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Exploring bias risks in artificial intelligence and targeted medicines manufacturing.
Ngozi Nwebonyi1, Francis McKay2
1Department of Translational Health Sciences, Bristol Medical School, Learning and Research Building, University of Bristol, Level 1 Southmead Hospital, Bristol, BS10 5NB, UK.
BMC Medical Ethics
|October 16, 2024
Summary
Artificial intelligence in targeted medicines manufacturing may introduce bias risks. Some biases could potentially correct health inequalities, but require further critical reflection before implementation.
Area of Science:
- Bioethics
- Precision Medicine
- Artificial Intelligence
Background:
- Artificial intelligence (AI) offers significant healthcare value but risks amplifying health inequalities through bias.
- Targeted medicines manufacturing, utilizing digitalized systems and AI, faces uncertain bias risks due to its novelty.
Purpose of the Study:
- To explore potential bias risks in AI-driven targeted medicines manufacturing.
- To examine stakeholder perspectives on bias in this emerging field.
Main Methods:
- Conducted eleven semi-structured interviews with stakeholders across bioethics, precision medicine, and AI.
- Analyzed interview data to identify opinions on bias in AI-driven targeted therapies manufacturing.
Main Results:
- Bias can emerge in both upstream (R&D) and downstream (production) stages of targeted medicines manufacturing.
- Downstream processes less reliant on patient data may pose lower bias risks.
- A spectrum of bias meanings was identified, including potentially "corrective bias" that could address health inequalities.
Conclusions:
- The concept of "corrective bias" presents a novel perspective, challenging the purely negative view of bias.
- While "corrective bias" may offer a way to address health inequalities, it requires significant critical reflection before practical application.
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