Harnessing Artificial Intelligence to Transform Clinical Trials and Cancer Care: Opportunities and Challenges

Abstract

Artificial intelligence (AI) is working toward the reality of speeding up oncology drug development, offering the ability to cut years off the pipeline while maintaining patient safety and personalized care. Machine learning (ML) models analyze historical and real-world data to optimize eligibility criteria, simulate in silico cohorts, flag protocol risks, and recommend real-time adaptations. Natural language processing enhances patient screening by extracting patient data from electronic health records to match diverse patient populations to trials faster than traditional methods. AI-driven analysis of data from electronic wearables and imaging enables early toxicity and efficacy signals, allowing providers real-time monitoring. However, the same code that accelerates technology can also amplify bias, increase data security issues, hallucinate unsafe recommendations, and raise legal and ethical alarms. Safeguards, including transparent model reporting, bias mitigation, robust cybersecurity, clinician oversight, and education for providers and patients, are essential. Harnessed responsibly, AI can transform clinical trials and oncology care without sacrificing empathy, accountability, and patient-centered values.

Ähnliche Arbeiten