Anaemia: A Scientific Overview on Types and Recent Advancements in the Detection of Anaemia.

The accounting and auditing profession is undergoing a significant transformation driven by the rapid advancement and incorporation of artificial intelligence (AI) and machine learning (ML) tools. These innovative technologies have the potential to automate mundane tasks, improve data analysis capabilities, and enhance decision-making processes, fundamentally changing how accounting and auditing professionals carry out their responsibilities. While these advancements present challenges and opportunities, they also require a reevaluation of skills, knowledge, and ethical considerations for the profession to flourish in this evolving environment. This paper investigates the expectations of accounting and auditing students regarding the impact of artificial intelligence (AI) and machine learning (ML) tools on their future professions. Data was collected through a structured survey questionnaire completed by a sample of 98 students. Statistical methods, including regression analysis, were used to analyze the collected data. The results indicate that students are generally optimistic about the potential of these technologies to enhance efficiency, accuracy, and decision-making in accounting and auditing. However, they also express apprehensions about job security and the necessity for continuous learning to adapt to technological advancements.This research contributes to the literature by offering insights into the expectations of future professionals in the accounting and auditing domain and emphasizing the significance of preparing students for the evolving technological landscape. The study’s findings underline the importance of aligning educational programs with the demands of AI and ML integration to ensure the readiness of future practitioners.

Artificial intelligence (AI) and machine learning (ML) tools play a significant role in the recent evolution of smart systems. AI solutions are pushing towards a significant shift in many fields such as healthcare, autonomous airplanes and vehicles, security, marketing customer profiling and other diverse areas. One of the main challenges hindering the AI potential is the demand for high-performance computation resources. Recently, hardware accelerators are developed in order to provide the needed computational power for the AI and ML tools. In the literature, hardware accelerators are built using FPGAs, GPUs and ASICs to accelerate computationally intensive tasks. These accelerators provide high-performance hardware while preserving the required accuracy. In this work, we present a systematic literature review that focuses on exploring the available hardware accelerators for the AI and ML tools. More than 169 different research papers published between the years 2009 and 2019 are studied and analysed.

Focus issue: Artificial intelligence in medical physics.

Performance of an artificial intelligence tool with real-time clinical workflow integration - Detection of intracranial hemorrhage and pulmonary embolism.

The Value of Artificial Intelligence for Healthcare Decision Making—Lessons Learned

Artificial intelligence (AI) and machine learning (ML) tools have been used across all spectrums of the drug development process. The US Food and Drug Administration has accepted over 100 submissions that cite the use of AI that has assisted with clinical trial recruitment, drug discovery, and dose optimization. With these insights in mind, health authorities expect the use of AI and ML tools in regulatory processes and is working to provide meaningful oversight. In medical writing, AI and ML sources have been essential to the writing process by formatting documents, designing study protocols, analyzing manuscripts, and communicating complex scientific information to lay audiences. The technological advances of AI and ML are rapidly evolving, and there is indeed a place for such technologies in the medical writer’s resource toolbox.

Risk prediction plays an important role in clinical transplantation research. Traditionally, most risk models have been based on regression models.1 Although useful to help understand relationships between predictors and outcomes, these statistical methods can typically evaluate only a small number of predictors, which are assumed to affect everyone in the same way, and uniformly throughout the participants' lifespan. These methods have several limitations,2 including the inability to analyze nonlinear relationships, the requirement of setting a level of binary significance, impracticality for analyzing large datasets, and vulnerability to bias secondary to variable selection and/or omission of relevant confounders. With the emergence of P4 (Predictive, Preventive, Personalized, and Participatory) and Precision Medicine, artificial intelligence and machine learning methods have come to attention as methods aimed at solving the challenges in analysis not well addressed by regression approaches. Machine learning methods provide algorithms to understand patterns from large, complex, and heterogeneous data.3 Of the machine learning methods, recursive partitioning, and especially random forests, can deal with large numbers of predictor variables even in the presence of complex interactions.2,4 These methods have been applied successfully in genetics, clinical research, and bioinformatics. In this issue of Transplantation, Scheffner et al report on the development and internal validation of a random forest prediction model for patient survival.5 Random forest models are composed of a collection of decision trees. In the process of building each decision tree, different random subsets of the variables from the training dataset are selected to establish how best to partition the dataset at each node.6 Random forest models are considered less vulnerable to overfitting the training dataset given the large number of trees built, making each tree an independent model. The lower likelihood of bias is a result of bootstrapping several trees over randomly selected subsets of variables and subsamples of data.6 Random forest models require little preprocessing of data; the data need not be normalized; and the approach is resilient to outliers. While missing data will be a challenge when trying to draw clinical inferences from standard statistical models, machine learning methods tend to make fewer assumptions about the underlying data and, thus, are less vulnerable to the challenges associated with violation of those assumptions. Relying on fewer assumptions than regression analysis, machine learning methods have been shown to deliver more robust predictions. Scheffner and colleagues5 split a retrospective cohort of kidney transplant recipients with posttransplantation protocol biopsies into training and validation datasets (Figure 2A and B). Using all pretransplant and 3- and 12-months posttransplant variables, the obtained models showed good performance to predict death (concordance index: 0.77–0.78). Validation showed a concordance index of 0.76 and good discrimination of risks by the models, despite substantial differences in clinical variables and the derivation dataset representing an earlier era (2000–2007) than the validation dataset (2008–2013). To contrast with outputs of multivariable regression models using the same datasets, see Tables 2 and 3 and nomograms predicting mortality risk using estimators from multivariable Cox models (Figure 3) in Abeling et al.7 Random survival forests also inform on the importance of descriptive variables.6 Scheffner found the potentially modifiable (and highly correlated) graft rejection treatment and urinary tract infection to be important predictors of patient survival in addition to established factors like age, cardiovascular disease, diabetes, and graft function (Figure 3A and B).5 Many of the predictors retained in multivariable regression models7 were also deemed important in random forest survival analyses.5 To validate selected predictors and model construction, it is important to pursue external validation with independent datasets. Random survival forests may complement regression analyses when handling highly correlated complex survival data. Opportunities for application (and limitations) of each of the regression and random survival forests for prediction are summarized in Table 1.TABLE 1.: Regression and random survival forests for survival analysisPredictive models in transplantation and donation help risk stratify patients and could improve quality of healthcare delivery as well as patient outcomes. The increasing interest in these tools warrants a better understanding of their challenges and limitations.8 First, highly predictive variables may not necessarily be causally related to the outcomes of interest. Second, the success of machine learning models depends on the relationship between predictors and outcome being represented in training/validation datasets, the number of observations and features, selection and parameterization of features, and the algorithm chosen for the model. Careful variable definition (eg, urinary tract infection) is necessary. Presence of highly correlated linear and nonlinear relationships between independent variables may warrant mechanisms for removal of the correlated variables. Model performance may also be compromised when studying rare outcomes.4 Inevitably, generalizability of machine learning models may be limited when the clinical context, local factors (including patient/physician preferences, health systems, and care standards), and therapeutic strategies vary. To enable assessment of model validity, correct interpretation of model outputs, replication, and future knowledge synthesis, it is vital that the transplantation and donation community promote adherence to guidelines on the dissemination and reporting of machine learning models.8,9 Authors should be encouraged to report all model parameters, transformations applied to raw data, sampling methods, and random number generator seeds. Whenever possible, algorithms and associated code should be released in public software archive domains. There is a need for new models of health data ownership with rights to the individual, highly secure data repositories, government legislation for data sharing, and usage policies to ensure privacy and data security. Moreover, with wide uptake of machine learning and artificial intelligence tools, the scale of iatrogenic risks and liabilities related to their application, in contrast to the implications of a single doctor's mistake for a given patient, also warrant assessment.10 Most practice guidelines are geared toward the "average patient." Machine learning tools can capture the complexity of individual patients' characteristics and aid transplant clinicians with patient-specific care decisions. As these tools become more prevalent, it is important to develop best practice guidelines and ensure there is regulatory oversight on their development and application.

Subsea tiebacks are a principal building block of deepwater developments. Operators seek to continuously enhance operational efficiencies and tieback distances while reducing capital, operating expenditures, and emissions. Rapid advances have been made in remote operations, subsea equipment capabilities and standardization. However, significant additional improvements in subsea tieback operational efficiencies are achievable by leveraging the steep decline in data storage and processing costs, the massive increase in processing power and high-speed internet along with the availability of proven Artificial Intelligence (AI) and Machine Learning (ML) tools. Subsea tieback operational efficiency improvements are bottlenecked by human operator ability to process and respond in a timely manner to the overwhelming quantity of data collected by modern subsea monitoring and sensor technologies. This paper will address specific areas where the proven ability of AI and ML tools to assimilate large quantities of data, together with bespoke algorithms, can provide real-time, targeted recommendations to unlock the following improvements in subsea tieback operational efficiencies: Enhanced Oil Recovery: With real-time detection and analyses of changes and anomalies in production flow, AI can use Model Predictive Control (MPC) or Hybrid AI-Physics Models to optimize production rates, riser base gas lift, and gas or water injection systems. Predictive Maintenance: By ingesting and analyzing real-time sensor and Autonomous Underwater Vehicle (AUV)/Remote Operated Vehicle (ROV) data, AI algorithms can:Improve performance and reliability of subsea boosting systems (multiphase pumps, power generation and conditioning).Increase operational uptime and service life by anticipating potential failures in subsea infrastructure. Operational Efficiencies: AI can process vast amounts of data from disparate sources to support decision-making related to:Monitoring and predicting flow assurance challenges while suggesting targeted mitigations.Maintaining optimum production rates and flow conditions from wellhead to production manifold.Providing a holistic view of complex multi-tieback systems to facilitate decision-making targeting total asset performance. By combining data gathered from multiple sources (SCADA, PLC, Camera DVR, etc.) with bespoke algorithms, AI can provide diagnostic, prescriptive, intelligent insights; accelerate positive interventions; increase ultimate recovery while reducing downtime, power requirements, and emissions. The offshore oil and gas industry is embracing AI to make operations safer and more efficient. This paper will show how an integrated, holistic and targeted approach of incorporating AI into subsea tiebacks will enable the industry to immediately and inexpensively attain new levels of operational excellence, cost-effectiveness, and environmental sustainability.

Artificial intelligence (AI) is lowering the period and cost of the medication research and discovery process Artificial Intelligence is a revolution of medical research in pharmaceutical companies. In a review article, we give a summary of the many artificial intelligence tools of machine learning (ML) and deep learning (DL) techniques that will be used in drug research and discovery in the future. AI techniques and tools are more specifically designed or better programmed to mimic the operations of the human brain. AI is frequently used in drug discovery for de novo drug creation, virtual screening, reaction prediction, and de novo protein design. In addition, the application of AI and techniques of AI. ML is a disease diagnosis, de novo drug design, drug prediction for diseases, and big data prediction using ANN, CNN, and SNN, as well as deep learning. Furthermore, the function, applications, and methods of AI. Technological hurdles also face the contemporary XAI, and “low level” molecular representations (such as SMILES strings) that are useful for machine learning and AI tools are ‘deep chem’ in drug development several cutting-edge methods referred to as Knowledge Base Systems (KBS). The AI-based nanorobots are drug discovery on creating implantable nanorobots for the targeted delivery of medications and genes, factors including sustained release, dose modification, and control release need to be taken into consideration. Finally recent development of ML and DL techniques and AI models are more useful in the drug development and drug discovery process.

Recent advances in the fields of artificial intelligence and machine learning have paved a way in solving the unsolved problems embarking into a new dimension, especially, when there is increase in complexity of molecules. Reports have shown the necessity to employ these techniques to address the environmental problems. Herein we report the CO2 sequestration process by means of artificial intelligence (AI) and machine learning (ML) tools. The AI and ML approaches adopted enhance the accuracy of the results and at the same time give scope to explore new strategies in understanding the CO2 sequestration process. Herein we considered the reported active compounds observed in traditional medicinal plants like Oscimum, Azadiracta, Psidium and Ficus leaves and Curcuma and, their interactions with CO2. The crystal structures of the active compounds, collected from NCBI portal, are used for all the calculations. To understand the probable interactions of CO2 with active components AI tool IBMRXN was used and the properties of molecules are evaluated. ML techniques are employed using density functional theory method. Keeping in view the complexity of the molecules, optimization of the molecules is carried out at M062X/6-31G(d) level of theory. HOMO-LUMO energy gaps and binding energies are calculated at M062X/6-311+G(d,p)//M062X/6-31G(d) level of theory.

The widespread deployment of artificial intelligence (AI) and machine learning tools has created a shift in knowledge culture. The marginalisation of slower, more traditional modes of engagement for quantifiable data easily parsed by mathematical algorithms has resulted in prioritising proprietary or opaque datasets (knowledge) explicitly constructed with measurable parameters. Well-documented concerns persist regarding the narrow range of human data used by algorithmic tools, data that arguably encapsulates the many failures of human society. The inevitable result of the use and priority of this data, alongside very particular notions of value and what is valuable, is a replication of many of the foibles of our history as a species.Cultural practice in general necessitates the communication of what drives our hopes and underlies our experiences. In algorithmic times we can see that this kind of communication supports some of the many critiques of AI and machine learning already extant in activist circles. Through investigating some of the theoretical backgrounds of this resistance, this article uses the first iteration of HEXORCISMOS’S SEMILLA AI project and the resulting album release as one of the many possible ways in which we might use machine learning and AI tools alongside very deliberate and uplifting models of community and community building.

The increasing threat of antimicrobial resistance has prompted a need for more effective antimicrobial stewardship programs (AMS). Artificial intelligence (AI) and machine learning (ML) tools have emerged as potential solutions to enhance decision-making and improve patient outcomes in AMS. This systematic review and meta-analysis aims to evaluate the impact of AI in AMS and to assess its predictive performance and diagnostic accuracy. We conducted a comprehensive literature search across PubMed/MEDLINE, Scopus, EMBASE, and Web of Science to identify studies published up to July 2024. Studies included were observational, cohort, or retrospective, focusing on the application of AI/ML in AMS. The outcomes assessed were the area under the curve (AUC), accuracy, sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). We calculated the mean pooled effect size (ES) and its 95% confidence interval (CI) using a random-effects model. The risk of bias was assessed using the QUADAS-AI tool, and the protocol was registered in PROSPERO. Out of 3,458 retrieved articles, 80 studies met the inclusion criteria. Our meta-analysis demonstrated that ML models exhibited strong predictive performance and diagnostic accuracy, with the following results: AUC [ES: 72.28 (70.42-74.14)], accuracy [ES: 74.97 (73.35-76.58)], sensitivity [ES: 76.89; (71.90-81.89)], specificity [ES: 73.77; (67.87-79.67)], NPV [ES:79.92 (76.54-83.31)], and PPV [ES: 69.41 (60.19-78.63)] across various AMS settings. AI and ML tools offer promising enhancements due to their strong predictive performance. The integration of AI into AMS could lead to more precise antimicrobial prescribing, reduced antimicrobial resistance, and better resource utilization.

The Indian Human Resource (HR) market is undergoing a significant transformation with the advent of Artificial Intelligence (AI) and Machine Learning (ML) tools. This research paper explores the current state of the Indian HR market, the integration of AI and ML in HR practices, and the resultant impact on growth and development. Through comprehensive analysis and case studies, this paper highlights the opportunities and challenges of adopting AI and ML in HR and provides strategic recommendations for HR professionals in India to leverage these technologies for sustainable growth.

Recent developments across various scientific fields have revolutionized biology through the advent of omics technologies. Multi-omics, an emerging field that integrates different omics data types, offers more comprehensive insights into biological processes than any single omics approach alone. Currently, many researchers routinely incorporate omics technologies such as proteomics and metabolomics into their studies to achieve a deeper understanding of complex biological systems. As technological advancements continue, the cost of omics research is expected to decline, making large datasets more accessible to the scientific community. The vast amount of data generated by omics technologies can be immense, necessitating advanced computational techniques for analysis. To address this challenge, scientists have developed artificial intelligence (AI) and machine learning (ML) tools capable of analyzing these datasets, extracting significant biological information, and enhancing understanding of both normal and disease processes. AI and ML contribute to omics research by offering powerful capabilities in parameter selection, dimensionality reduction, complexity management, feature identification, pattern recognition, classification, and predictive modeling. By combining these cutting-edge approaches, researchers can create robust analytical frameworks that effectively manage the complexities of biological systems, thereby overcoming the limitations of conventional phenotyping techniques. This review aims to provide a comprehensive overview of the application of AI and ML tools in omics research, highlighting their potential to revolutionize understanding of biology

The article investigates the role and effectiveness of artificial intelligence tools in modern software testing processes, the main areas of their application, and their significance for ensuring the reliability, security, and efficiency of software. With the rapid development of internet technologies, the effective development of web applications has become extremely important, necessitating the implementation of new methods in the software testing process. Objective. To investigate and summarize the role and effectiveness of artificial intelligence (AI) tools in web application testing within modern software development processes, and to determine their role in ensuring the reliability, security, and efficiency of web applications. Methodology. The study employs a review of internet publications and literature, as well as an analysis of existing artificial intelligence tools that can be applied in software testing. Scientific Novelty. The study underscores the necessity of employing artificial intelligence in software testing in response to the increasing demands for efficiency in software testing. It highlights the importance of training testers to utilize the latest tools in their work. Conclusions. The demands for efficiency in modern software development have become exceptionally high, making the use of only manual testing with classical approaches increasingly ineffective. The employment of test automation tools, along with the application of machine learning and artificial intelligence tools, is essential for enhancing the efficiency of testing processes and optimizing resources. Development and testing engineering teams require the implementation of the latest tools. Cloud technologies, DevOps tools, and artificial intelligence are transforming testing approaches. With the growing availability of AI tools, engineers can work more efficiently and ensure high testing effectiveness.