STRICT LIABILITY OR FAULT-BASED REGIMES FOR AI-CAUSED HARM? A DOCTRINAL ANALYSIS ACROSS COMMON LAW AND CIVIL LAW SYSTEMS

Abstract

Scientists have entered a new scientific era which uses multiple genomic assessment methods to study diseases across all their molecular components. Multi-omics methods which study all genetic material in cancer and complex diseases enable researchers to identify treatment targets and drug resistance mechanisms through advanced methods which assess entire biological systems instead of studying single genetic changes. This paper provides a comprehensive synthesis of current multi-omics methodologies, computational integration frameworks, and their applications in therapeutic target discovery and resistance marker identification. The study investigates the process of creating whole-genome sequencing data, transcriptomic data, proteomic data, epigenomic data, and metabolomic data, which healthcare professionals use to discover targets that can lead to clinical applications. Case studies from lung adenocarcinoma breast cancer and haematological malignancies demonstrate how multi-omics profiling has uncovered resistance mechanisms which targeted therapies face against EGFR inhibitors and HER2-directed agents and BCR-ABL tyrosine kinase inhibitors. We describe the necessary computational infrastructure to integrate multi-omics data and the difficulties of using multi-omics in clinical oncology and the rising use of artificial intelligence for prioritizing multi-omics targets. The paper concludes with a forward-looking perspective on single-cell multi-omics and spatial transcriptomics as next frontiers in precision medicine.