Artificial Intelligence Strategies Research Articles

Enhanced Patient-Centricity: How the Biopharmaceutical Industry Is Optimizing Patient Care through AI/ML/DL.

Technologies utilizing cutting-edge methodologies, including artificial intelligence (AI), machine learning (ML) and deep learning (DL), present powerful opportunities to help evaluate, predict, and improve patient outcomes by drawing insights from real-world data (RWD) generated during medical care. They played a role during and following the Coronavirus Disease 2019 (COVID-19) pandemic by helping protect healthcare providers, prioritize care for vulnerable populations, predict disease trends, and find optimal therapies. Potential applications across therapeutic areas include diagnosis, disease management and patient journey mapping. Use of fit-for-purpose datasets for ML models is seeing growth and may potentially help additional enterprises develop AI strategies. However, biopharmaceutical companies often face specific challenges, including multi-setting data, system interoperability, data governance, and patient privacy requirements. There remains a need for evolving regulatory frameworks, operating models, and data governance to enable further developments and additional research. We explore recent literature and examine the hurdles faced by researchers in the biopharmaceutical industry to fully realize the promise of AI/ML/DL for patient-centric purposes.

Ethics and diversity in artificial intelligence policies, strategies and initiatives

A burgeoning of Artificial Intelligence (AI) technologies in recent years has led to increased discussion about its potential to address many issues considered otherwise intractable, including those highlighted by the United Nations 2030 Agenda for Sustainable Development and associated Sustainable Development Goals. In tandem with this growth in AI is an expanding body of documentation regarding how such advanced technologies should be governed and managed. Issued by a variety of sources and comprising frameworks, policies and guidelines, this body of work encompasses the legal, social, ethical and policy issues around AI. With at least 470 such documents identified, as of May 2021, in the Council of Europe’s tracker of AI initiatives, questions are emerging around the diversity of views expressed, especially regarding the influence of the Global North or Euro-American perspectives. Our previous analysis of a corpus of largely grey literature discovered blind spots regarding both gender representation and perspectives from the Global South. Expanding on that work, this paper examines a significantly extended corpus, with a focus on the role of underrepresented groups in the wider AI discourse. We find that voices from the Global South and consideration of alternative ethical approaches are largely absent from the conversation. In light of the prominence of social, cultural and ethical perspectives from the Global North, this paper explores implications for the development of standards for ethical AI. Concluding by offering approaches to incorporate more diverse ethical viewpoints and beliefs, we call for increased consideration of power structures when developing AI ethics policies and standards within these alternative socio-cultural and socio-economic contexts.

Artificial intelligence for multimodal data integration in oncology.

In oncology, the patient state is characterized by a whole spectrum of modalities, ranging from radiology, histology, and genomics to electronic health records. Current artificial intelligence (AI) models operate mainly in the realm of a single modality, neglecting the broader clinical context, which inevitably diminishes their potential. Integration of different data modalities provides opportunities to increase robustness and accuracy of diagnostic and prognostic models, bringing AI closer to clinical practice. AI models are also capable of discovering novel patterns within and across modalities suitable for explaining differences in patient outcomes or treatment resistance. The insights gleaned from such models can guide exploration studies and contribute to the discovery of novel biomarkers and therapeutic targets. To support these advances, here we present a synopsis of AI methods and strategies for multimodal data fusion and association discovery. We outline approaches for AI interpretability and directions for AI-driven exploration through multimodal data interconnections. We examine challenges in clinical adoption and discuss emerging solutions.

Viruses Broaden the Definition of Life by Genomic Incorporation of Artificial Intelligence and Machine Learning Processes.

Viruses have been classified as non-living because they require a cellular host to support their replicative processes. Empirical investigations have significantly advanced our understanding of the many strategies employed by viruses to usurp and divert host regulatory and metabolic processes to drive the synthesis and release of infectious particles. The recent emergence of SARS-CoV-2 has permitted us to evaluate and discuss a potentially novel classification of viruses as living entities. The ability of SARS CoV-2 to engender comprehensive regulatory control of integrative cellular processes is strongly suggestive of an inherently dynamic informational registry that is programmatically encoded by linear ssRNA sequences responding to distinct evolutionary constraints. Responses to positive evolutionary constraints have resulted in a single-stranded RNA viral genome that occupies a threedimensional space defined by conserved base-paring resulting from a complex pattern of both secondary and tertiary structures. Additionally, regulatory control of virus-mediated infectious processes relies on extensive protein-protein interactions that drive conformational matching and shape recognition events to provide a functional link between complementary viral and host nucleic acid and protein domains. We also recognize that the seamless integration of complex replicative processes is highly dependent on the precise temporal matching of complementary nucleotide sequences and their corresponding structural and non-structural viral proteins. Interestingly, the deployment of concerted transcriptional and translational activities within targeted cellular domains may be modeled by artificial intelligence (AI) strategies that are inherently fluid, self-correcting, and adaptive at accommodating temporal changes in host defense mechanisms. An in-depth understanding of multiple self-correcting AIassociated viral processes will most certainly lead to novel therapeutic development platforms, notably the design of efficacious neuropharmacological agents to treat chronic CNS syndromes associated with long-COVID. In summary, it appears that viruses, notably SARS-CoV-2, are very much alive due to acquired genetic advantages that are intimately entrained to existential host processes via evolutionarily constrained AI-associated learning paradigms.

Integrating futures imaginaries, expectations and anticipatory practices: practitioners of artificial intelligence between now and future

ABSTRACT Artificial intelligence (AI) is a world-changing technology due to its abilities to learn independently, process big data, and automate human work. Imagining the socio-technical future is necessary, but challenging, in the era of AI that rapidly developing technology has made turbulent. In this study, we addressed the need to understand the ways AI practitioners actualise their AI-related futures imaginaries at the grassroots level and in the present. Our empirical case study concerned Finland and Singapore, focussing on their AI strategies based on interviews with 26 AI practitioners. We created a new conceptual perspective by integrating three concepts: futures imaginaries, expectations, and anticipatory practices. We showed that imagining socio-technical futures is an ongoing process in which AI practitioners repeatedly co-constitute the future in ‘the now’. These practitioners interpret futures imaginaries as expectations and address AI-related challenges via anticipatory practices. Whereas some AI practitioners ‘ride the wave’ of the AI hype, others are proactive and critically active in developing and educating people on AI. AI practitioners hold significant anticipatory agency that is actualised in the anticipatory zone.

Precision Medicine Approaches with Metabolomics and Artificial Intelligence.

Recent technological innovations in the field of mass spectrometry have supported the use of metabolomics analysis for precision medicine. This growth has been allowed also by the application of algorithms to data analysis, including multivariate and machine learning methods, which are fundamental to managing large number of variables and samples. In the present review, we reported and discussed the application of artificial intelligence (AI) strategies for metabolomics data analysis. Particularly, we focused on widely used non-linear machine learning classifiers, such as ANN, random forest, and support vector machine (SVM) algorithms. A discussion of recent studies and research focused on disease classification, biomarker identification and early diagnosis is presented. Challenges in the implementation of metabolomics–AI systems, limitations thereof and recent tools were also discussed.

Edge intelligence-based proposal for onboard catenary stagger amplitude diagnosis

In recent years, the integration of Digital Twins (DT) for the adoption of smarter maintenance strategies has grown exponentially in different industrial sectors. New IoT and edge computing systems are being developed for this purpose, however, there are still some open issues and challenges to be solved. Firstly, this paper presents new approaches to the initial dependencies of the studied solution and make a new proposal to improve the interoperability of the presented system. Secondly, this paper provides a methodology applicable to similar developments of edge-based AI (Artificial Intelligence) solution, which comprises of four phases: the presentation of the multi-objective problem and the pre-selection of AI-based models, the description of the evaluation architecture, the profiling of the different models for the selection of the most suitable one and explainable AI strategies for getting insights of the selected model. Finally, it presents a use case of an edge-solution for the railway catenary geometry diagnostic (stagger amplitude of the overhead wire), saving the interoperability of the message exchange with other systems is provided.

Application of Artificial Intelligence Techniques to Predict Risk of Recurrence of Breast Cancer: A Systematic Review.

Breast cancer is the most common disease among women, with over million new diagnoses each year worldwide. About of patients initially presenting with early stage disease have a recurrence of cancer within 10 years. Predicting who will have a recurrence and who will not remains challenging, with consequent implications for associated treatment. Artificial intelligence strategies that can predict the risk of recurrence of breast cancer could help breast cancer clinicians avoid ineffective overtreatment. Despite its significance, most breast cancer recurrence datasets are insufficiently large, not publicly available, or imbalanced, making these studies more difficult. This systematic review investigates the role of artificial intelligence in the prediction of breast cancer recurrence. We summarise common techniques, features, training and testing methodologies, metrics, and discuss current challenges relating to implementation in clinical practice. We systematically reviewed works published between 1 January 2011 and 1 November 2021 using the methodology of Kitchenham and Charter. We leveraged Springer, Google Scholar, PubMed, and IEEE search engines. This review found three areas that require further work. First, there is no agreement on artificial intelligence methodologies, feature predictors, or assessment metrics. Second, issues such as sampling strategies, missing data, and class imbalance problems are rarely addressed or discussed. Third, representative datasets for breast cancer recurrence are scarce, which hinders model validation and deployment. We conclude that predicting breast cancer recurrence remains an open problem despite the use of artificial intelligence.

Governing artificial intelligence in China and the European Union: Comparing aims and promoting ethical outcomes

In this article, we compare the artificial intelligence strategies of China and the European Union, assessing the key similarities and differences regarding what the high-level aims of each governance strategy are, how the development and use of AI is promoted in the public and private sectors, and whom these policies are meant to benefit. We characterize China’s strategy by its primary focus on fostering innovation and a more recent emphasis on “common prosperity,” and the EU’s on promoting ethical outcomes through protecting fundamental rights. Building on this comparative analysis, we consider the areas where the EU and China could learn from and improve upon each other’s approaches to AI governance to promote more ethical outcomes. We outline policy recommendations for both European and Chinese policymakers that would support them in achieving this aim.

Marketing analytics capability, artificial intelligence adoption, and firms' competitive advantage: Evidence from the manufacturing industry

Data-driven analytics and artificial intelligence (AI) have become the most crucial aspects of today's industrial marketing management. Although many firms have embraced analytics and AI strategies, corresponding academic advances have been slow. This research investigates how industrial goods manufacturers sustain their competitive advantage in export markets, convincing buyers in a competitive data-rich business environment. The evidence has been taken from the RMG (readymade garment) industry, one of the largest manufacturing industries significantly attached to the export markets. Utilizing multi-phase research design, the study reveals that firms marketing analytics capability play a vital role in sensing, seizing, and reconfiguring the market, consequently leading to a sustained competitive advantage. The performance of sensing, seizing, and reconfiguring becomes higher for a firm when they adopt AI on the strength of the marketing analytics platform. These findings exhibit the latest avenue of exploration within marketing analytics and AI's academic research paradigm. Further, in practice, managers will be aware of the facts that create resilience in this specific industry context.

Interdisciplinary Collaboration Opportunities, Challenges, and Solutions for Artificial Intelligence in Ultrasound.

Ultrasound is one of the most widely utilized imaging tools in clinical practice with the advantages of noninvasive nature and ease of use. However, ultrasound examinations have low reproducibility and considerable heterogeneity due to the variability of operators, scanners, and patients. Artificial Intelligence (AI)-assisted ultrasound has advanced in recent years, bringing it closer to routine clinical use. The combination of AI with ultrasound has opened up a world of possibilities for increasing work productivity and precision diagnostics. In this article, we describe AI strategies in ultrasound, from current opportunities, constraints to potential options for AI-assisted ultrasound.

The Medical and Health Humanities in the Middle East: Report on the Meeting of the 2nd International Conference on the Medical Humanities in the Middle East (online) in Doha, Qatar

The Medical and Health Humanities in the Middle East: Report on the Meeting of the 2nd International Conference on the Medical Humanities in the Middle East (online) in Doha, Qatar

An Ethical Analysis of the AI Values / Principles in “Turkey’s AI Strategy Plan” for Improving Patients’ Access to Health Care

The Turkish National Artificial Intelligence Strategy (TNAIS) has been prepared in line with the 11th Development Plan and the Presidential Annual Programs of the Republic of Turkey. The purpose of this strategy report is to describe the methods of determining national strategic AI priorities and set these priorities. In this report, according to the patent applications between 1980 and 2016, it is stated that the fields in which AI technologies are applied the most are: communication (24%), transportation (24%), and health sciences (19%). Therefore, it is seen that almost 1/5 of all AI technologies are applied in the field of health science. It can be concluded that AI technologies used in the field of health have a special place in the scope of the TNAIS report. The report covers four ethical values including: respect for human rights, democracy and the rule of law, improving the environment and biological ecosystem, ensuring diversity and inclusion, and living in peaceful, just and cohesive societies; and eight ethical principles including (1) proportionality, (2) safety and security, (3) fairness, (4) privacy, (5) transparency and explainability, (6) responsibility and accountability, (7) data sovereignty, and (8) multi-stakeholder governance. Consequently, in this study, the TNAIS report will be analyzed based on the ethical values and principles it contains within the scope of patients' access to healthcare. Then the current or prospective ethical issues emerging from the existence of AI technology used in healthcare will be addressed. Finally, the suggestions about the perspectives for approaching ethical issues when these values and principles are in conflict will be shared.

A HUMAN RIGHTS-BASED APPROACH TO THE USE AND REGULATION OF ARTIFICIAL INTELLIGENCE – AN AFRICAN PERSPECTIVE

This article aims to describe the impact of artificial intelligence (AI) on human rights, analyze the use and regulation of AI in some African countries and propose a human rights-based approach to such use and regulation, among other recommendations. Whereas there is no doubt that artificial intelligence (AI) has enhanced technological innovation and contributed tremendously to addressing societal and global challenges, it has however, also disrupted certain legal, social and economic systems. In many ways, it has also had a negative impact on the enjoyment of human rights. This article begins with a conceptual context that focuses on the term artificial intelligence. It then proceeds to analyze how artificial intelligence impacts on the protection and enjoyment of several human rights, including civil and political rights on the one hand and socio-economic rights on the other. This is followed by a brief discussion of the various artificial intelligence strategies, policies and relevant legislation in some African countries. It is argued that because of the importance of human rights, such strategies, policies and regulation must incorporate human rights norms and standards. This calls for a human rights-based approach. The novelty of this study lies in the fact that artificial intelligence is a new technological development. Many people are not familiar with this, especially in Africa. There are not many studies conducted on its impact on human rights. Therefore, the paper concludes with recommendations on how best African countries can use and regulate artificial intelligence, including adopting a human based-approach.

Cellular manufacturing design 1996–2021: a review and introduction to applications of Industry 4.0

As globalisation and competition grow rapidly, adopting a flexible manufacturing philosophy becomes more critical for manufacturers. Cellular Manufacturing System (CMS) is among one of the most favourable ones which has been deeply studied in academia since this philosophy was coined in 1925. To design the manufacturing system efficiently and effectively, the management team needs to have a comprehensive representation of the definitions, classifications, solution approach to cellular manufacturing design problems. In this study, exact methods, heuristic approaches, metaheuristic techniques, and artificial intelligence strategies for cellular manufacturing design are reviewed. A comparison of solution approaches is discussed and based on this analysis, some suggestions for future research are proposed. Moreover, we tried to highlight the importance of investigating sustainability decisions related to CMS problems, and integration of supply chain decisions with CMS decisions which have not been sufficiently studied to identify a research direction for researchers to model and analyse those issues. Finally, we briefly discuss the placement of CMS as one of the major production systems in the industry 4.0 evolution paradigm. Reviewing the adoption of industry 4.0 techniques by CMS sheds light on the contribution of methodological approaches in transition to intelligent CMS.

The future of education utilizing artificial intelligence in Turkey

This study examined the potential effects of artificial intelligence on Turkish education. A qualitative research approach was employed by posing an open-ended question to academics in order to attain this objective thanks to built-in capabilities for conducting complicated computer operations, cloud-based services, and conciliatory accession for agile network connections. This study emphasizes that Turkey is highly fragmented and consists of various business organizations at both the municipal and regional levels. The two main policy documents produced by the Turkish government suggest that colleges play a strong role in national and regional Artificial Intelligence (AI) strategies for workforce growth, with substantial consequences for AI adoption strategies. These documents include information on three well-known educational entities: The new oriental workgroups, recurrent neural networks, and classroom clustering. Significant aspects of Turkey’s educational AI growth include a strong private education industry and a growing international interest. The investigation results revealed a decline in the level of understanding regarding the methods of using artificial intelligence, indicating the necessity for additional awareness-raising in Turkey.

Overview and commentary of the CDEI's extended roadmap to an effective AI assurance ecosystem.

In recent years, the field of ethical artificial intelligence (AI), or AI ethics, has gained traction and aims to develop guidelines and best practices for the responsible and ethical use of AI across sectors. As part of this, nations have proposed AI strategies, with the UK releasing both national AI and data strategies, as well as a transparency standard. Extending these efforts, the Centre for Data Ethics and Innovation (CDEI) has published an AI Assurance Roadmap, which is the first of its kind and provides guidance on how to manage the risks that come from the use of AI. In this article, we provide an overview of the document's vision for a “mature AI assurance ecosystem” and how the CDEI will work with other organizations for the development of regulation, industry standards, and the creation of AI assurance practitioners. We also provide a commentary of some key themes identified in the CDEI's roadmap in relation to (i) the complexities of building “justified trust”, (ii) the role of research in AI assurance, (iii) the current developments in the AI assurance industry, and (iv) convergence with international regulation.

Performance Optimization of Industrial Supply Chain Using Artificial Intelligence.

Nowadays, organized retailing has witnessed a newer trend in the upcoming generations. Globally, these changes are attributed to growing family income, increased female participation, the transformation from joint to nuclear family structure, and technological advancements. Moreover, other variables such as lower supply chain costs, growing sales, rising consumer demands, changing market structure, and increasing competition also influenced supply chain networks. It is observed that the organizational nonlivestock supply chain performance is affected by strategic, operational, and environmental aspects. AI is helping to deliver powerful optimization capabilities, which are required for more accurate capacity planning, improved productivity, high quality, lower costs, and greater output, all while fostering safer working conditions. These benefits are all made possible thanks to the introduction of AI in supply chains. By conducting a comprehensive analysis of the relevant previous research, the purpose of this work is to determine the specific contributions that artificial intelligence (AI) has made to supply chain management. This research attempted to discover the present as well as possible AI strategies that may increase both the study of Supply Chain Management as well as the practice of it. This was done in order to solve the current scientific gap of AI in Supply Chain Management. It was also found that there are holes in the existing study that need to be filled by more scientific investigation. To be more exact, the following four facets were discussed: (1) the AI approaches that are most often used in Supply Chain Management; (2) the AI techniques that have the potential to be used in Supply Chain Management; (3) the Supply Chain Management subfields that have benefited from the application of AI so far; and (4) the subfields that have a high potential to be improved by AI. Identifying and evaluating articles from the four supply chain management domains of logistics, marketing, supply chain management, and manufacturing require the use of a predetermined set of inclusion and exclusion criteria. In this study, insights are provided via the use of methodical analysis and synthesis. A better understanding of these parameters not only improves the nonlivestock supply chain processes but also ensures competitive advantage. The present research aims to test the following elements that including supply chain speed, customer retention, supply chain management integration, and various management. The proposed work categorizes the performance of the supply chain using the Improved Feed Forward Network with Particle Swarm Optimization technique. Results indicate that inventory management, customer happiness, profitability, and client base identification are listed as competitive advantage elements. On the other hand, stakeholder satisfaction, innovation and learning, market performance, customer satisfaction, and financial success are the six recognized organizational performance criteria. Resultantly, the overall performance metrics of the proposed work is 94.12%, while accuracy rate, specificity, and sensitivity rate are found to be 94.12%, 92.15%, and 89.14%, respectively. The research can be helpful for industrial managers to optimize the performance of supply chain systems using artificial intelligence.

Role of the state and responsibility in governing artificial intelligence: a comparative analysis of AI strategies

ABSTRACT Technologies based on artificial intelligence (AI) represent a crucial governance challenge for policymakers. This study contributes to the understanding of how states plan to govern AI with respect to the role they assume and to the way they develop AI in a responsible manner. In different policy instruments across 22 countries plus the European Union, there is considerable variation in how governments approach the governance of AI, both regarding the policy measures proposed and their focus on public responsibility. Analysing a set of policy instruments we find multiple modes of AI governance, with the major difference being between self-regulation-promoting and market-based approaches, and a combination of entrepreneurial and regulatory governance approaches. Our analysis also indicates that the approach to public responsibility is largely independent of the chosen policy mix of AI governance. Therefore, responsibility seems to be a cross-cutting issue that cannot be tied to a specific approach of states towards technology.

Fast Prediction of Binding Affinities of SARS-CoV-2 Spike Protein and Its Mutants with Antibodies through Intermolecular Interaction Modeling-Based Machine Learning.

Since the introduction of the novel SARS-CoV-2 virus (COVID-19) in late 2019, various new variants have appeared with mutations that confer resistance to the vaccines and monoclonal antibodies that were developed in response to the wild-type virus. As we continue through the pandemic, an accurate and efficient methodology is needed to help predict the effects certain mutations will have on both our currently produced therapeutics and those that are in development. Using published cryo-electron microscopy and X-ray crystallography structures of the spike receptor binding domain region with currently known antibodies, in the present study, we created and cross-validated an intermolecular interaction modeling-based multi-layer perceptron machine learning approach that can accurately predict the mutation-caused shifts in the binding affinity between the spike protein (wild-type or mutant) and various antibodies. This validated artificial intelligence (AI) model was used to predict the binding affinity (Kd) of reported SARS-CoV-2 antibodies with various variants of concern, including the most recently identified “Deltamicron” (or “Deltacron”) variant. This AI model may be employed in the future to predict the Kd of developed novel antibody therapeutics to overcome the challenging antibody resistance issue and develop structural bases for the effects of both current and new mutants of the spike protein. In addition, the similar AI strategy and approach based on modeling of the intermolecular interactions may be useful in development of machine learning models predicting binding affinities for other protein–protein binding systems, including other antibodies binding with their antigens.