Ethical considerations for AI in Pharmacy

Dr. Yasmin Karsan explores the potential ethical implications of AI in pharmacy and how to address them

Over the last few years pharmacy and the role of pharmacy teams has changed dramatically. The introduction of technology and the integration of artificial intelligence (AI) within systems that provide healthcare and support to pharmacies holds great promise; for improving access to health, supporting patient outcomes, and optimising operational efficiency.

Currently, the potential use of AI could span the whole of the medicines value chain, from AI-driven drug discovery to personalised medicines and automated dispensing systems. However, the rapid advancement of AI technology does raise several ethical concerns. This article will explore these concerns and how they can be addressed.

In previous articles, I have discussed what underpins artificially intelligent machines and the importance of data. Datasets are the foundation on which AI algorithms learn and generate conclusions. The first step to understanding the potential ethical implications of AI across the pharmacy sector is to understand the data that is held within these foundational datasets.

Patient privacy and data security

AI systems in and outside of the pharmacy sector, which support patient care, rely heavily on vast amounts of medical data (patient medical records, PMR data, etc). However, the collection, storage, and use of such sensitive data can possibly bring significant privacy concerns.

GDPR compliance is essential within the UK and ethical questions arise around data ownership, patient consent, and the possibility of data and cybersecurity breaches. As frontline healthcare professionals, we need to be able to support our patients when questions are asked about their data. For example, how can patients be sure their data is used only for its intended purposes? Is anonymised data truly safe from de-identification techniques that could expose private information?

Cross contamination of data across sectors is also a concern, for example, confidential healthcare data used for financial or insurance applications also holds some ethical concerns. Addressing these issues requires robust cybersecurity measures, strict access controls, and transparency.

Bias in AI algorithms

There is a term coined when discussing the generation of AI algorithms – ‘Rubbish in, rubbish out’. AI algorithms, especially those used in clinical decision-making and personalised treatment, are only as good as the data they are trained on. Many datasets used are not representative of certain diversity in populations, therefore biases can emerge which could result in unequal treatment, where certain groups (e.g., minorities) may not receive the same level of care or have access to care or medications. This would also be important when adopting new technologies which may have been developed in less diverse countries. For example, an AI tool designed in a country with a predominantly homogeneous population may not be as effective in a multicultural society. Therefore, it is ethically imperative to ensure that AI systems in pharmacy are trained on diverse data and are continuously monitored for fairness and inclusivity.

Accountability and responsibility

As AI tools become more involved in decision-making processes, questions of accountability arise. Who is responsible if an AI-driven recommendation leads to incorrect therapy? Is it the pharmacist, the AI developer, or the healthcare institution?

Pharmacists are ultimately responsible for patient care, but when AI is involved, they may rely on machine-generated recommendations. To ensure ethical practice, pharmacists must retain the authority to override AI recommendations and exercise their clinical judgment. Moreover, developers of AI systems need to provide transparency in how algorithms make decisions. Without this, patient safety becomes compromised.

Informed consent and autonomy

In pharmacy, informed consent is a core ethical principle. Patients have the right to know the options available for their treatment, the potential risks and benefits, and the rationale behind specific therapeutic decisions. With AI-driven recommendations, it is essential that patients are informed not only about the treatment options but also about the involvement of AI in the decision-making process, making it critical that pharmacy teams understand why certain recommendations have been suggested.

AI-based systems often present recommendations based on probabilities or patterns that may not be easily understood by patients. Pharmacists and healthcare providers have an ethical duty to explain these decisions in a way that patients can comprehend.

Job displacement and workforce implications

The increasing use of AI in pharmacy raises concerns about job displacement. Automated systems for dispensing medications or AI-driven clinical decision support tools deliver the perception that these tools could potentially reduce the need for human pharmacists or pharmacy technicians. This may lead to workforce restructuring or a change in the role of pharmacy teams. Continuous education and training should be provided to help pharmacy teams to adapt to the changing landscape. Rather than replacing roles, AI should be viewed as a tool to augment their capabilities, allowing them to focus more on patient care, counselling, and complex clinical decisions.

Access and equity

AI has the potential to revolutionize pharmacy, but there is a risk that these advances may not be accessible to everyone. Patients in rural areas, low-income communities, or developing countries may not have the same access to AI-driven healthcare tools and personalised therapies as those in urban or wealthy regions. This creates ethical concerns around equity in healthcare delivery.

Healthcare systems and policymakers must work to ensure that the benefits of AI in pharmacy are distributed equitably. This could involve investing in digital infrastructure in underserved areas, creating affordable AI-driven tools, and ensuring that marginalised populations are not left behind in the AI revolution. It is also important to develop global standards for the use of AI in healthcare to reduce disparities between countries.

Conclusion

The use of AI in pharmacy holds immense potential to enhance patient care, streamline processes, and improve healthcare outcomes. However, these benefits must be weighed against the ethical considerations that arise from its implementation. Ensuring patient privacy, mitigating bias and maintaining accountability are crucial challenges that must be addressed.

Pharmacy teams across all areas of pharmacy (from policy to practice) have a shared responsibility to ensure that AI is implemented ethically and equitably, with patients at the focal point. As AI continues to evolve, ongoing dialogue, transparency, and ethical scrutiny will be necessary to harness its power whilst safeguarding those within the healthcare ecosystem.

Authored by Dr Yasmin Karsan, MRPharmS, PhD, MSc, IP

Pharmacist, Clinical Safety Officer and AI Engineer