首页|University of Bristol Reports Findings in Gene Therapy (Exploring bias risks in artificial intelligence and targeted medicines manufacturing)
University of Bristol Reports Findings in Gene Therapy (Exploring bias risks in artificial intelligence and targeted medicines manufacturing)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Biotechnology - Gene T herapy is the subject of a report. According to news reporting originating in Br istol, United Kingdom, by NewsRx journalists, research stated, "Though artificia l intelligence holds great value for healthcare, it may also amplify health ineq ualities through risks of bias. In this paper, we explore bias risks in targeted medicines manufacturing." Financial support for this research came from Future Targeted Healthcare Manufac turing (FTHM) Hub. The news reporters obtained a quote from the research from the University of Bri stol, "Targeted medicines manufacturing refers to the act of making medicines ta rgeted to individual patients or to subpopulations of patients within a general group, which can be achieved, for example, by means of cell and gene therapies. These manufacturing processes are increasingly reliant on digitalised systems wh ich can be controlled by artificial intelligence algorithms. Whether and how bia s might turn up in the process, however, is uncertain due to the novelty of the development. Examining stakeholder views across bioethics, precision medicine, a nd artificial intelligence, we document a range of opinions from eleven semi-str uctured interviews about the possibility of bias in AI-driven targeted therapies manufacturing. Findings show that bias can emerge in upstream (research and dev elopment) and downstream (medicine production) processes when manufacturing targ eted medicines. However, interviewees emphasized that downstream processes, part icularly those not relying on patient or population data, may have lower bias ri sks. The study also identified a spectrum of bias meanings ranging from negative and ambivalent to positive and productive. Notably, some participants highlight ed the potential for certain biases to have productive moral value in correcting health inequalities. This idea of ‘corrective bias' problematizes the conventio nal understanding of bias as primarily a negative concept defined by systematic error or unfair outcomes and suggests potential value in capitalizing on biases to help address health inequalities."
BristolUnited KingdomEuropeArtific ial IntelligenceBioengineeringBiotechnologyDrugs and TherapiesEmerging T echnologiesGene TherapyMachine Learning
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