Healthcare ethics
l The technology has shown promise for X-rays, MRIs and detecting cancer abnormalities.
l However, a major bias issue emerged with skin cancer detection algorithms that were primarily trained on fair-skinned populations, making them less effective for people of colour.
l This highlights the critical need for diverse, representative training data.
A recent publication revealed that AI classified all skin change images as pathological solely due to the presence of a ruler. This highlights a dual challenge: while AI can identify subtle nuances that human eyes might miss, it’s crucial to flag cases needing specialist input for second opinions. Ongoing issues include image accuracy; surgeons often find discrepancies between diagnostic imaging and actual disease extent. Additionally, images should correlate with clinical findings. We must also consider the risk of data distortion from computer viruses. Can AI recognise anomalies, or will it provide a definitive yet misleading diagnosis? The challenge of AI image misclassification, exemplified by the ruler case, demonstrates the issue of spurious correlations in deep learning systems. This highlights why we need medical- specific foundation models and fine-tuned architectures that better understand clinical imaging nuances. However, with increasing AI-enabled
cyberattacks in healthcare, we must prioritise both model accuracy and robust cybersecurity measures to protect system integrity. Success requires advancing model development, implementing rigorous validation protocols, and fostering meaningful collaboration between healthcare professionals and AI systems.
2. Patient triage and resource allocation During the COVID-19 pandemic, DeepSOFA was developed as an AI version of the Sequential Organ Failure Assessment score to help predict mortality and guide resource allocation decisions.22 Key considerations:
l It can help optimise scarce resources like hospital beds and ventilators.
l Raises ethical concerns about algorithmic bias affecting access to care.
l Must ensure human oversight of critical care decisions.
l Requires transparency about how algorithms make prioritisation decisions.
It’s crucial to emphasise that AI, including large language models (LLMs), does not possess consciousness or genuine understanding of
human suffering. These systems are essentially processing tokens - strings of text or numerical data - based on patterns learned from training data. They lack the emotional intelligence,
empathy, and nuanced understanding that human healthcare professionals bring to these sensitive situations. This limitation of AI systems underscores the importance of managing expectations around AI capabilities in healthcare settings.
Healthcare professionals and patients must be clearly informed about what AI can and cannot do. AI should enhance human decision-making, not replace it, especially in ethically complex situations like end-of-life care. Moreover, AI developers bear the responsibility of embedding ethical considerations in their designs. While AI lacks empathy, developers can ensure models flag cases needing human intervention and prioritise patient well-being over economic factors.
Future directions and recommendations To ensure the ethical implementation of AI in acute healthcare settings, we recommend the following:23,24,25
1. Regulatory Framework Development l Create specific guidelines for AI use in acute care settings.
l Establish clear accountability mechanisms.
l Develop standards for AI system validation and testing.
2. Educational Initiative l Implement comprehensive training programmes for healthcare professionals.
l Develop resources for patient education about AI in healthcare.
l Foster interdisciplinary collaboration between technical and medical experts.
3. Continuous Evaluation and Improvement l Regular assessment of AI system performance and impact.
l Ongoing monitoring for bias and ethical concerns.
l Continuous updating of protocols based on emerging evidence and experience.
Conclusion The integration of AI in acute healthcare settings represents both an extraordinary opportunity and a significant ethical challenge. Success lies in maintaining a careful balance between technological advancement and ethical healthcare delivery. In short, AI should
Dr. Julia Mokhova is Head of Medical at Vivanti, bringing extensive clinical experience to the intersection of healthcare and technology. Her practical approach to AI integration in healthcare ensures that technological advancement serves the fundamental goal of improving patient care while maintaining essential human elements in medical practice.
January 2025 I
www.clinicalservicesjournal.com 21
be “an assistant, but not a doctor”. The path forward requires continuous dialogue between healthcare professionals, technologists, ethicists, and patients. By addressing ethical considerations proactively and maintaining focus on patient welfare, we can harness AI’s potential, while preserving the human element that lies at the heart of healthcare delivery. Only through this balanced approach can we ensure that AI serves its intended purpose: enhancing, rather than replacing, the critical human elements of healthcare delivery in acute settings.
References for this article are available upon request.
. About the authors
CSJ
Kenza Benkirane is the AI Lead at Vivanti, specialising in the development and implementation of ethical AI solutions in healthcare settings. With extensive experience in AI and machine learning, she focuses on creating robust, unbiased algorithms that enhance healthcare delivery while maintaining ethical standards.
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