Regulating complexity in AI-enabled omics and multi-omics technologies for precision medicine.

The integration of artificial intelligence (AI) in healthcare presents significant promise to enhance clinical procedures and patient outcomes. This research examines the setting, methodology, conclusions, and issues associated with AI in healthcare. The swift proliferation of digital health data, encompassing medical imaging and clinical records, has generated substantial prospects for AI applications. Artificial intelligence methodologies, including machine learning, natural language processing, and computer vision, facilitate the derivation of significant insights from intricate datasets, hence improving clinical decision-making. A thorough literature review examines the practical applications of AI, encompassing its roles in medical diagnostics, treatment planning, and patient outcome prediction. The report also examines ethical issues, data protection, and legal frameworks, which are crucial for the responsible application of AI in healthcare. The results illustrate AI's capacity to enhance diagnostic precision, facilitate administrative efficiency, and optimise resource distribution, resulting in tailored therapies and improved healthcare administration. Nonetheless, obstacles persist, such as data integrity, algorithm transparency, and ethical considerations, which must be resolved to guarantee the secure and efficient deployment of AI. Continuous research, cooperation between healthcare and AI experts, and the establishment of comprehensive regulatory frameworks are essential for optimising the advantages of AI while minimising hazards. This research highlights AI's capacity to transform healthcare, stressing the necessity for a multidisciplinary strategy to effectively harness its benefits and tackle the associated ethical and regulatory dilemmas.

In the field of cancer genomics, the broad availability of genetic information offered by next-generation sequencing technologies and rapid growth in biomedical publication has led to the advent of the big-data era. Integration of artificial intelligence (AI) approaches such as machine learning, deep learning, and natural language processing (NLP) to tackle the challenges of scalability and high dimensionality of data and to transform big data into clinically actionable knowledge is expanding and becoming the foundation of precision medicine. In this paper, we review the current status and future directions of AI application in cancer genomics within the context of workflows to integrate genomic analysis for precision cancer care. The existing solutions of AI and their limitations in cancer genetic testing and diagnostics such as variant calling and interpretation are critically analyzed. Publicly available tools or algorithms for key NLP technologies in the literature mining for evidence-based clinical recommendations are reviewed and compared. In addition, the present paper highlights the challenges to AI adoption in digital healthcare with regard to data requirements, algorithmic transparency, reproducibility, and real-world assessment, and discusses the importance of preparing patients and physicians for modern digitized healthcare. We believe that AI will remain the main driver to healthcare transformation toward precision medicine, yet the unprecedented challenges posed should be addressed to ensure safety and beneficial impact to healthcare.

Artificial intelligence (AI) is reshaping healthcare and personalized medicine, particularly in the pharmaceutical industry. This review provides a comprehensive examination of current AI applications across various stages of the drug development pipeline, including drug discovery, clinical trial design, patient stratification, diagnosis, and treatment personalization. By employing advanced computational approaches such as machine learning, deep learning, and natural language processing, AI enables the integration and analysis of large and complex biological and clinical datasets, including genomic, proteomic, and electronic health record data. These capabilities facilitate the identification of novel therapeutic targets and support the development of individualized treatment strategies. The review also addresses critical considerations such as data quality, algorithmic transparency, ethical challenges, and regulatory frameworks that influence the safe and effective deployment of AI technologies. Furthermore, it highlights the growing need for robust data integration, interpretation strategies, and interdisciplinary collaboration to fully realize AI’s potential in advancing personalized medicine. Future perspectives emphasize AI’s role as a transformative tool for innovation in patient-centered pharmaceutical care.

The merger of the Internet of Things (IoT) and Artificial Intelligence (AI) has occurred, which implies that healthcare has undergone a revolution in the meaning that it has allowed creating a personalized treatment plan based on the real-time analysis of data. AI-IoT ecosystems are offered by smart devices, wearable sensors, predictive algorithms and continuously measure the physiological parameters and adjust therapeutic interventions to the personal profile of biological and behavioral parameters of the patient. The synergy aids in precision medicine by closing the divide between the delivery of care and the diagnosis on an individual level. Nevertheless, privacy of data, interoperability and transparency of algorithms have also some privacy of data. To render individual interventions ethical and equal, ensure that there is safe governance of information, the mechanisms of explainable AI and the design is patient-friendly. Finally, the last category is the AI-IoT integration, here change in history of healthcare took place, when the concept of individualization, automation and ethics are merged to redesign the essence of preventive and precision medicine.

Artificial intelligence (AI) has transformed pharmacological research through machine learning, deep learning, and natural language processing. These advancements have greatly influenced drug discovery, development, and precision medicine. AI algorithms analyze vast biomedical data identifying potential drug targets, predicting efficacy, and optimizing lead compounds. AI has diverse applications in pharmacological research, including target identification, drug repurposing, virtual screening, de novo drug design, toxicity prediction, and personalized medicine. AI improves patient selection, trial design, and real-time data analysis in clinical trials, leading to enhanced safety and efficacy outcomes. Post-marketing surveillance utilizes AI-based systems to monitor adverse events, detect drug interactions, and support pharmacovigilance efforts.Machine learning models extract patterns from complex datasets, enabling accurate predictions and informed decision-making, thus accelerating drug discovery. Deep learning, specifically convolutional neural networks (CNN), excels in image analysis, aiding biomarker identification and optimizing drug formulation. Natural language processing facilitates the mining and analysis of scientific literature, unlocking valuable insights and information.However, the adoption of AI in pharmacological research raises ethical considerations. Ensuring data privacy and security, addressing algorithm bias and transparency, obtaining informed consent, and maintaining human oversight in decision-making are crucial ethical concerns. The responsible deployment of AI necessitates robust frameworks and regulations.The future of AI in pharmacological research is promising, with integration with emerging technologies like genomics, proteomics, and metabolomics offering the potential for personalized medicine and targeted therapies. Collaboration among academia, industry, and regulatory bodies is essential for the ethical implementation of AI in drug discovery and development. Continuous research and development in AI techniques and comprehensive training programs will empower scientists and healthcare professionals to fully exploit AI's potential, leading to improved patient outcomes and innovative pharmacological interventions.

Artificial Intelligence (AI) is revolutionizing healthcare, particularly in precision medicine and clinical decision-making. In neonatal care, accurate dosing of the drugs, fluids, in case of surgeries, the required anesthetics and analgesics is critical for patient safety and outcomes. This review explores the current landscape of AI-based dosage algorithms used in intraoperative and postoperative neonatal care, evaluating their development, application, performance, and clinical integration. We discuss role of machine learning models, data sources, algorithmic transparency, and ethical considerations, challenges and future directions in neonatal care with special emphasize of surgical and intensive care.

The rapid expansion of artificial intelligence and emerging digital technologies in medicine has fundamentally reshaped clinical decision-making, healthcare governance, and biomedical knowledge production. While medical artificial intelligence promises enhanced efficiency, diagnostic accuracy, and system optimization, it simultaneously generates complex ethical risks that challenge traditional medical norms and regulatory approaches. Existing discussions of medical AI ethics often prioritize technical safeguards, algorithmic transparency, or regulatory compliance, yet they frequently underestimate the need for deeper normative reflection on responsibility, moral agency, and the meaning of care. This paper argues that medical humanities plays an indispensable normative role in identifying, interpreting, and addressing the ethical risks embedded in medical AI applications. Focusing explicitly on artificial intelligence and frontier medical technologies, the study analyzes the structural sources of ethical risk in algorithm-driven medicine and examines how medical humanities contributes to ethical norm construction beyond procedural governance. By situating medical AI within humanistic concerns such as moral responsibility, interpretive judgment, and human dignity, the paper demonstrates that medical humanities is essential for ethically robust and socially legitimate AI-enabled healthcare.

The rapid growth in adoption of artificial intelligence (AI) for decision-making processes, administrative functions and academic services has created an environment of rapid change throughout the university setting. As technology advances, concerns surrounding transparency, fairness and accountability of algorithms also arise; these issues affect how employees of universities perceive and trust AI systems. Research on AI governance and trust in organisations will be utilised in this study to explore how transparency and communication among leaders impact employee trust in AI as a governance tool within universities. Data from academic and administrative staff from universities that are currently undergoing transformation through digitalisation influenced by AI will be analysed using a structural equation modelling (SEM) method. The study will propose that algorithm transparency has both a direct and indirect positive correlation to employee trust through perceived organisational fairness. Additionally, the relationship between algorithm transparency and employee trust will be moderated by leader communication through reducing uncertainty and creating understanding of AI as a decision tool. Overall, this research is intended to advance the body of literature related to human-centred governance of AI by identifying socio-organisational mechanisms through which employee trust in AI systems develops. Furthermore, it will provide university leaders and policymakers with actionable recommendations for creating responsible governance structures for AI that prioritise transparency, communication and employee participation. By evaluating individual level trust related to the ethical implementation of AI at universities, our research will assist universities in understanding how they can balance their drive towards innovation through technology and caring for people.

Artificial intelligence (AI) is rapidly expanding in myriad industries and systems. This study sought to investigate public trust in using AI in the criminal court process. While previous research has identified factors that influence trust in AI, such as perceived accuracy and transparency of algorithms, less is known about the role of influential leaders—such as judges—in shaping public trust in new technology. This study examined the relationship between locus of control, anthropomorphism, cultural values, and perceived trust in AI. Participants completed a survey assessing their perceptions of trust in AI in determining bail eligibility, bail fines and fees, sentencing length, sentencing fines and fees, and writing legal documents (e.g., findings and disposition). Participants were more likely to trust AI performing financial calculations rather than determining bail eligibility, sentence length, or drafting legal documents. Participants’ comfort with AI in decision-making also depended on their perceptions of judges’ trust in AI, and they expressed concerns about AI perpetuating bias and the need for extensive testing to ensure accuracy. Interestingly, no significant association was found with other participant characteristics (e.g., locus of control, anthropomorphism, or cultural values). This study contributes to the literature by highlighting the role of judges as influential leaders in shaping public trust in AI and examining the influence of individual differences on trust in AI. The findings also help inform the development of recommended practices and ethical guidelines for the responsible use of AI in the courts.

Artificial intelligence (AI) is becoming increasingly accessible to the general public. There is an ongoing debate regarding the implications of widespread AI adoption. Some argue that placing advanced AI systems in the hands of the general public could have dangerous consequences if misused either intentionally or unintentionally. Others counter that AI can be safe and beneficial if developed and deployed responsibly. This paper explores both sides of this complex issue. On the one hand, broad AI availability could boost productivity, efficiency, and innovation across industries and domains. Individuals may benefit from AI assistants that help with tasks like scheduling, research, content creation, recommendations, and more personalized services. However, without proper safeguards and oversight, AI could also be misused to spread misinformation, manipulate people, or perpetrate cybercrime. And if AI systems become extremely advanced, there are risks related to the alignment of AI goal systems with human values. On the other hand, with thoughtful coordination between policymakers, researchers, companies, and civil society groups, AI can be developed safely and for the benefit of humanity. Ongoing research into AI safety and ethics is crucial, as are governance frameworks regarding areas like data privacy, algorithmic transparency, and accountability. As AI becomes more deeply integrated into products and platforms, best practices should be established regarding appropriate use cases, human oversight, and user empowerment. With conscientious, ethical implementation, AI can empower individuals and enhance society. But key issues around alignment, security, and governance must be proactively addressed to minimize risks as advanced AI proliferates. This will likely require evolving perspectives, policies, and scientific breakthroughs that promote innovation while putting human interests first.

In this article, we aim to explore the rapidly developing role of artificial intelligence (AI) in cardiac metabolism research, highlighting its impact on biomarker discovery, precision medicine, and patient stratification. Cardiac metabolism, a key determinant of cardiovascular health, is often disrupted in cardiovascular diseases (CVDs) like heart failure and coronary artery disease. AI’s ability to process and analyze large-scale data offers new chances for understanding and addressing these metabolic dysfunctions. By integrating up-to-date technologies with molecular and clinical insights, AI enables the achievement of personalized treatments, more accurate diagnostics, and the discovery of potential novel therapeutic targets. The main challenges include ethical concerns around data privacy, algorithmic bias, and the need for representative datasets. Future directions focus on developing transparent, accountable, and collaborative AI models that integrate data and enable real-time monitoring, ensuring fairness and accessibility in healthcare. As AI continues to evolve, its role in advancing cardiovascular care is expected to grow, offering new trends in cardiovascular research.

Cancer surgery is undergoing transformative integration of precision medicine, artificial intelligence (AI), robotics, advanced imaging, and molecular technologies. These innovations promise enhanced surgical precision, improved patient outcomes, and personalized treatment approaches through data-driven decision-making. A comprehensive systematic literature review was conducted across PubMed, Embase, Cochrane Library, and Web of Science databases from January 2020 to November 2025. Studies were analyzed for clinical applications, therapeutic outcomes, cost-effectiveness, and implementation challenges. Primary endpoints included surgical accuracy, margin status, survival outcomes, complication rates, and technology adoption metrics. Precision medicine utilizing genomic profiling and circulating tumor DNA demonstrated 94.9% sensitivity and 88.8% specificity in multi-cancer detection. The CIRCULATE-Japan GALAXY study showed ctDNA positivity during the molecular residual disease window predicted significantly inferior disease-free survival (HR 11.99; P < 0.0001) and overall survival (HR 9.68; P < 0.0001). AI-assisted surgical systems achieved area under the curve values of 0.76-0.85 in outcome prediction and reduced surgical complications by 25-30%. The da Vinci 5 robotic system demonstrated 43% reduction in tissue damage through force feedback technology. Meta-analysis of 15,137 patients showed robotic pancreatoduodenectomy reduced hospital stays and conversion rates compared to laparoscopy. Fluorescence-guided surgery achieved improved 5-year overall survival (80.6% vs. 66.7%, P = 0.018) in gastric cancer. Mass spectrometry techniques achieved 93.4-97.1% diagnostic accuracy. Perioperative immunotherapy in non-small cell lung cancer reduced recurrence risk by 43% (HR 0.57) and improved pathological complete response rates over 5-fold (RR 5.58). Nanotechnology-based delivery systems reduced cardiac toxicity (6% vs. 21%) while maintaining therapeutic efficacy. The convergence of precision medicine, AI, robotics, and molecular technologies is revolutionizing cancer surgery toward personalized, data-driven interventions with substantial clinical outcome improvements. Implementation challenges including cost, standardization, and healthcare disparities require systematic addressing for widespread adoption.

OPINION article Front. Physiol., 06 November 2015Sec. Systems Biology Archive Volume 6 - 2015 | https://doi.org/10.3389/fphys.2015.00318

The increasing integration of Artificial Intelligence (AI) in e-commerce has transformed digital marketing strategies, particularly through personalized recommendation systems. However, this advancement has raised ethical concerns regarding data privacy, algorithmic transparency, and consumer autonomy. In Southeast Asia, where digital platforms rapidly expand, these concerns become more complex due to diverse regulatory landscapes and user expectations. This study aims to analyze and compare the transparency of AI-based recommendation systems across three major e-commerce platforms in the region: Shopee, Lazada, and Tokopedia. A qualitative comparative approach was employed using content analysis of official policy documents, including privacy policies and AI guidelines, sourced from each platform between January and March 2024. The analysis utilized LLM-assisted text mining and thematic coding to identify ethical indicators such as algorithmic transparency, user control, and policy readability. The results reveal significant variation in ethical practices: Shopee scored the highest in all dimensions, including algorithmic transparency (score: 9/10), user control (8/10), and policy readability (9/10). Lazada ranked moderately with scores of 6/10, 4/10, and 6/10 respectively, while Tokopedia scored lowest with 3/10 for transparency, 0/10 for user control, and 4/10 for readability. These disparities indicate that ethical communication and user empowerment remain uneven across platforms. This study contributes to the discourse on digital ethics by highlighting the need for regional standards in AI transparency and promoting user-centric ethical design. The findings provide valuable insights for policymakers, platform designers, and digital consumers navigating trust in algorithm-driven environments.

Abstract: This book critically explores how artificial intelligence (AI) is reshaping India’s trade law landscape and enabling strategic reforms for improved access to global markets. It situates India’s economic liberalization within an AI-driven paradigm, analyzing how algorithmic decision-making, data flows, and automation affect trade facilitation, legal compliance, and global competitiveness. By examining emerging issues like algorithmic transparency, AI-fueled supply chains, and digital trade governance, the book bridges technological innovation and trade regulation. With sectoral case studies spanning services, pharmaceuticals, fintech, and agri-tech, the book provides an in-depth assessment of India’s preparedness to align domestic laws with multilateral and bilateral commitments in a tech-forward global economy. Emphasizing policy recommendations and future-proof frameworks, it offers a roadmap for legal, infrastructural, and diplomatic strategies to elevate India’s AI-enhanced trade capacity. Grounded in legal theory, regulatory practice, and digital diplomacy, this volume is essential reading for policymakers, scholars, and practitioners navigating the intersection of trade law and AI in the 21st century. Keywords: India, trade law, artificial intelligence, global market access, WTO, FTAs, regulatory reform, algorithmic transparency, data governance, digital trade, AI policy, export competitiveness