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Healthcare ethics


records might overlook individuals with limited healthcare access, skewing data towards wealthier or urban groups.


l Data preprocessing: Decisions made during data cleaning can introduce bias. Removing outliers, for instance, could disproportionately impact minority groups whose health patterns differ from the majority.


l Feature selection: Choosing variables can reinforce bias. For instance, using postcodes as a proxy for race can lead to unfair outcomes.


l Model development: Algorithms can embed biases based on design or optimisation criteria. A focus on overall accuracy, for example, might compromise fairness across demographic groups.


l Model evaluation: Standard metrics may miss biases; assessing performance across subgroups helps ensure fairness.


l Deployment and monitoring: Ongoing monitoring post-deployment is crucial, as real-world usage may reveal new biases.


By maintaining a critical eye on bias throughout these stages, we can work towards creating AI systems that are not only accurate but also equitable in their application across diverse patient populations. This comprehensive approach is essential for building trust in AI- driven healthcare solutions and ensuring they benefit all patients equally.16


3. Transparency in AI decision-making The “black box” nature of deep-learning-based AI algorithms poses significant ethical challenges, particularly in critical healthcare settings where clinicians need to understand and explain treatment decisions.17


To address transparency


concerns: l Implement explainable AI systems where possible.


The “black box” nature of deep-learning- based AI algorithms poses significant ethical challenges, particularly in critical healthcare settings where clinicians need to understand and explain treatment decisions


l Maintain clear documentation of AI decision- making processes.


l Develop protocols for situations where AI recommendations conflict with clinical judgment.


l Ensure clinicians understand both the capabilities and limitations of AI systems.


Some experts criticise AI for its lack of flexible thinking. For instance, AI may identify a prominent feature and build its reasoning around it (i.e., acting in a ‘logical and direct’ way). However, there are times when it’s necessary to consider a less obvious factor, even if it seems less significant at that moment. In these situations, doctors often rely on intuition. For AI to develop a similar kind of ‘intuition,’ it would likely require an extensive database of diverse cases. Current AI systems excel at recognising


prominent features but often lack the causal understanding and contextual awareness that human intuition has, particularly in complex fields like medicine. This limitation comes from how heavily they rely on training data and the challenges of generalising to novel situations. Research in causal AI, meta-learning, and neuro- symbolic approaches aims to enhance AI’s adaptability and mimic human-like reasoning. However, achieving true cognitive flexibility


comparable to human intuition - a key requirement for artificial general intelligence (AGI) - remains an unmet challenge. In the meantime, a practical solution is to work on


fine-tuned models for a specific downstream task.18,19


However, this strategy requires substantial domain expertise and extensive high-quality training data tailored to the target task. Such task-specific models, while effective within their defined scope, demand significant resources for both development and validation. The quest for fairness and equity in healthcare delivery becomes more complex with AI implementation. Algorithmic bias can perpetuate or exacerbate existing healthcare disparities if not carefully monitored and corrected. Transparency and accountability in AI systems must be balanced with their technical complexity, ensuring patients and healthcare providers can trust AI-driven decisions while maintaining system sophistication.20 The burning question is: how will the boundaries of a doctor’s responsibility be defined when relying on SaMD (Software as a Medical Device) for decision-making? Doctors sometimes assume full responsibility when making risky choices. But what if they follow AI guidance from SaMD officially used in their clinic? And what happens if SaMD suggests one course, while the doctor’s experience points to another? Clear guidelines are essential to protect doctors when working with AI-driven SaMD. While legal questions about responsibility and liability are critical concerns, AI model development necessitates collaboration with medical professionals for relevant integration. This approach enables clinicians to evaluate model outputs critically, incorporates robust trustworthiness metrics, establishes clear usage protocols, and defines liability boundaries. By fostering this collaboration, we create systems that augment clinical judgment while preserving physician autonomy and patient safety.


Use cases and implementation challenges 1. Clinical diagnosis & medical imaging Specific example: In radiology and thoracic imaging, AI models have demonstrated the capability to detect cancerous lesions and analyse medical images with accuracy comparable to or exceeding human dermatologists, as shown in rigorous studies published in scientific journals.21 Key considerations:


20 www.clinicalservicesjournal.com I January 2025


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