1. INTRODUCTION:

AI methods have transitioned from being research topics to practical tools that significantly impact pharmaceutical research and daily pharmacy operations studied in pharmaceutical sciences and pharmacy practice show that AI is being quickly adopted for in-silico screening, pharmacokinetic/pharmacodynamic modelling, automated dispensing, clinical decision support, and patient engagement systems.^1-4The advantages of this technology are clear, such as faster screening processes, early identification of potential issues, and improved efficiency in dispensing⁵. However, these benefits must be weighed against potential risks like biased results, data breaches, and loss of skills if human roles are properly not redefined.^6,7

1.1 AI-Driven Innovation in Pharmacy:

1. Drug discovery and preclinical development:

· Virtual screening and de novo design: Deep learning and generative AI allow screening of billions of molecules and creation of novel scaffolds.^7,8

· Property and toxicity prediction: Machine learning models forecasts solubility, permeability, metabolic stability, and toxicity, decreasing reliance on animal testing.^3,9

· Structural biology: Protein structure prediction model speeds up target validation and development.¹⁰

Note: Case studies show AI assisted screening can cut hit-to-lead timelines in half.^1,7

2. Pharmaceutical technology and formulation:

· AI predicts excipient compatibility, dissolution, and stability, minimizing experimental workload.¹¹

· Manufacture employs AI for real-time process monitoring, anomaly detection, and yield optimization.^12,13

3. Clinical pharmacy and dispensing:

· AI powered robotic dispensing lowers error rates and enhance productivity.¹⁴

· Clinical decision support systems (CDSS): Predict adverse drug reaction (ADRs), drug interactions, and dosing errors.^4,15

4. Personalized and precision pharmacy:

· AI models use genetic, physiological, and therapeutic drug-monitoring data for precise dosing, particularly in drugs with narrow therapeutic indices.^16,17

Table 1: AI Applications in Pharmacy

Application Area	AI Contribution	Benefit
Drug discovery	Virtual screening, de novo design, ADMET prediction ^1,7	Faster hit identification, cost reduction
Formulation and manufacturing	Predict compatibility, optimize process ^3,19	Fewer trials, higher yield, stable products
Clinical decision support	DDI alerts, ADR prediction ^4,12,21	Safer prescribing, fewer adverse events
Automation and logistics	Robotic dispensing, inventory forecasting^11,20	Reduced errors, efficiency
Patient engagement	AI chatbots, adherence apps ¹⁸	Better adherence, counselling support

2: Ethical, Legal and Social Considerations:

1. Data privacy and management:

Pharmacy data including medication history and genomics need to be protected via encryption and governed under HIPAA/GDPR standards.^22,23

2. Bias in algorithms and fairness:

Artificial intelligence may increase healthcare disparities if trained on biased data. A notable case revealed an algorithm underestimated health risks for black patients was used as a proxy.²⁴

3. Clarity and transparency:

Black- box AI decreases trust. Explainable AI (XAI) is essential for supporting pharmacy decisions.^25,26

4. Responsibilities and roles:

Pharmacists should remain the final decision-makers. AI should support, not substitute, professional judgment.²⁷

Table 2: Ethical Challenges of AI in Pharmacy

Concern	Implication	Mitigation
Data privacy	Patient identity risks²²	Encryption, access controls, minimal use
Bias and fairness	Under-treatment of minorities²⁴	Diverse datasets, fairness audits
Transparency	Clinician distrust of black-box outputs²⁵	Explainable AI, confidence intervals
Overreliance	Reduced human oversight²⁸	Mandatory pharmacist validation
Liability	Legal ambiguity for AI-driven errors²⁷	Regulatory clarity, audit trails

3. Impacts and Measurable Outcomes:

1. Pharmacist productivity:

AI driven automation increases efficiency by approximately 20-33%.¹⁴

2. Dispensing errors:

Robotic central fill systems report near zero error rates in controlled trials.¹⁴

3. Drug discovery timelines:

AI assisted platforms accelerate lead identification, reducing timelines by up to 50%.^1,29

4. Bias detection:

Audits reveal subgroup disparities, emphasizing the need for fairness monitoring.³⁰

Table 3. Performance Indicators of AI in Pharmacy³¹

Indicator	Impact
Productivity	+15–33% pharmacist output
Error reduction	Near-zero dispensing errors
Time-to-lead discovery	~2× faster hit-to-lead
Bias detection	Subgroup disparities noted

4. Implementation Roadmap:

· Pilot projects: Being with low-risk applications such as inventory forecasting.¹⁴

· Data governance: Create high quality, unbiased database.²²

· Validation: Apply prospective clinical validation and subgroup testing.²⁴

· Governance: Set up oversight boards and ethics committees.^25,32

· Training: Provide education for pharmacists on AI literacy and their oversight responsibilities.²⁷

5. CONCLUSION:

Artificial Intelligence is no longer just a futuristic idea; it is already changing the pharmacy industry. Its application in various areas such as drug discovery, formulation development, pharmaceutical manufacturing, and accuracy. AI helps reduce the failure rate of potential drug candidates, improves the prediction of how drugs behave in the body and their toxic effects, and speed up the process of moving promising compounds into clinical trials. In clinical and community pharmacy settings, automation and AI based decision support systems help reduce errors in dispensing medications, improve patient safety, and allow pharmacists to spend more time on patient centred care.

At a time, the growing use of AI raises new ethical and social concerns. Matters like data privacy, biased algorithms, transparency, and accountability must not be ignored. If not properly managed, these tools meant to enhance healthcare might unintentionally worsen existing inequalities or create new risks. Therefore, the adoption of AI in pharmacy must be supported by strong regulatory structures, fairness checks, tools that explain AI decisions, and ongoing human supervision.

ACKNOWLEDGEMENT:

The authors sincerely acknowledge Ms. Priyanka S. Jadhav and Ms. Rutuja A. Patil for their guidance and support throughout the preparation of this review. The author also thank Ms. Priya N. Shendage for her valuable assistance. The authors are grateful to the faculty and staff of Shree Santkrupa College of Pharmacy, Ghogaon, for providing the necessary facilities. The authors also acknowledge all researchers whose work contributed to this review.

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Received on 02.12.2025 Revised on 10.01.2026

Accepted on 12.02.2026 Published on 21.04.2026

Available online from April 24, 2026

Res. J. Pharma. Dosage Forms and Tech.2026; 18(2):137-140.

DOI: 10.52711/0975-4377.2026.00021

This work is licensed under a Creative Commons Attribution-Non Commercial-Share Alike 4.0 International License. Creative Commons License.

Table 1: AI Applications in Pharmacy

Table 3. Performance Indicators of AI in Pharmacy31

Table 3. Performance Indicators of AI in Pharmacy³¹