Artificial intelligence (AI) enables computers and digital systems to perform functions that resemble human learning, reasoning, and problem-solving. AI is reshaping pharmacy practice at a rapid pace, influencing clinical care, operational efficiency, and patient interactions in meaningful ways.
The benefits of AI in pharmacy extend across the full spectrum of patient care. AI can strengthen personalized medicine through pharmacogenomics, clinical decision support, and predictive analytics that enhance medication safety. It improves operational efficiency by streamlining workflows, enhancing inventory accuracy, and supporting billing and regulatory compliance. AI can reinforce patient safety through adverse-event detection, provide educational tools, and accelerate drug development by improving clinical trial design and identifying drug repurposing opportunities.
Despite these advantages, integrating AI into pharmacy introduces ethical responsibilities that must be addressed to protect patient well-being. Issues such as data privacy, algorithmic bias, transparency, accountability, equitable access, and the need for meaningful human oversight require ongoing evaluation to uphold ethical standards while adopting new technologies.
Data Privacy and Security
AI systems require large volumes of sensitive patient information, which raises concerns about confidentiality and regulatory compliance, including requirements under the Health Insurance Portability and Accountability Act (HIPAA).
Data Breaches and Cyberattacks
AI-driven environments face elevated risks involving unauthorized access to patient data, intellectual property, and drug information. Increased digital connectivity creates vulnerabilities that attackers may exploit through ransomware, phishing, or manipulation of AI data pipelines.
Re-Identification of De-Identified Data
Even anonymized data can sometimes be reconstructed by combining multiple datasets with advanced algorithms, creating the risk of re-identification. This highlights the need for strong data governance practices.
Third-Party Vendor Risks
A significant percentage of health-related data breaches occur due to compromised vendors. When external partners manage or access sensitive records, weaknesses in their systems can serve as points of entry for attackers.
Algorithmic Bias and Fairness
AI models depend on the quality and diversity of training data. When datasets are incomplete, unbalanced, or inaccurate, the resulting outputs can reinforce inequities or produce unsafe recommendations.
Biased Training Data
Training data that does not reflect diverse patient populations can lead to clinically unreliable results.
Lack of Data Diversity
Comprehensive datasets should represent diversity across demographic, cultural, behavioral, and geographic factors to ensure accurate and equitable AI performance.
Information Bias
Errors in data collection, measurement, or processing can distort outcomes and produce discriminatory effects.
Unintended Feedback Bias
Feedback loops within AI models can unintentionally reinforce flawed assumptions if they are not monitored by human experts.
Transparency and Explainability
Clear and understandable AI processes help pharmacists evaluate the accuracy and relevance of AI-generated recommendations. Transparency strengthens patient safety and supports shared decision-making by ensuring that providers and patients understand the basis for treatment options.
Human Oversight and Control
Human judgment remains essential in all AI-supported medication processes. Pharmacists bring clinical expertise, critical reasoning, and ethical judgment that AI cannot replicate. Regulatory discussions increasingly focus on human-in-the-loop and human-on-the-loop models, which emphasize continuous human supervision at critical steps in the AI workflow.
Equitable Access
Health equity in AI-supported care means ensuring that all communities, including those across Bronx, NY, benefit equally from technological innovation. AI should reduce, rather than deepen, existing healthcare disparities.
In many Bronx neighborhoods, access to consistent and supportive pharmaceutical care often relies on local pharmacies that function as trusted healthcare partners. A responsible community pharmacy in the Bronx, such as Mediserv Pharmacy, demonstrates how services like medication synchronization, delivery, vaccinations, and patient support can integrate technology while maintaining the personal care patients depend on.
Accountability and Liability
Responsibility for AI use in pharmacy involves several stakeholders.
- Pharmacists: They must continue to use professional judgment when relying on AI tools. Ignoring flawed AI recommendations, or failing to use available AI that might prevent errors, may lead to liability.
- Health Care Institutions: Pharmacies and hospitals must ensure proper staff training, reliable system selection, and sufficient oversight to prevent negligence.
- AI Developers and Vendors: They may be held liable for coding issues, inadequate testing, undisclosed risks, or biased data that results in inequitable clinical outcomes.
Patient Autonomy and Informed Consent
Patients must be informed about how AI influences their care. Understanding how AI evaluates information and contributes to recommendations supports patient autonomy and allows individuals to make informed decisions.
Regular Monitoring and Auditing
AI tools must be consistently monitored to ensure accuracy over time. Model performance may decline or shift as data evolves; a phenomenon known as model drift. Regular evaluation ensures that AI systems remain aligned with current clinical needs and best practices.
AI presents enormous opportunities to enhance pharmacy practice, particularly in diverse urban regions such as Bronx, NY. Realizing this potential requires strong ethical oversight, transparency, fairness, and continual human involvement. By working collaboratively, pharmacists, healthcare leaders, AI developers, and regulators can ensure that AI strengthens patient care while maintaining the highest ethical standards.
REFERENCES
- What is AI? IBM website. August 9, 2024. Accessed September 9, 2025.
https://www.ibm.com/think/topics/artificial-intelligence - Artificial Intelligence in Pharmacy: Appropriate Use of AI (Part 1 of 2). Florida Pharmacy Foundation website. Accessed September 9, 2025.
https://flpharmfound.org/artificial-intelligence-in-pharmacy-part-1 - Li J. Security Implications of AI Chatbots in Health Care. J Med Internet Res. 2023;25:e47551. doi:10.2196/47551.
- Erosion of Anonymity: Mitigating the Risk of Re-Identification of De-Identified Health Data. Health Law Advisor website. February 28, 2019. Accessed September 10, 2025.
https://www.healthlawadvisor.com/erosion-of-anonymity-mitigating-the-risk-of-re-identification - More Than One-Third of Data Breaches Due to Third-Party Supplier Compromises. The HIPAA Journal website. March 28, 2025. Accessed September 10, 2025.
https://www.hipaajournal.com/more-than-one-third-data-breaches-third-party-compromises/ - Norori N, Hu Q, Aellen FM, Faraci FD, Tzovara A. Addressing bias in big data and AI for health care: A call for open science. Patterns (N Y). 2021;2(10):100347. doi:10.1016/j.patter.2021.100347.
- Singh R, Paxton M, Auclair J. Regulating the AI-enabled ecosystem for human therapeutics. Commun Med (Lond). 2025;5(1):181. doi:10.1038/s43856-025-00910-x.
- Health Equity and Ethical Considerations in Using Artificial Intelligence in Public Health and Medicine. Centers for Disease Control and Prevention website. August 22, 2024. Accessed September 10, 2025.
https://www.cdc.gov/pcd/issues/2024/24_0245.htm
