AI in health and medicine

Nature Medicine, Journal Year: 2022, Volume and Issue: 28(1), P. 31 - 38

Published: Jan. 1, 2022

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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COVID-19 Image Data Collection: Prospective Predictions are the Future

The Journal of Machine Learning for Biomedical Imaging, Journal Year: 2020, Volume and Issue: 1(December 2020), P. 1 - 38

Published: Dec. 15, 2020

Across the world’s coronavirus disease 2019 (COVID-19) hot spots, need to streamline patient diagnosis and management has become more pressing than ever. As one of main imaging tools, chest X-rays (CXRs) are common, fast, non-invasive, relatively cheap, potentially bedside monitor progression disease. This paper describes first public COVID-19 image data collection as well a preliminary exploration possible use cases for data. dataset currently contains hundreds frontal view is largest resource prognostic data, making it necessary develop evaluate tools aid in treatment COVID-19. It was manually aggregated from publication figures various web based repositories into machine learning (ML) friendly format with accompanying dataloader code. We collected lateral imagery metadata such time since symptoms, intensive care unit (ICU) status, survival intubation or hospital location. present multiple predicting ICU, survival, understanding patient’s trajectory during treatment. Data can be accessed here: https://github.com/ieee8023/covid-chestxray-dataset

Language: Английский

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Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects

Business Process Management Journal, Journal Year: 2020, Volume and Issue: 26(7), P. 1893 - 1924

Published: May 12, 2020

Purpose The main purpose of our study is to analyze the influence Artificial Intelligence (AI) on firm performance, notably by building business value AI-based transformation projects. This was conducted using a four-step sequential approach: (1) analysis AI and concepts/technologies; (2) in-depth exploration case studies from great number industrial sectors; (3) data collection databases (websites) solution providers; (4) review literature identify their impact performance organizations while highlighting AI-enabled projects within organizations. Design/methodology/approach has called theory IT capabilities seize (at organizational process levels). research (responding question, making discussions, interpretations comparisons, formulating recommendations) based 500 IBM, AWS, Cloudera, Nvidia, Conversica, Universal Robots websites, etc. Studying organizations, more specifically, such organizations’ projects, required us make an archival following three steps, namely conceptual phase, refinement development assessment phase. Findings covers wide range technologies, including machine translation, chatbots self-learning algorithms, all which can allow individuals better understand environment act accordingly. Organizations have been adopting technological innovations with view adapting or disrupting ecosystem developing optimizing strategic competitive advantages. fully expresses its potential through ability optimize existing processes improve automation, information effects, but also detect, predict interact humans. Thus, results highlighted benefits in at both (financial, marketing administrative) levels. By these attributes, can, therefore, enhance transformed same showed that achieve only when they use features/technologies reconfigure processes. Research limitations/implications obviously influences way businesses are done today. Therefore, practitioners researchers need consider as valuable support even pilot for new model. For study, we adopted framework geared toward inclusive comprehensive approach so account intangible In terms interest, this nurtures scientific aims proposing model analyzing time, filling associated gap literature. As managerial provide managers elements be reconfigured added order take advantage full AI, therefore profitability investments some advantage. allows not single technology set/combination several different configurations various company’s areas because multiple key must brought together ensure success AI: data, talent mix, domain knowledge, decisions, external partnerships scalable infrastructure. Originality/value article analyses reuse secondary deployment reports focuses mainly indirectly those occurring level. being examined significant tangible evidence about performance. More article, studies, exposes levels, considering it industries.

Language: Английский

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Artificial intelligence in healthcare: transforming the practice of medicine

Future Healthcare Journal, Journal Year: 2021, Volume and Issue: 8(2), P. e188 - e194

Published: July 1, 2021

Artificial intelligence (AI) is a powerful and disruptive area of computer science, with the potential to fundamentally transform practice medicine delivery healthcare. In this review article, we outline recent breakthroughs in application AI healthcare, describe roadmap building effective, reliable safe systems, discuss possible future direction augmented healthcare systems.

Language: Английский

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Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension

Nature Medicine, Journal Year: 2020, Volume and Issue: 26(9), P. 1364 - 1374

Published: Sept. 1, 2020

Abstract The CONSORT 2010 statement provides minimum guidelines for reporting randomized trials. Its widespread use has been instrumental in ensuring transparency the evaluation of new interventions. More recently, there a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective demonstrate impact on health outcomes. CONSORT-AI (Consolidated Standards Reporting Trials–Artificial Intelligence) extension is guideline clinical trials evaluating with an AI component. It was developed parallel its companion trial protocols: SPIRIT-AI (Standard Protocol Items: Recommendations Interventional Intelligence). Both were through staged consensus process literature review and expert consultation generate 29 candidate items, which assessed by international multi-stakeholder group two-stage Delphi survey (103 stakeholders), agreed upon two-day meeting (31 stakeholders) refined checklist pilot (34 participants). includes 14 items considered sufficiently important they should be routinely reported addition core items. recommends investigators provide clear descriptions intervention, including instructions skills required use, setting intervention integrated, handling inputs outputs human–AI interaction provision analysis error cases. will help promote completeness assist editors peer reviewers, as well general readership, understand, interpret critically appraise quality design risk bias

Language: Английский

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Deep learning in histopathology: the path to the clinic

Nature Medicine, Journal Year: 2021, Volume and Issue: 27(5), P. 775 - 784

Published: May 1, 2021

Language: Английский

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Deep learning in cancer diagnosis, prognosis and treatment selection

Genome Medicine, Journal Year: 2021, Volume and Issue: 13(1)

Published: Sept. 27, 2021

Abstract Deep learning is a subdiscipline of artificial intelligence that uses machine technique called neural networks to extract patterns and make predictions from large data sets. The increasing adoption deep across healthcare domains together with the availability highly characterised cancer datasets has accelerated research into utility in analysis complex biology cancer. While early results are promising, this rapidly evolving field new knowledge emerging both learning. In review, we provide an overview techniques how they being applied oncology. We focus on applications for omics types, including genomic, methylation transcriptomic data, as well histopathology-based genomic inference, perspectives different types can be integrated develop decision support tools. specific examples may diagnosis, prognosis treatment management. also assess current limitations challenges application precision oncology, lack phenotypically rich need more explainable models. Finally, conclude discussion obstacles overcome enable future clinical utilisation

Language: Английский

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Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis

npj Digital Medicine, Journal Year: 2021, Volume and Issue: 4(1)

Published: April 7, 2021

Deep learning (DL) has the potential to transform medical diagnostics. However, diagnostic accuracy of DL is uncertain. Our aim was evaluate algorithms identify pathology in imaging. Searches were conducted Medline and EMBASE up January 2020. We identified 11,921 studies, which 503 included systematic review. Eighty-two studies ophthalmology, 82 breast disease 115 respiratory for meta-analysis. Two hundred twenty-four other specialities qualitative Peer-reviewed that reported on using imaging included. Primary outcomes measures accuracy, study design reporting standards literature. Estimates pooled random-effects In AUC's ranged between 0.933 1 diagnosing diabetic retinopathy, age-related macular degeneration glaucoma retinal fundus photographs optical coherence tomography. imaging, 0.864 0.937 lung nodules or cancer chest X-ray CT scan. For 0.868 0.909 mammogram, ultrasound, MRI digital tomosynthesis. Heterogeneity high extensive variation methodology, terminology outcome noted. This can lead an overestimation There immediate need development artificial intelligence-specific EQUATOR guidelines, particularly STARD, order provide guidance around key issues this field.

Language: Английский

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Recent advances and clinical applications of deep learning in medical image analysis

Medical Image Analysis, Journal Year: 2022, Volume and Issue: 79, P. 102444 - 102444

Published: April 4, 2022

Language: Английский

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