The practical implementation of artificial intelligence technologies in medicine

Nature Medicine, Journal Year: 2018, Volume and Issue: 25(1), P. 30 - 36

Published: Dec. 24, 2018

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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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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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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Fiber/Fabric‐Based Piezoelectric and Triboelectric Nanogenerators for Flexible/Stretchable and Wearable Electronics and Artificial Intelligence

Advanced Materials, Journal Year: 2019, Volume and Issue: 32(5)

Published: July 26, 2019

Abstract Integration of advanced nanogenerator technology with conventional textile processes fosters the emergence textile‐based nanogenerators (NGs), which will inevitably promote rapid development and widespread applications next‐generation wearable electronics multifaceted artificial intelligence systems. NGs endow smart textiles mechanical energy harvesting multifunctional self‐powered sensing capabilities, while provide a versatile flexible design carrier extensive application platform for their development. However, due to lack an effective interactive communication channel between researchers specializing in those good at textiles, it is rather difficult achieve fiber/fabric‐based both excellent electrical output properties outstanding textile‐related performances. To this end, critical review presented on current state arts piezoelectric triboelectric respect basic classifications, material selections, fabrication techniques, structural designs, working principles, as well potential applications. Furthermore, difficulties tough challenges that can impede large‐scale commercial are summarized discussed. It hoped not only deepen ties NGs, but also push forward further research future NGs.

Language: Английский

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<p>Taiwan’s National Health Insurance Research Database: past and future</p>

Clinical Epidemiology, Journal Year: 2019, Volume and Issue: Volume 11, P. 349 - 358

Published: May 1, 2019

Abstract: Taiwan's National Health Insurance Research Database (NHIRD) exemplifies a population-level data source for generating real-world evidence to support clinical decisions and health care policy-making. Like with all claims databases, there have been some validity concerns of studies using the NHIRD, such as accuracy diagnosis codes issues around unmeasured confounders. Endeavors validate diagnosed or develop methodologic approaches address confounders largely increased reliability NHIRD studies. Recently, Ministry Welfare (MOHW) established Data Center (HWDC), repository site that centralizes about 70 other health-related databases management analyses. To strengthen protection privacy, investigators are required conduct on-site analysis at an HWDC through remote connection MOHW servers. Although tight regulation this has led inconvenience analysts time costs research, created opportunities enriched dimensions study by linking across databases. In near future, researchers will greater opportunity distill knowledge from linked hospital-based electronic medical records containing unstructured patient-level information artificial intelligence techniques, including machine learning natural language processes. We believe multiple sources could represent powerful research engine serve guiding light evidence-based medicine in Taiwan. Keywords: Taiwan, data, big analysis, validation, database cross-linkage

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 drug development: present status and future prospects

Drug Discovery Today, Journal Year: 2018, Volume and Issue: 24(3), P. 773 - 780

Published: Nov. 23, 2018

Language: Английский

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Plant leaf disease classification using EfficientNet deep learning model

Ecological Informatics, Journal Year: 2020, Volume and Issue: 61, P. 101182 - 101182

Published: Oct. 30, 2020

Language: Английский

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The role of artificial intelligence in healthcare: a structured literature review

BMC Medical Informatics and Decision Making, Journal Year: 2021, Volume and Issue: 21(1)

Published: April 10, 2021

Abstract Background/Introduction Artificial intelligence (AI) in the healthcare sector is receiving attention from researchers and health professionals. Few previous studies have investigated this topic a multi-disciplinary perspective, including accounting, business management, decision sciences professions. Methods The structured literature review with its reliable replicable research protocol allowed to extract 288 peer-reviewed papers Scopus. authors used qualitative quantitative variables analyse authors, journals, keywords, collaboration networks among researchers. Additionally, paper benefited Bibliometrix R software package. Results investigation showed that field emerging. It focuses on services predictive medicine, patient data diagnostics, clinical decision-making. United States, China, Kingdom contributed highest number of studies. Keyword analysis revealed AI can support physicians making diagnosis, predicting spread diseases customising treatment paths. Conclusions reveals several applications for stream has not fully been covered. For instance, projects require skills quality awareness data-intensive knowledge-based management. Insights help professionals understand address future field.

Language: Английский

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