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Fulltext Next-generation nanophotonic-enabled biosensors for intelligent diagnosis of SARS-CoV-2 variants

Taha BA, Al Mashhadany Y, Al-Jubouri Q, Rashid ARBA, Luo Y, Chen Z, et al.

Sci Total Environ, 2023 Jul 01;880:163333.
PMID: 37028663 DOI: 10.1016/j.scitotenv.2023.163333

Constantly mutating SARS-CoV-2 is a global concern resulting in COVID-19 infectious waves from time to time in different regions, challenging present-day diagnostics and therapeutics. Early-stage point-of-care diagnostic (POC) biosensors are a crucial vector for the timely management of morbidity and mortalities caused due to COVID-19. The state-of-the-art SARS-CoV-2 biosensors depend upon developing a single platform for its diverse variants/biomarkers, enabling precise detection and monitoring. Nanophotonic-enabled biosensors have emerged as 'one platform' to diagnose COVID-19, addressing the concern of constant viral mutation. This review assesses the evolution of current and future variants of the SARS-CoV-2 and critically summarizes the current state of biosensor approaches for detecting SARS-CoV-2 variants/biomarkers employing nanophotonic-enabled diagnostics. It discusses the integration of modern-age technologies, including artificial intelligence, machine learning and 5G communication with nanophotonic biosensors for intelligent COVID-19 monitoring and management. It also highlights the challenges and potential opportunities for developing intelligent biosensors for diagnosing future SARS-CoV-2 variants. This review will guide future research and development on nano-enabled intelligent photonic-biosensor strategies for early-stage diagnosing of highly infectious diseases to prevent repeated outbreaks and save associated human mortalities.

Matched MeSH terms: Artificial Intelligence
Fulltext Diagnosis of Schizophrenia Based on the Data of Various Modalities: Biomarkers and Machine Learning Techniques (Review)

Sharaev MG, Malashenkova IK, Maslennikova AV, Zakharova NV, Bernstein AV, Burnaev EV, et al.

Sovrem Tekhnologii Med, 2022;14(5):53-75.
PMID: 37181835 DOI: 10.17691/stm2022.14.5.06

Schizophrenia is a socially significant mental disorder resulting frequently in severe forms of disability. Diagnosis, choice of treatment tactics, and rehabilitation in clinical psychiatry are mainly based on the assessment of behavioral patterns, socio-demographic data, and other investigations such as clinical observations and neuropsychological testing including examination of patients by the psychiatrist, self-reports, and questionnaires. In many respects, these data are subjective and therefore a large number of works have appeared in recent years devoted to the search for objective characteristics (indices, biomarkers) of the processes going on in the human body and reflected in the behavioral and psychoneurological patterns of patients. Such biomarkers are based on the results of instrumental and laboratory studies (neuroimaging, electro-physiological, biochemical, immunological, genetic, and others) and are successfully being used in neurosciences for understanding the mechanisms of the emergence and development of nervous system pathologies. Presently, with the advent of new effective neuroimaging, laboratory, and other methods of investigation and also with the development of modern methods of data analysis, machine learning, and artificial intelligence, a great number of scientific and clinical studies is being conducted devoted to the search for the markers which have diagnostic and prognostic value and may be used in clinical practice to objectivize the processes of establishing and clarifying the diagnosis, choosing and optimizing treatment and rehabilitation tactics, predicting the course and outcome of the disease. This review presents the analysis of the works which describe the correlates between the diagnosis of schizophrenia, established by health professionals, various manifestations of the psychiatric disorder (its subtype, variant of the course, severity degree, observed symptoms, etc.), and objectively measured characteristics/quantitative indicators (anatomical, functional, immunological, genetic, and others) obtained during instrumental and laboratory examinations of patients. A considerable part of these works has been devoted to correlates/biomarkers of schizophrenia based on the data of structural and functional (at rest and under cognitive load) MRI, EEG, tractography, and immunological data. The found correlates/biomarkers reflect anatomic disorders in the specific brain regions, impairment of functional activity of brain regions and their interconnections, specific microstructure of the brain white matter and the levels of connectivity between the tracts of various structures, alterations of electrical activity in various parts of the brain in different EEG spectral ranges, as well as changes in the innate and adaptive links of immunity. Current methods of data analysis and machine learning to search for schizophrenia biomarkers using the data of diverse modalities and their application during building and interpretation of predictive diagnostic models of schizophrenia have been considered in the present review.

Matched MeSH terms: Artificial Intelligence
Fulltext Predicting compressive strength of eco-friendly plastic sand paver blocks using gene expression and artificial intelligence programming

Iftikhar B, Alih SC, Vafaei M, Javed MF, Rehman MF, Abdullaev SS, et al.

Sci Rep, 2023 Jul 27;13(1):12149.
PMID: 37500697 DOI: 10.1038/s41598-023-39349-2

Plastic sand paver blocks provide a sustainable alternative by using plastic waste and reducing the need for cement. This innovative approach leads to a more sustainable construction sector by promoting environmental preservation. No model or Equation has been devised that can predict the compressive strength of these blocks. This study utilized gene expression programming (GEP) and multi-expression programming (MEP) to develop empirical models to forecast the compressive strength of plastic sand paver blocks (PSPB) comprised of plastic, sand, and fibre in an effort to advance the field. The database contains 135 results for compressive strength with seven input parameters. The R2 values of 0.87 for GEP and 0.91 for MEP for compressive strength reveal a relatively significant relationship between predicted and actual values. MEP outperformed GEP by displaying a higher R2 and lower values for statistical evaluations. In addition, a sensitivity analysis was conducted, which revealed that the sand grain size and percentage of fibres play an essential part in compressive strength. It was estimated that they contributed almost 50% of the total. The outcomes of this research have the potential to promote the reuse of PSPB in the building of green environments, hence boosting environmental protection and economic advantage.

Matched MeSH terms: Artificial Intelligence
Fulltext The Use of ChatGPT for Education Modules on Integrated Pharmacotherapy of Infectious Disease: Educators' Perspectives

Al-Worafi YM, Goh KW, Hermansyah A, Tan CS, Ming LC

JMIR Med Educ, 2024 Jan 12;10:e47339.
PMID: 38214967 DOI: 10.2196/47339

BACKGROUND: Artificial Intelligence (AI) plays an important role in many fields, including medical education, practice, and research. Many medical educators started using ChatGPT at the end of 2022 for many purposes.
OBJECTIVE: The aim of this study was to explore the potential uses, benefits, and risks of using ChatGPT in education modules on integrated pharmacotherapy of infectious disease.
METHODS: A content analysis was conducted to investigate the applications of ChatGPT in education modules on integrated pharmacotherapy of infectious disease. Questions pertaining to curriculum development, syllabus design, lecture note preparation, and examination construction were posed during data collection. Three experienced professors rated the appropriateness and precision of the answers provided by ChatGPT. The consensus rating was considered. The professors also discussed the prospective applications, benefits, and risks of ChatGPT in this educational setting.
RESULTS: ChatGPT demonstrated the ability to contribute to various aspects of curriculum design, with ratings ranging from 50% to 92% for appropriateness and accuracy. However, there were limitations and risks associated with its use, including incomplete syllabi, the absence of essential learning objectives, and the inability to design valid questionnaires and qualitative studies. It was suggested that educators use ChatGPT as a resource rather than relying primarily on its output. There are recommendations for effectively incorporating ChatGPT into the curriculum of the education modules on integrated pharmacotherapy of infectious disease.
CONCLUSIONS: Medical and health sciences educators can use ChatGPT as a guide in many aspects related to the development of the curriculum of the education modules on integrated pharmacotherapy of infectious disease, syllabus design, lecture notes preparation, and examination preparation with caution.

Matched MeSH terms: Artificial Intelligence
Fulltext Towards hospital-on-chip supported by 2D MXenes-based 5th generation intelligent biosensors

Chaudhary V, Khanna V, Ahmed Awan HT, Singh K, Khalid M, Mishra YK, et al.

Biosens Bioelectron, 2023 Jan 15;220:114847.
PMID: 36335709 DOI: 10.1016/j.bios.2022.114847

Existing public health emergencies due to fatal/infectious diseases such as coronavirus disease (COVID-19) and monkeypox have raised the paradigm of 5th generation portable intelligent and multifunctional biosensors embedded on a single chip. The state-of-the-art 5th generation biosensors are concerned with integrating advanced functional materials with controllable physicochemical attributes and optimal machine processability. In this direction, 2D metal carbides and nitrides (MXenes), owing to their enhanced effective surface area, tunable physicochemical properties, and rich surface functionalities, have shown promising performances in biosensing flatlands. Moreover, their hybridization with diversified nanomaterials caters to their associated challenges for the commercialization of stability due to restacking and oxidation. MXenes and its hybrid biosensors have demonstrated intelligent and lab-on-chip prospects for determining diverse biomarkers/pathogens related to fatal and infectious diseases. Recently, on-site detection has been clubbed with solution-on-chip MXenes by interfacing biosensors with modern-age technologies, including 5G communication, internet-of-medical-things (IoMT), artificial intelligence (AI), and data clouding to progress toward hospital-on-chip (HOC) modules. This review comprehensively summarizes the state-of-the-art MXene fabrication, advancements in physicochemical properties to architect biosensors, and the progress of MXene-based lab-on-chip biosensors toward HOC solutions. Besides, it discusses sustainable aspects, practical challenges and alternative solutions associated with these modules to develop personalized and remote healthcare solutions for every individual in the world.

Matched MeSH terms: Artificial Intelligence
Resource recovery from hydroponic wastewaters using microalgae-based biorefineries: A circular bioeconomy perspective

Ajeng AA, Rosli NSM, Abdullah R, Yaacob JS, Qi NC, Loke SP

J Biotechnol, 2022 Dec 10;360:11-22.
PMID: 36272573 DOI: 10.1016/j.jbiotec.2022.10.011

As the world's population grows, it is necessary to rethink how countries throughout the world produce food in order to replace the conventional and unsustainable agricultural techniques. Microalgae cultivation using a nutrient-rich solution from hydroponic systems not only presents a novel approach to solving problems pertaining to the impact of the discharges on the natural environment but also provides a plethora of other biotechnological applications particularly in the productions of high value-added products and plants growth stimulants, which can be potentially assimilated into the circular bioeconomy (CBE) in the hydroponic sector. In this review, the potential and practicability of microalgae to be merged into hydroponics CBE are reviewed. Overall, the integration of microalgal biorefineries in hydroponics systems can be realized after considering their Technology Readiness Level and System Readiness Level beforehand. Several suggestions on strains and hydroponics system improvement using existing biotechnological tools, Artificial Intelligence (AI) and nanobiotechnology in support of the CBE will be covered.

Matched MeSH terms: Artificial Intelligence
Global variations in diagnostic guidelines for Barrett's esophagus

Kusano C, Singh R, Lee YY, Soh YSA, Sharma P, Ho KY, et al.

Dig Endosc, 2022 Nov;34(7):1320-1328.
PMID: 35475586 DOI: 10.1111/den.14342

Endoscopic diagnosis of gastroesophageal junction and Barrett's esophagus is essential for surveillance and early detection of esophageal adenocarcinoma and esophagogastric junction cancer. Despite its small size, the gastroesophageal junction has many inherent problems, including marked differences in diagnostic methods for Barrett's esophagus in international guidelines. To define Barrett's esophagus, gastroesophageal junction location should be clarified. Although gastric folds and palisade vessels are landmarks for identifying this junction, they are sometimes difficult to observe due to air entry or reflux esophagitis. The possibility of diagnosing a malignancy associated with Barrett's esophagus <1 cm, identified using palisade vessels, should be re-examined. Nontargeted biopsies of Barrett's esophagus are commonly used to detect intestinal metaplasia, dysplasia, and cancer as described in the Seattle protocol. Barrett's esophagus with intestinal metaplasia has a high risk of becoming cancerous. Furthermore, the frequency of cancer in patients with Barrett's esophagus without intestinal metaplasia is high, and the guidelines differ on whether to include the presence of intestinal metaplasia in the diagnosis of Barrett's esophagus. Use of advanced imaging technologies, including narrow-band imaging with magnifying endoscopy and linked color imaging, is reportedly valid for diagnosing Barrett's esophagus. Furthermore, artificial intelligence has facilitated the diagnosis of Barrett's esophagus through its deep learning and image recognition capabilities. However, it is necessary to first use the endoscopic definition of the gastroesophageal junction, which is common in all countries, and then elucidate the characteristics of Barrett's esophagus in each region, for example, length differences in the risk of carcinogenesis with and without intestinal metaplasia.

Matched MeSH terms: Artificial Intelligence
Computational Approaches in the Discovery and Development of Therapeutic and Prophylactic Agents for Viral Diseases

Gaurav A, Agrawal N, Al-Nema M, Gautam V

Curr Top Med Chem, 2022;22(26):2190-2206.
PMID: 36278463 DOI: 10.2174/1568026623666221019110334

Over the last two decades, computational technologies have played a crucial role in antiviral drug development. Whenever a virus spreads and becomes a threat to global health, it brings along the challenge of developing new therapeutics and prophylactics. Computational drug and vaccine discovery has evolved quickly over the years. Some interesting examples of computational drug discovery are anti-AIDS drugs, where HIV protease and reverse transcriptase have been targeted by agents developed using computational methods. Various computational methods that have been applied to anti-viral research include ligand-based methods that rely on known active compounds, i.e., pharmacophore modeling, machine learning or classical QSAR; structure-based methods that rely on an experimentally determined 3D structure of the targets, i.e., molecular docking and molecular dynamics and methods for the development of vaccines such as reverse vaccinology; structural vaccinology and vaccine epitope prediction. This review summarizes these approaches to battle viral diseases and underscores their importance for anti-viral research. We discuss the role of computational methods in developing small molecules and vaccines against human immunodeficiency virus, yellow fever, human papilloma virus, SARS-CoV-2, and other viruses. Various computational tools available for the abovementioned purposes have been listed and described. A discussion on applying artificial intelligence-based methods for antiviral drug discovery has also been included.

Matched MeSH terms: Artificial Intelligence
Smart systems in producing algae-based protein to improve functional food ingredients industries

Neo YT, Chia WY, Lim SS, Ngan CL, Kurniawan TA, Chew KW

Food Res Int, 2023 Mar;165:112480.
PMID: 36869493 DOI: 10.1016/j.foodres.2023.112480

Production and extraction systems of algal protein and handling process of functional food ingredients need to control several parameters such as temperature, pH, intensity, and turbidity. Many researchers have investigated the Internet of Things (IoT) approach for enhancing the yield of microalgae biomass and machine learning for identifying and classifying microalgae. However, there have been few specific studies on using IoT and artificial intelligence (AI) for production and extraction of algal protein as well as functional food ingredients processing. In order to improve the production of algal protein and functional food ingredients, the implementation of smart system is a must to have real-time monitoring, remote control system, quick response to sudden events, prediction and characterisation. Techniques of IoT and AI are expected to help functional food industries to have a big breakthrough in the future. Manufacturing and implementation of beneficial smart systems are important to provide convenience and to increase the efficiency of work by using the interconnectivity of IoT devices to have good capturing, processing, archiving, analyzing, and automation. This review investigates the possibilities of implementation of IoT and AI in production and extraction of algal protein and processing of functional food ingredients.

Matched MeSH terms: Artificial Intelligence
Fulltext Detection of Colorectal Polyps from Colonoscopy Using Machine Learning: A Survey on Modern Techniques

ELKarazle K, Raman V, Then P, Chua C

Sensors (Basel), 2023 Jan 20;23(3).
PMID: 36772263 DOI: 10.3390/s23031225

Given the increased interest in utilizing artificial intelligence as an assistive tool in the medical sector, colorectal polyp detection and classification using deep learning techniques has been an active area of research in recent years. The motivation for researching this topic is that physicians miss polyps from time to time due to fatigue and lack of experience carrying out the procedure. Unidentified polyps can cause further complications and ultimately lead to colorectal cancer (CRC), one of the leading causes of cancer mortality. Although various techniques have been presented recently, several key issues, such as the lack of enough training data, white light reflection, and blur affect the performance of such methods. This paper presents a survey on recently proposed methods for detecting polyps from colonoscopy. The survey covers benchmark dataset analysis, evaluation metrics, common challenges, standard methods of building polyp detectors and a review of the latest work in the literature. We conclude this paper by providing a precise analysis of the gaps and trends discovered in the reviewed literature for future work.

Matched MeSH terms: Artificial Intelligence
Fulltext Role of Artificial Intelligence in COVID-19 Detection

Gudigar A, Raghavendra U, Nayak S, Ooi CP, Chan WY, Gangavarapu MR, et al.

Sensors (Basel), 2021 Dec 01;21(23).
PMID: 34884045 DOI: 10.3390/s21238045

The global pandemic of coronavirus disease (COVID-19) has caused millions of deaths and affected the livelihood of many more people. Early and rapid detection of COVID-19 is a challenging task for the medical community, but it is also crucial in stopping the spread of the SARS-CoV-2 virus. Prior substantiation of artificial intelligence (AI) in various fields of science has encouraged researchers to further address this problem. Various medical imaging modalities including X-ray, computed tomography (CT) and ultrasound (US) using AI techniques have greatly helped to curb the COVID-19 outbreak by assisting with early diagnosis. We carried out a systematic review on state-of-the-art AI techniques applied with X-ray, CT, and US images to detect COVID-19. In this paper, we discuss approaches used by various authors and the significance of these research efforts, the potential challenges, and future trends related to the implementation of an AI system for disease detection during the COVID-19 pandemic.

Matched MeSH terms: Artificial Intelligence
Fulltext Automatic identification of hypertension and assessment of its secondary effects using artificial intelligence: A systematic review (2013-2023)

Gudigar A, Kadri NA, Raghavendra U, Samanth J, Maithri M, Inamdar MA, et al.

Comput Biol Med, 2024 Apr;172:108207.
PMID: 38489986 DOI: 10.1016/j.compbiomed.2024.108207

Artificial Intelligence (AI) techniques are increasingly used in computer-aided diagnostic tools in medicine. These techniques can also help to identify Hypertension (HTN) in its early stage, as it is a global health issue. Automated HTN detection uses socio-demographic, clinical data, and physiological signals. Additionally, signs of secondary HTN can also be identified using various imaging modalities. This systematic review examines related work on automated HTN detection. We identify datasets, techniques, and classifiers used to develop AI models from clinical data, physiological signals, and fused data (a combination of both). Image-based models for assessing secondary HTN are also reviewed. The majority of the studies have primarily utilized single-modality approaches, such as biological signals (e.g., electrocardiography, photoplethysmography), and medical imaging (e.g., magnetic resonance angiography, ultrasound). Surprisingly, only a small portion of the studies (22 out of 122) utilized a multi-modal fusion approach combining data from different sources. Even fewer investigated integrating clinical data, physiological signals, and medical imaging to understand the intricate relationships between these factors. Future research directions are discussed that could build better healthcare systems for early HTN detection through more integrated modeling of multi-modal data sources.

Matched MeSH terms: Artificial Intelligence
An emerging network for COVID-19 CT-scan classification using an ensemble deep transfer learning model

Yousefpanah K, Ebadi MJ, Sabzekar S, Zakaria NH, Osman NA, Ahmadian A

Acta Trop, 2024 Sep;257:107277.
PMID: 38878849 DOI: 10.1016/j.actatropica.2024.107277

Over the past few years, the widespread outbreak of COVID-19 has caused the death of millions of people worldwide. Early diagnosis of the virus is essential to control its spread and provide timely treatment. Artificial intelligence methods are often used as powerful tools to reach a COVID-19 diagnosis via computed tomography (CT) samples. In this paper, artificial intelligence-based methods are introduced to diagnose COVID-19. At first, a network called CT6-CNN is designed, and then two ensemble deep transfer learning models are developed based on Xception, ResNet-101, DenseNet-169, and CT6-CNN to reach a COVID-19 diagnosis by CT samples. The publicly available SARS-CoV-2 CT dataset is utilized for our implementation, including 2481 CT scans. The dataset is separated into 2108, 248, and 125 images for training, validation, and testing, respectively. Based on experimental results, the CT6-CNN model achieved 94.66% accuracy, 94.67% precision, 94.67% sensitivity, and 94.65% F1-score rate. Moreover, the ensemble learning models reached 99.2% accuracy. Experimental results affirm the effectiveness of designed models, especially the ensemble deep learning models, to reach a diagnosis of COVID-19.

Matched MeSH terms: Artificial Intelligence
Fulltext A novel voice in head actor critic reinforcement learning with human feedback framework for enhanced robot navigation

Sharma A, Balasundaram A, Shaik A, Vaithilingam CA

Sci Rep, 2025 Feb 28;15(1):7237.
PMID: 40021934 DOI: 10.1038/s41598-025-92252-w

This work presents a novel Voice in Head (ViH) framework, that integrates Large Language Models (LLMs) and the power of semantic understanding to enhance robotic navigation and interaction within complex environments. Our system strategically combines GPT and Gemini powered LLMs as Actor and Critic components within a reinforcement learning (RL) loop for continuous learning and adaptation. ViH employs a sophisticated semantic search mechanism powered by Azure AI Search, allowing users to interact with the system through natural language queries. To ensure safety and address potential LLM limitations, the system incorporates a Reinforcement Learning with Human Feedback (RLHF) component, triggered only when necessary. This hybrid approach delivers impressive results, achieving success rates of up to 94.54%, surpassing established benchmarks. Most importantly, the ViH framework offers a modular and scalable architecture. By simply modifying the environment, the system demonstrates the potential to adapt to diverse application domains. This research provides a significant advancement in the field of cognitive robotics, paving the way for intelligent autonomous systems capable of sophisticated reasoning and decision-making in real-world scenarios bringing us one step closer to achieving Artificial General Intelligence.

Matched MeSH terms: Artificial Intelligence
Recent advances in non-invasive techniques for assessing food quality: Applications and innovations

Jimoh KA, Hashim N

Adv Food Nutr Res, 2025;114:301-352.
PMID: 40155087 DOI: 10.1016/bs.afnr.2024.09.006

The global concern for ensuring the safety and authenticity of high-quality food necessitates continuous advancements in food assessment technologies. While conventional methods of food assessment are accurate and precise, they are also laborious, destructive, time-consuming, energy-intensive, chemical-demanding, and less eco-friendly. Their reliability diminishes when dealing with large numbers of food samples. This chapter explores recent advances in non-invasive technologies for food quality assessment, including spectroscopy, optical imaging, and e-sensors. Enhanced by artificial intelligence (AI), these technologies have shown remarkable capabilities in rapid and accurate food identification, authentication, physical appraisal, early disease detection, chemical analysis, and biochemical evaluation. As a result, non-invasive technology holds the potential to revolutionize food quality assessment and assure food safety at every stage of the food supply chain.

Matched MeSH terms: Artificial Intelligence
Fulltext Systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in terms of evaluation and benchmarking: Taxonomy analysis, challenges, future solutions and methodological aspects

Albahri OS, Zaidan AA, Albahri AS, Zaidan BB, Abdulkareem KH, Al-Qaysi ZT, et al.

J Infect Public Health, 2020 Oct;13(10):1381-1396.
PMID: 32646771 DOI: 10.1016/j.jiph.2020.06.028

This study presents a systematic review of artificial intelligence (AI) techniques used in the detection and classification of coronavirus disease 2019 (COVID-19) medical images in terms of evaluation and benchmarking. Five reliable databases, namely, IEEE Xplore, Web of Science, PubMed, ScienceDirect and Scopus were used to obtain relevant studies of the given topic. Several filtering and scanning stages were performed according to the inclusion/exclusion criteria to screen the 36 studies obtained; however, only 11 studies met the criteria. Taxonomy was performed, and the 11 studies were classified on the basis of two categories, namely, review and research studies. Then, a deep analysis and critical review were performed to highlight the challenges and critical gaps outlined in the academic literature of the given subject. Results showed that no relevant study evaluated and benchmarked AI techniques utilised in classification tasks (i.e. binary, multi-class, multi-labelled and hierarchical classifications) of COVID-19 medical images. In case evaluation and benchmarking will be conducted, three future challenges will be encountered, namely, multiple evaluation criteria within each classification task, trade-off amongst criteria and importance of these criteria. According to the discussed future challenges, the process of evaluation and benchmarking AI techniques used in the classification of COVID-19 medical images considered multi-complex attribute problems. Thus, adopting multi-criteria decision analysis (MCDA) is an essential and effective approach to tackle the problem complexity. Moreover, this study proposes a detailed methodology for the evaluation and benchmarking of AI techniques used in all classification tasks of COVID-19 medical images as future directions; such methodology is presented on the basis of three sequential phases. Firstly, the identification procedure for the construction of four decision matrices, namely, binary, multi-class, multi-labelled and hierarchical, is presented on the basis of the intersection of evaluation criteria of each classification task and AI classification techniques. Secondly, the development of the MCDA approach for benchmarking AI classification techniques is provided on the basis of the integrated analytic hierarchy process and VlseKriterijumska Optimizacija I Kompromisno Resenje methods. Lastly, objective and subjective validation procedures are described to validate the proposed benchmarking solutions.

Matched MeSH terms: Artificial Intelligence/standards*
Healthcare knowledge acquisition: an ontology-based approach using the extensible markup language (XML)

Cheah YN, Abidi SS

Stud Health Technol Inform, 2000;77:827-31.
PMID: 11187669

The healthcare enterprise requires a great deal of knowledge to maintain premium efficiency in the delivery of quality healthcare. We employ Knowledge Management based knowledge acquisition strategies to procure 'tacit' healthcare knowledge from experienced healthcare practitioners. Situational, problem-specific Scenarios are proposed as viable knowledge acquisition and representation constructs. We present a healthcare Tacit Knowledge Acquisition Info-structure (TKAI) that allows remote healthcare practitioners to record their tacit knowledge. TKAI employs (a) ontologies for standardisation of tacit knowledge and (b) XML to represent scenario instances for their transfer over the Internet to the server-side Scenario-Base and for the global sharing of acquired tacit healthcare knowledge.

Matched MeSH terms: Artificial Intelligence*
Transforming XML-based electronic patient records for use in medical case based reasoning systems

Abidi SS, Manickam S

Stud Health Technol Inform, 2000;77:709-13.
PMID: 11187645

Electronic patient records (EPR) can be regarded as an implicit source of clinical behaviour and problem-solving knowledge, systematically compiled by clinicians. We present an approach, together with its computational implementation, to pro-actively transform XML-based EPR into specialised Clinical Cases (CC) in the realm of Medical Case Base Systems. The 'correct' transformation of EPR to CC involves structural, terminological and conceptual standardisation, which is achieved by a confluence of techniques and resources, such as XML, UMLS (meta-thesaurus) and medical knowledge ontologies. We present below the functional architecture of a Medical Case-Base Reasoning Info-Structure (MCRIS) that features two distinct, yet related, functionalities: (1) a generic medical case-based reasoning system for decision-support activities; and (2) an EPR-CC transformation system to transform typical EPR's to CC.

Matched MeSH terms: Artificial Intelligence*
A Critical Review for Developing Accurate and Dynamic Predictive Models Using Machine Learning Methods in Medicine and Health Care

Alanazi HO, Abdullah AH, Qureshi KN

J Med Syst, 2017 Apr;41(4):69.
PMID: 28285459 DOI: 10.1007/s10916-017-0715-6

Recently, Artificial Intelligence (AI) has been used widely in medicine and health care sector. In machine learning, the classification or prediction is a major field of AI. Today, the study of existing predictive models based on machine learning methods is extremely active. Doctors need accurate predictions for the outcomes of their patients' diseases. In addition, for accurate predictions, timing is another significant factor that influences treatment decisions. In this paper, existing predictive models in medicine and health care have critically reviewed. Furthermore, the most famous machine learning methods have explained, and the confusion between a statistical approach and machine learning has clarified. A review of related literature reveals that the predictions of existing predictive models differ even when the same dataset is used. Therefore, existing predictive models are essential, and current methods must be improved.

Matched MeSH terms: Artificial Intelligence*
Technology, knowledge systems, population dynamics, and coastal ecosystems

Williams MJ

Ambio, 2002 Jun;31(4):337-9.
PMID: 12174604
Matched MeSH terms: Artificial Intelligence*

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