This study demonstrates the feasibility and acceptance of conditional autonomous AI systems for pharmaceutical decision-making in high-stakes clinical scenarios where conventional predictive methods are inadequate. The high rates of physician adoption and patient compliance suggest that the medical community may be prepared for this level of AI autonomy when specific conditions are met: demonstrated clinical need, algorithmic transparency, workflow compatibility, and maintained physician control. These findings establish a potential framework for deploying similar autonomous AI systems in other clinical contexts requiring early intervention and lacking reliable predictive tools. However, critical issues remain unresolved, including liability determination, implementation of closed-loop feedback mechanisms for continuous model refinement, and validation across diverse patient populations and institutional protocols. Further investigation is warranted to assess efficacy in controlled trials and to establish regulatory and ethical frameworks for autonomous AI pharmaceutical intervention.