Drug development in oncology is facing rising complexity, prolonged timelines, and increasing costs, with many trials failing to reach completion or secure regulatory approval for the investigated treatment. Against this backdrop, artificial intelligence (AI) and real-world data have emerged as promising tools to improve the efficiency and quality of clinical research. This narrative review explores how AI can support clinical trial design, conduct, and analysis. AI-driven methods can streamline information gathering, optimize eligibility criteria, and predict trial success, thereby reducing costly failures. Patient recruitment and retention, often the most challenging aspects of oncology trials, may benefit from AI-supported matching algorithms, digital health technologies, and personalized interactions. During trial conduct, natural language processing and sensor-based monitoring offer opportunities to reduce administrative burden and capture real-world, patient-centred outcomes. For trial analyses, AI enhances radiology, digital pathology, pharmacometrics, and multimodal modelling, enabling more accurate prognostic and predictive insights. In addition, AI-based simulations, such as digital twins and in silico trials, hold potential to complement conventional trial designs. However, despite rapid technical advances, evidence supporting clinical utility remains limited. Most applications are tested retrospectively or in single-centre settings, limiting generalizability. Regulatory frameworks, including the European Union Artificial Intelligence Act and emerging ESMO standards for AI-based biomarkers, emphasize transparency, reproducibility, and prospective validation. Ultimately, the successful integration of AI into oncology trials will depend less on technical capacity than on rigorous evaluation, harmonized regulation, and adoption of shared quality standards.
Knapen et al. (Fri,) studied this question.