Medical technology increasingly relies on artificial intelligence to speed drug development, predict clinical trial delays, and improve diagnostics. AI‑driven platforms such as IQVIA’s Predictive Clinical Development claim to identify risk early and can shave up to two years off a ten‑year development cycle, promising faster patient access to new therapies. However, the same AI capabilities carry notable risks. Bias in algorithms may produce lower‑accuracy results for historically underserved groups, and broader AI hazards include threats to security, privacy, and even human safety. Because these risks can undermine trust and patient outcomes, responsible AI practices and a formal AI governance strategy—covering frameworks, policies, and processes—are recommended to ensure that benefits are realized while minimizing harms. In practice, developers and regulators must balance the speed and efficiency gains of AI with vigilant oversight to address bias, data security, and ethical concerns. [1] [2]
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