Board Directors has endorsed article. It not undergone review editing journal.

Language: Английский

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How AI May Transform Musculoskeletal Imaging

Radiology, Journal Year: 2024, Volume and Issue: 310(1)

Published: Jan. 1, 2024

While musculoskeletal imaging volumes are increasing, there is a relative shortage of subspecialized radiologists to interpret the studies. Will artificial intelligence (AI) be solution? For AI solution, wide implementation AI-supported data acquisition methods in clinical practice requires establishing trusted and reliable results. This will demand close collaboration between core researchers radiologists. Upon successful implementation, variety AI-based tools can improve radiologist's workflow by triaging examinations, helping with image interpretation, decreasing reporting time. Additional applications may also helpful for business, education, research purposes if successfully integrated into daily radiology. The question not whether replace radiologists, but rather how take advantage enhance their expert capabilities. © RSNA, 2024 Supplemental material available this article. See review "Present Future Innovations Cardiac MRI" Morales et al issue. An earlier incorrect version appeared online. article was corrected on January 19, 2024.

Language: Английский

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External validation of a commercially available deep learning algorithm for fracture detection in children

Diagnostic and Interventional Imaging, Journal Year: 2021, Volume and Issue: 103(3), P. 151 - 159

Published: Nov. 19, 2021

Language: Английский

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