-
American Journal of Public Health Jan 2021
Topics: Artificial Intelligence; Humans; Prejudice; Public Health
PubMed: 33326280
DOI: 10.2105/AJPH.2020.306006 -
The American Journal of Pathology Oct 2021
Topics: Artificial Intelligence; Humans; Machine Learning; Neural Networks, Computer; Pathology
PubMed: 34391718
DOI: 10.1016/j.ajpath.2021.07.011 -
Bioengineered Dec 2023Fucoxanthin is a carotenoid that possesses various beneficial medicinal properties for human well-being. However, the current extraction technologies and quantification... (Review)
Review
Fucoxanthin is a carotenoid that possesses various beneficial medicinal properties for human well-being. However, the current extraction technologies and quantification techniques are still lacking in terms of cost validation, high energy consumption, long extraction time, and low yield production. To date, artificial intelligence (AI) models can assist and improvise the bottleneck of fucoxanthin extraction and quantification process by establishing new technologies and processes which involve big data, digitalization, and automation for efficiency fucoxanthin production. This review highlights the application of AI models such as artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS), capable of learning patterns and relationships from large datasets, capturing non-linearity, and predicting optimal conditions that significantly impact the fucoxanthin extraction yield. On top of that, combining metaheuristic algorithm such as genetic algorithm (GA) can further improve the parameter space and discovery of optimal conditions of ANN and ANFIS models, which results in high R accuracy ranging from 98.28% to 99.60% after optimization. Besides, AI models such as support vector machine (SVM), convolutional neural networks (CNNs), and ANN have been leveraged for the quantification of fucoxanthin, either computer vision based on color space of images or regression analysis based on statistical data. The findings are reliable when modeling for the concentration of pigments with high R accuracy ranging from 66.0% - 99.2%. This review paper has reviewed the feasibility and potential of AI for the extraction and quantification purposes, which can reduce the cost, accelerate the fucoxanthin yields, and development of fucoxanthin-based products.
Topics: Humans; Artificial Intelligence; Microalgae; Fuzzy Logic; Neural Networks, Computer
PubMed: 37578162
DOI: 10.1080/21655979.2023.2244232 -
Kidney International Jul 2020Artificial intelligence (AI) for the purpose of this review is an umbrella term for technologies emulating a nephropathologist's ability to extract information on... (Review)
Review
Artificial intelligence (AI) for the purpose of this review is an umbrella term for technologies emulating a nephropathologist's ability to extract information on diagnosis, prognosis, and therapy responsiveness from native or transplant kidney biopsies. Although AI can be used to analyze a wide variety of biopsy-related data, this review focuses on whole slide images traditionally used in nephropathology. AI applications in nephropathology have recently become available through several advancing technologies, including (i) widespread introduction of glass slide scanners, (ii) data servers in pathology departments worldwide, and (iii) through greatly improved computer hardware to enable AI training. In this review, we explain how AI can enhance the reproducibility of nephropathology results for certain parameters in the context of precision medicine using advanced architectures, such as convolutional neural networks, that are currently the state of the art in machine learning software for this task. Because AI applications in nephropathology are still in their infancy, we show the power and potential of AI applications mostly in the example of oncopathology. Moreover, we discuss the technological obstacles as well as the current stakeholder and regulatory concerns about developing AI applications in nephropathology from the perspective of nephropathologists and the wider nephrology community. We expect the gradual introduction of these technologies into routine diagnostics and research for selective tasks, suggesting that this technology will enhance the performance of nephropathologists rather than making them redundant.
Topics: Artificial Intelligence; Machine Learning; Neural Networks, Computer; Reproducibility of Results; Software
PubMed: 32475607
DOI: 10.1016/j.kint.2020.02.027 -
Clinica Chimica Acta; International... Jun 2023Artificial intelligence (AI)-based medical technologies are rapidly evolving into actionable solutions for clinical practice. Machine learning (ML) algorithms can... (Review)
Review
Artificial intelligence (AI)-based medical technologies are rapidly evolving into actionable solutions for clinical practice. Machine learning (ML) algorithms can process increasing amounts of laboratory data such as gene expression immunophenotyping data and biomarkers. In recent years, the analysis of ML has become particularly useful for the study of complex chronic diseases, such as rheumatic diseases, heterogenous conditions with multiple triggers. Numerous studies have used ML to classify patients and improve diagnosis, to stratify the risk and determine disease subtypes, as well as to discover biomarkers and gene signatures. This review aims to provide examples of ML models for specific rheumatic diseases using laboratory data and some insights into relevant strengths and limitations. A better understanding and future application of these analytical strategies could facilitate the development of precision medicine for rheumatic patients.
Topics: Humans; Artificial Intelligence; Algorithms; Machine Learning; Rheumatic Diseases; Biomarkers
PubMed: 37187221
DOI: 10.1016/j.cca.2023.117388 -
Annual Review of Pharmacology and... Jan 2023The use of artificial intelligence (AI) and machine learning (ML) in pharmaceutical research and development has to date focused on research: target identification;... (Review)
Review
The use of artificial intelligence (AI) and machine learning (ML) in pharmaceutical research and development has to date focused on research: target identification; docking-, fragment-, and motif-based generation of compound libraries; modeling of synthesis feasibility; rank-ordering likely hits according to structural and chemometric similarity to compounds having known activity and affinity to the target(s); optimizing a smaller library for synthesis and high-throughput screening; and combining evidence from screening to support hit-to-lead decisions. Applying AI/ML methods to lead optimization and lead-to-candidate (L2C) decision-making has shown slower progress, especially regarding predicting absorption, distribution, metabolism, excretion, and toxicology properties. The present review surveys reasons why this is so, reports progress that has occurred in recent years, and summarizes some of the issues that remain. Effective AI/ML tools to derisk L2C and later phases of development are important to accelerate the pharmaceutical development process, ameliorate escalating development costs, and achieve greater success rates.
Topics: Humans; Artificial Intelligence; Machine Learning; High-Throughput Screening Assays; Drug Development
PubMed: 35679624
DOI: 10.1146/annurev-pharmtox-051921-023255 -
Pharmacological Reports : PR Feb 2023Artificial intelligence (AI) is a machine science that can mimic human behaviour like intelligent analysis of data. AI functions with specialized algorithms and... (Review)
Review
Artificial intelligence (AI) is a machine science that can mimic human behaviour like intelligent analysis of data. AI functions with specialized algorithms and integrates with deep and machine learning. Living in the digital world can generate a huge amount of medical data every day. Therefore, we need an automated and reliable evaluation tool that can make decisions more accurately and faster. Machine learning has the potential to learn, understand and analyse the data used in healthcare systems. In the last few years, AI is known to be employed in various fields in pharmaceutical science especially in pharmacological research. It helps in the analysis of preclinical (laboratory animals) and clinical (in human) trial data. AI also plays important role in various processes such as drug discovery/manufacturing, diagnosis of big data for disease identification, personalized treatment, clinical trial research, radiotherapy, surgical robotics, smart electronic health records, and epidemic outbreak prediction. Moreover, AI has been used in the evaluation of biomarkers and diseases. In this review, we explain various models and general processes of machine learning and their role in pharmacological science. Therefore, AI with deep learning and machine learning could be relevant in pharmacological research.
Topics: Humans; Artificial Intelligence; Algorithms; Machine Learning; Drug Discovery
PubMed: 36624355
DOI: 10.1007/s43440-022-00445-1 -
Anaesthesia Jan 2021The current fourth industrial revolution is a distinct technological era characterised by the blurring of physics, computing and biology. The driver of change is data,... (Review)
Review
The current fourth industrial revolution is a distinct technological era characterised by the blurring of physics, computing and biology. The driver of change is data, powered by artificial intelligence. The UK National Health Service Topol Report embraced this digital revolution and emphasised the importance of artificial intelligence to the health service. Application of artificial intelligence within regional anaesthesia, however, remains limited. An example of the use of a convoluted neural network applied to visual detection of nerves on ultrasound images is described. New technologies that may impact on regional anaesthesia include robotics and artificial sensing. Robotics in anaesthesia falls into three categories. The first, used commonly, is pharmaceutical, typified by target-controlled anaesthesia using electroencephalography within a feedback loop. Other types include mechanical robots that provide precision and dexterity better than humans, and cognitive robots that act as decision support systems. It is likely that the latter technology will expand considerably over the next decades and provide an autopilot for anaesthesia. Technical robotics will focus on the development of accurate sensors for training that incorporate visual and motion metrics. These will be incorporated into augmented reality and visual reality environments that will provide training at home or the office on life-like simulators. Real-time feedback will be offered that stimulates and rewards performance. In discussing the scope, applications, limitations and barriers to adoption of these technologies, we aimed to stimulate discussion towards a framework for the optimal application of current and emerging technologies in regional anaesthesia.
Topics: Anesthesia, Conduction; Artificial Intelligence; Robotics
PubMed: 33426667
DOI: 10.1111/anae.15274 -
Sensors (Basel, Switzerland) May 2022Nondestructive evaluation (NDE) techniques are used in many industries to evaluate the properties of components and inspect for flaws and anomalies in structures without... (Review)
Review
Nondestructive evaluation (NDE) techniques are used in many industries to evaluate the properties of components and inspect for flaws and anomalies in structures without altering the part's integrity or causing damage to the component being tested. This includes monitoring materials' condition (Material State Awareness (MSA)) and health of structures (Structural Health Monitoring (SHM)). NDE techniques are highly valuable tools to help prevent potential losses and hazards arising from the failure of a component while saving time and cost by not compromising its future usage. On the other hand, Artificial Intelligence (AI) and Machine Learning (ML) techniques are useful tools which can help automating data collection and analyses, providing new insights, and potentially improving detection performance in a quick and low effort manner with great cost savings. This paper presents a survey on state of the art AI-ML techniques for NDE and the application of related smart technologies including Machine Vision (MV) and Digital Twins in NDE.
Topics: Artificial Intelligence; Forecasting; Machine Learning; Technology
PubMed: 35684675
DOI: 10.3390/s22114055 -
Texas Heart Institute Journal Mar 2022Artificial intelligence and machine learning are rapidly gaining popularity in every aspect of cardiovascular medicine. This review discusses the past, present, and... (Review)
Review
Artificial intelligence and machine learning are rapidly gaining popularity in every aspect of cardiovascular medicine. This review discusses the past, present, and future of artificial intelligence in education, remote proctoring, credentialing, research, and publication as they pertain to cardiovascular procedures. This review describes the benefits and limitations of artificial intelligence and machine learning and the exciting potential of integrating advanced simulation, holography, virtual reality, and extended reality into disease diagnosis and patient care, as well as their roles in cardiovascular research and education. Nonetheless, much of the available data resides in electronic medical records or within industry-sponsored proprietary programs that are not compatible or standardized for current clinical application. Many areas in cardiovascular medicine would benefit from the introduction or increased use of artificial intelligence. Web-based artificial intelligence applications could be used to address unmet needs for education, on-demand procedural proctoring, credentialing, and recredentialing for interventionists and physicians in remote locations. Further progress in artificial intelligence will require further collaboration among computer scientists and researchers in order to identify and correct the most relevant problems and to implement the best data-based approach to achieving this goal. The future success of artificial intelligence in cardiovascular medicine will depend on the degree of collaboration between all pertinent experts in this field. This will undoubtedly be a prolonged, stepwise process.
Topics: Artificial Intelligence; Credentialing; Forecasting; Humans; Machine Learning
PubMed: 35481865
DOI: 10.14503/THIJ-21-7572