AI Driven Algorithm for Personalized Dose Adjustment in Clinical Practice

Dose adjustment is one of the most critical challenges in clinical practice. Traditional dosing
strategies are often “one-size-fits-all,” which can result in under dosing (ineffectiveness) or over
dosing (toxicity). Patient variability in genetics, organ function, comorbidities, and drug
interactions makes personalized dosing essential.Artificial Intelligence (AI) offers a
transformative approach by integrating clinical data, pharmacokinetic/pharmacodynamic (PK/PD)
models, and real-time monitoring to recommend individualized doses.
Methodology
AI in Antibiotic Dose Adjustment in Critically ill patients (e.g., sepsis, septic shock) exhibit
altered drug clearance, making standard antibiotic dosing ineffective. Antimicrobial resistance
(AMR) adds further complexity. AI Solution includes Machine learning models predict patientspecific pharmacokinetics for drugs like vancomycin and aminoglycosides. Decision support
systems (e.g., AutoKinetics trial) combine EHR data, Bayesian PK modeling, and bedside
monitoring to optimize dosing. Impact: AI improves target attainment, reduces toxicity, and
supports antibiotic stewardship programs to slow AMR.
Conclusion
AI-driven algorithms are reshaping the way clinicians approach dose adjustment in oncology,
antibiotics, and anticoagulant therapy. By leveraging real-time patient data and predictive
modeling, these systems move us closer to true precision dosing, improving patient outcomes
while minimizing risks.