explainable ai

Responsible Integration of AI in Cancer Genomics: Barriers, Risks, and Pathways to Trustworthy Clinical Translation

Artificial intelligence (AI) and natural language processing (NLP) are increasingly used to extract, integrate, and interpret biomedical knowledge relevant to cancer genomics, yet their translation into routine clinical oncology has been comparatively slow. The central challenge is not computational capability alone, but trustworthy integration into clinical workflows. This review examines how NLP and AI support the cancer genomics pipeline, from literature mining and automated variant interpretation to clinical trial matching, knowledge graph construction, and multimodal data integration. We identify four interrelated translational failure domains: evidence inconsistency, explainability and uncertainty, data governance and reproducibility, and interoperability. Rather than considering these challenges in isolation, we take a systems-level view, focusing on their interaction across the translational pathway. We propose a conceptual framework and roadmap for addressing these domains through rigorous validation, uncertainty-aware methods, interoperable infrastructures, regulatory alignment, and human oversight across the AI lifecycle. Progress toward routine clinical use will depend less on further improving model capability than on systematically addressing these interacting failure domains from development through deployment and post-deployment monitoring.

The PUR-1 Cyber-Physical Digital Twin

Digital twin technologies have the potential to improve operational flexibility and responsiveness capabilities of nuclear systems. To provide decision support, cyber event characterization, state estimation, predictive control, and real-time dynamic processing of operational data, however, an efficient digital twin needs to integrate multiple models (data-driven as well as physics-based) with explainability while at the same time maintain two-way synchronization with the physical facility at a time constant less than its operational cycle. In this work, we present the Purdue University Reactor One Digital Twin (PUR-1 DT), a cyber-physical digital twin with a complete high-fidelity physics-based and AI-driven virtual model stack (neutronics, thermal-hydraulics, point kinetics) which provides closed-loop explainable diagnostics, forecasting, predictive control, and action recommendation back to the reactor via two-way communications and a cyber-physical testbed. We demonstrate real-time synchronized state estimation and short-term forecasting over a full reactor operational cycle and conduct a series of benchmarking experiments to validate accuracy and latency. Our results show good agreement with experimental results and lay the groundwork for further development and experimental demonstration of DT-enabled functionalities in real-world facilities.

Future of Financial Crime Compliance: From Reactive Reporting to Proactive Risk

The field of financial crime compliance is undergoing rapid transformation driven by increasingly stringent global regulations, technological advancement, and the growing sophistication of criminal methodologies. Traditional financial crime compliance frameworks have relied predominantly on reactive measures such as suspicious activity reporting and retrospective audits. This study examines the global shift from reactive to proactive financial crime compliance using a mixed-methods approach, combining a quantitative survey of 100 compliance professionals from financial institutions across North America, Europe, Asia-Pacific, and the Middle East with qualitative in-depth interviews of 10 senior compliance executives. Findings reveal significant regional variation in the adoption of proactive tools including predictive analytics and behavioural monitoring, with Asia-Pacific leading adoption and the Middle East showing the lowest uptake. Real-time transaction monitoring is deployed by 78% of surveyed institutions, while only 37% use graph analytics. The study proposes a proactive quadrant framework for financial crime compliance and identifies key barriers to transformation including legacy infrastructure, regulatory uncertainty around AI explainability, skill gaps, and organisational cultural resistance. The paper calls for closer collaboration between financial institutions and regulators to enable the transition from reactive reporting to proactive risk management.

Future of Financial Crime Compliance: From Reactive Reporting to Proactive Risk

The field of financial crime compliance is undergoing rapid transformation driven by increasingly stringent global regulations, technological advancement, and the growing sophistication of criminal methodologies. Traditional financial crime compliance frameworks have relied predominantly on reactive measures such as suspicious activity reporting and retrospective audits. This study examines the global shift from reactive to proactive financial crime compliance using a mixed-methods approach, combining a quantitative survey of 100 compliance professionals from financial institutions across North America, Europe, Asia-Pacific, and the Middle East with qualitative in-depth interviews of 10 senior compliance executives. Findings reveal significant regional variation in the adoption of proactive tools including predictive analytics and behavioural monitoring, with Asia-Pacific leading adoption and the Middle East showing the lowest uptake. Real-time transaction monitoring is deployed by 78% of surveyed institutions, while only 37% use graph analytics. The study proposes a proactive quadrant framework for financial crime compliance and identifies key barriers to transformation including legacy infrastructure, regulatory uncertainty around AI explainability, skill gaps, and organisational cultural resistance. The paper calls for closer collaboration between financial institutions and regulators to enable the transition from reactive reporting to proactive risk management.

FROM EMPIRICAL HERBAL MEDICINE TO PREDICTIVE THERAPEUTICS: THE ROLE OF AI AND MULTI-OMICS

Purpose: Medicinal plants are the richest source of structurally diverse biologically active compounds. Herbal leads discovery can also be accelerated using artificial intelligence and multi omics technologies. This review evaluates and summarizes the applications of artificial intelligence (AI), multi-omics, synthetic biology, and biomanufacturing in advancing research for herbal therapeutics. Virtual computational screening, deep learning, and molecular docking extremely accelerate lead identifications, structure-activity predictions, and target identification. Method: We comprehensively examine literature on virtual computational screening, deep learning, molecular docking, multi-omics platforms, genome mining, CRISPR-based synthetic biology, and emerging tools like organ-on-a-chip systems and nanobiotechnology using PubMed, ScienceDirect and Google Scholar. Result: Virtual screening, deep learning, and molecular docking extremely accelerate lead identifications, structure-activity predictions, and target identification. Simultaneously, multi-omics platforms evaluate system-level metabolic networks, regulatory pathways, and host–microbiome interactions. Genome mining and CRISPR-based synthetic biology unlock cryptic metabolic pathways and enable scalable, sustainable production of high-value natural products. Furthermore, tools like organ-on-a-chip systems and nanobiotechnology bridge the gap toward precision herbal medicine and standardization. Conclusion: By integrating these multidisciplinary strategies, herbal drug discovery is evolving from empirical observation toward predictive, sustainable, and mechanism-driven pharmaceutical science. Finally, key challenges like data standardization, explainable AI, experimental validation, and regulatory approval must be addressed for clinical translation.

FROM EMPIRICAL HERBAL MEDICINE TO PREDICTIVE THERAPEUTICS: THE ROLE OF AI AND MULTI-OMICS

Purpose: Medicinal plants are the richest source of structurally diverse biologically active compounds. Herbal leads discovery can also be accelerated using artificial intelligence and multi omics technologies. This review evaluates and summarizes the applications of artificial intelligence (AI), multi-omics, synthetic biology, and biomanufacturing in advancing research for herbal therapeutics. Virtual computational screening, deep learning, and molecular docking extremely accelerate lead identifications, structure-activity predictions, and target identification. Method: We comprehensively examine literature on virtual computational screening, deep learning, molecular docking, multi-omics platforms, genome mining, CRISPR-based synthetic biology, and emerging tools like organ-on-a-chip systems and nanobiotechnology using PubMed, ScienceDirect and Google Scholar. Result: Virtual screening, deep learning, and molecular docking extremely accelerate lead identifications, structure-activity predictions, and target identification. Simultaneously, multi-omics platforms evaluate system-level metabolic networks, regulatory pathways, and host–microbiome interactions. Genome mining and CRISPR-based synthetic biology unlock cryptic metabolic pathways and enable scalable, sustainable production of high-value natural products. Furthermore, tools like organ-on-a-chip systems and nanobiotechnology bridge the gap toward precision herbal medicine and standardization. Conclusion: By integrating these multidisciplinary strategies, herbal drug discovery is evolving from empirical observation toward predictive, sustainable, and mechanism-driven pharmaceutical science. Finally, key challenges like data standardization, explainable AI, experimental validation, and regulatory approval must be addressed for clinical translation.

AI explained: a guide for non-technical readers

AI Explained: A Guide for Non-Technical Readers builds understanding of artificial intelligence from first principles rather than diving in at the top. Written by experienced policy and technical experts, the book walks through rules and logic-based approaches, statistical methods, neural networks, machine learning, and generative models in accessible, structured terms. Each major section concludes with a dedicated use-cases chapter grounding abstract concepts in practical scenarios drawn from healthcare, law, and business. Rather than teaching readers how to build or deploy AI, the book answers a more fundamental question: how do these systems achieve the outcomes they produce? Coverage of AI policy, ethics, and societal impact rounds out the treatment, informed directly by the authors' advisory roles with governments and international bodies.

AI Ethics in Industry: A Research Framework

Artificial Intelligence (AI) systems exert a growing influence on our society. As they become more ubiquitous, their potential negative impacts also become evident through various real-world incidents. Following such early incidents, academic and public discussion on AI ethics has highlighted the need for implementing ethics in AI system development. However, little currently exists in the way of frameworks for understanding the practical implementation of AI ethics. In this paper, we discuss a research framework for implementing AI ethics in industrial settings. The framework presents a starting point for empirical studies into AI ethics but is still being developed further based on its practical utilization.

Implementing Ethics in AI: An industrial multiple case study

Solutions in artificial intelligence (AI) are becoming increasingly widespread in system development endeavors. As the AI systems affect various stakeholders due to their unique nature, the growing influence of these systems calls for eth-ical considerations. Academic discussion and practical examples of autonomous system failures have highlighted the need for implementing ethics in software development. However, research on methods and tools for implementing ethics into AI system design and development in practice is still lacking. This paper be-gins to address this focal problem by providing a baseline for ethics in AI based software development. This is achieved by reporting results from an industrial multiple case study on AI systems development in the health care sector. In the context of this study, ethics were perceived as interplay of transparency, re-sponsibility and accountability, upon which research model is outlined. Through these cases, we explore the current state of practice out on the field in the ab-sence of formal methods and tools for ethically aligned design. Based on our data, we discuss the current state of practice and outline existing good practic-es, as well as suggest future research directions in the area.

Continuous experimentation on artificial intelligence software: a research agenda

Moving from experiments to industrial level AI software development requires a shift from understanding AI/ ML model attributes as a standalone experiment to know-how integrating and operating AI models in a large-scale software system. It is a growing demand for adopting state-of-the-art software engineering paradigms into AI development, so that the development efforts can be aligned with business strategies in a lean and fast-paced manner. We describe AI development as an “unknown unknown” problem where both business needs and AI models evolve over time. We describe a holistic view of an iterative, continuous approach to develop industrial AI software basing on business goals, requirements and Minimum Viable Products. From this, five areas of challenges are presented with the focus on experimentation. In the end, we propose a research agenda with seven questions for future studies.

Time for AI (Ethics) Maturity Model Is Now

There appears to be a common agreement that ethical concerns are of high importance when it comes to systems equipped with some sort of Artificial Intelligence (AI). Demands for ethical AI are declared from all directions. As a response, in recent years, public bodies, governments, and universities have rushed in to provide a set of principles to be considered when AI based systems are designed and used. We have learned, however, that high-level principles do not turn easily into actionable advice for practitioners. Hence, also companies are publishing their own ethical guidelines to guide their AI development. This paper argues that AI software is still software and needs to be approached from the software development perspective. The software engineering paradigm has introduced maturity model thinking, which provides a roadmap for companies to improve their performance from the selected viewpoints known as the key capabilities. We want to voice out a call for action for the development of a maturity model for AI software. We wish to discuss whether the focus should be on AI ethics or, more broadly, the quality of an AI system, called a maturity model for the development of AI systems.

Implementing AI Ethics in a Software Engineering Project-Based Learning Environment - The Case of WIMMA Lab