Jul 2026· Journal of Perinatology· 1 citation· 12 references
Medicine
TL;DR
Artificial intelligence (AI) may assist physicians by providing easy access to existing knowledge and discerning data patterns, perhaps previously unrecognized, however, whether AI can replace human decision-makers altogether remains an open question.
This Perspective concerns the most consequential of these applications: the autonomous triage of self-presenting, undifferentiated patients, with little or no clinician in the loop, and the evidence of safety does not yet exist.
Shayndhan Sivanathan, Shravan Nageswaran, Mehdi Zadem et al.· arXiv.org· 0 citations
Artificial intelligence (AI) is increasingly promoted as a tool to enhance clinical decision-making and thus improve the quality of healthcare. While much of the emerging scholarship on AI and healthcare in Africa has focused broadly on opportunities and systemic challenges, what remains underexplored is the specific application of AI to clinical decision-making. This paper contributes to addressing this gap by offering a conceptual and critical analysis of AI-based clinical decision support systems (AI-CDSS) in African contexts. Drawing on philosophical accounts of medical reasoning and relational moral frameworks such as Ubuntu, the paper draws on the moral ecology of care and shows that algorithmic systems can reconfigure epistemic authority, redistribute responsibility, and risk marginalising context-sensitive and relational dimensions of care. The paper further argues that AI systems are better understood as socio-technical mirrors that reflect and amplify existing human values, institutional arrangements, and power asymmetries. Moving beyond the algorithm, it proposes a shift toward relational and context-sensitive AI governance, including the development of relational impact assessments, the redistribution of responsibility across the AI lifecycle, and the co-production of knowledge with local stakeholders. While focusing on African clinical contexts, the analysis offers broader insights for global debates on AI ethics and clinical decision-making.
K. M. Mussie· Science and Engineering Ethi...· 0 citations
Healthcare is expected to benefit from advances in Artificial Intelligence (AI). But the pace of translation into practice has lagged behind expectations. This AI Chasm in healthcare may be an expression of insufficient attention being given to how humans and such machines need to work well together. This thesis has a specific contextual motivation in the work of the pre-hospital critical care teams of the Welsh National Health Service. These teams use a continual and intensive triage process to arrive at decisions on activating their specialist resources via an air ambulance service or a fleet of rapid response road vehicles. The work here investigates hybrid human-machine combination in decision-making and makes four contributions. First, an approach to designing for hybridity that lays out the dimensions of human-machine combination drawn from an extensive review of relevant theory and of recent studies. Second, a detailed account of how the situatedness of human action in the given clinical context is shown to affect the most appropriate design and development of an algorithmic support system. In the third contribution, an empirical study, we show how clinicians in this context can make positive use of algorithmic support, assuring that under-reliance is not a barrier to securing benefit. Specifically, we run a three-arm study in which users are exposed to support from a human expert score on the one hand, an algorithmic score on another and a no-score condition on the third arm. The presence of either score condition has a statistically significant positive influence on critical care triage decisions (p < 0.05) compared to having no assistive score. But the influence of whether the score is presented as algorithmic or human in provenance is not statistically significant (p ≈ 0.8). User satisfaction withour interface design is entirely positive, while satisfaction with the assistive value of the proposed tool is largely positive. There is a positive skew to the predisposition of hub staff to embrace AI technologies. Nevertheless, predisposition towards AI systems does not appear to predict satisfaction with the proposed algorithmic tool. The fourth contribution, a further study, shows how over-reliance is countered by the highly skilled participants while their ability to discriminate between the quality levels of different machines is maintained. In this study, support tools with three very different performance levels are presented to users. The approximate sensitivities of the tools are 80%, 63% and 40%. The differences between decision results obtained with each tool are not statistically significant (p > 0.7 in all paired tests). However, when comparing results here with the no-score treatment from the earlier study, we find that the influence of each of the two higher quality tools is to improve decision performance significantly (p < 0.05 in each case). In a striking result, we also find that subjective judgements achieve statistically significant discrimination between the different tools, ranking them in order of sensitivity (p < 0.05 in all paired tests). Critically, we establish that a realistic baseline performance for a Random Forest classifier on this task is 63%. And at this tool performance level, a tool appears to have a significant positive influence on decisions. Importantly, from our results, if a tool were to have significantly lower performance, it does not follow that the quality of decisions would decline below that of a decision-maker without support. In other words, poor tool performances is not found to be detectably unsafe. Human decision-makers do express the greatest concerns, however, at the high False Negative Rate of a poorly-performing machine. The thesis identifies that practical development on a larger dataset is not only possible, but likely to yield measurable benefit for the service and the public it serves. It concludes by drawing together the significance of studying situated action as a precondition and situated evaluation as a success factor in order to put human combination with machines into real effect for human benefit.
It is concluded that, without significant changes in the health care system's financial incentives and market structures, AI will not slow cost growth.
B. Kocher, Brian Zhao, Erin Duffy· NEJM catalyst innovations in...· 0 citations
Evidence-based clinical decision support artificial intelligence (AI) is rapidly expanding, but its safe and effective use depends on rigorous validation, trustworthy evidence sources and careful integration into clinical workflows. Current available systems show strong potential to improve diagnostic accuracy, reduce clinician workload and possibly benefit patient care, but challenges remain before its real-world adoption. We must be responsible in its integration to ensure AI truly strengthens clinical judgment. This article is a viewpoint of AI tools for clinical decision support, addressing a rapidly evolving field and provides insights that are useful for clinicians and educators in clinical settings. When used within appropriate medical training, AI may help augment diagnostic accuracy and improve efficiency. Nevertheless, while AI offers promising features, it also presents ethical and reliability challenges, which may negatively affect the medical professional identity.
Jorge Cervantes, B. Vashi· The Clinical Teacher· 0 citations
The development of artificial intelligence (AI) and artificial general intelligence (AGI) extends beyond diagnostic and therapeutic support in medicine and increasingly penetrates overall clinical decision-making. At the same time, owing to their high levels of accuracy and statistical persuasiveness, AI systems tend to acquire excessive authority over medical judgment. This tendency often manifests as an overreliance on AI and carries the risk of undermining the professional judgment, moral agency, and accountability of healthcare practitioners. Medical practice is not a merely technical activity but a bioethical endeavor oriented toward the dignity of the “persona” and the care of the whole person (cura personalis). Bioethical judgment and accountability, therefore, can be attributed only to human agents. Although AI can support medical decision-making through probabilistic recommendations, it is ontologically limited in that it cannot perform value-based medical judgments or exercise conscientious moral discernment. Consequently, AI attains bioethical legitimacy only insofar as it remains an auxiliary tool rather than a substitute for the judgment of medical professionals. Grounded in the principles of subsidiarity and accountability of healthcare personnel, medical practice in the era of AI can thus be preserved as a bioethical practice that integrates technical rationality within human bioethical deliberation and accountability.
Hun‐Sung Kim· Catholic Institute of Bioeth...· 0 citations
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