Healthcare ethics
Implementing AI: the ethical frontier
The integration of AI in healthcare holds transformative potential to enhance patient care, but it also brings forward essential ethical considerations that need addressing to ensure fair and equitable deployment.1,2
Dr. Julia Mokhova
and Kenza Benkirane discuss this significant topic and argue that AI should be “an assistant, but not a doctor”.
Before we dive into the ethical concerns around the implementation of artificial intelligence in the healthcare system, it is helpful to understanding the key AI frameworks: l Machine Learning (ML): Algorithms that learn how to do a task without being explicitly programmed to do so. It uses data to make predictions or decisions. In healthcare, ML can analyse patient records to predict disease risk or recommend treatments based on a large dataset.3
l Computer Vision: Systems that process and analyse visual information from the world. In healthcare, this enables automated medical imaging analysis, from X-rays to pathology slides.4
l Natural Language Processing (NLP): Technology that processes and analyses human language. This paradigm also includes Large Language Models (LLMs), with advanced models such as GPT (used by ChatGPT) were trained on vast text datasets that can understand and generate human- like text. In healthcare, these can assist in medical research, documentation, and patient communication.5
While radiology remains the top AI application in healthcare due to the high availability of medical images,6,7
natural language processing (NLP) is
increasingly being utilised – both with the onset of digitalisation in healthcare settings and to help manage the heavy workload healthcare professionals increasingly face.8,9,10
Core ethical considerations AI implementation in healthcare must be guided by fundamental ethical principles that protect patient interests while promoting innovation.
1. Patient privacy and data protection The development and operation of healthcare AI systems require vast amounts of sensitive
patient data. In acute care settings, where immediate access to patient information can be crucial, there’s a delicate balance between data accessibility and privacy protection.11,12 Key considerations include:
l Implementing robust data encryption and access controls.
l Ensuring compliance with healthcare data protection regulations.
l Developing clear protocols for data sharing and storage.
l Maintaining transparent communication with patients about data usage.
2. Algorithmic bias and fairness One of the most pressing ethical concerns in healthcare AI is algorithmic bias. AI systems learn from historical data, which may contain inherent biases reflecting societal inequalities in healthcare access and treatment. In acute care settings, where rapid decisions are crucial, these biases could lead to discriminatory outcomes affecting vulnerable populations.13,14,15 To address this, healthcare organisations must:
l Regularly audit AI systems for bias across different demographic groups.
l Ensure training data represents diverse patient populations.
l Implement continuous monitoring systems to detect and correct bias in real-time.
l Maintain transparency about known limitations and potential biases in AI systems.
The issue of bias is extremely important. On one hand, we have an obligation to address and correct injustices rooted in the past. On the other, while it’s still debated, we can’t ignore that there are gender and ethnic differences, such as physiological variations or differing reactions to certain drugs. It’s therefore essential to adopt a balanced approach when designing the databases that AI relies on. To effectively combat algorithmic bias in healthcare, we must address it at every stage of AI development and implementation: l Data collection: Data collection may unintentionally exclude certain populations. For example, using only electronic health
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