Most of us do not doubt that our car mechanic knows more about the inner workings of the internal combustion engine or the synchronized gearbox than we do, and that they also know more about interp...

The rapid progress and expanding development of machine learning-driven clinical decision support systems (ML_CDSS) have led to calls for involving “humans in the loop” in the design, development,...

Salloch and Eriksen (2024) present a compelling case for including patients as co-reasoners in medical decision-making involving artificial intelligence (AI). Drawing on O'Neill’s neo-Kantian frame...

Integrating artificial intelligence (AI) into healthcare can potentially revolutionize how clinical decisions are made. Advancements in AI-driven Clinical Decision Support Systems (AI_CDSS) are enh...

There is significant potential for machine learning (ML) models and systems to enhance prognostic, diagnostic, and therapeutic decision-making in the healthcare context. When used in clinical setti...

In their target article, Salloch and Eriksen (2024) argue that a “meaningful process of interrogating” between physicians and patients is the most appropriate way to evaluate medical AI, supporting...

In their article “What are Humans Doing in the Loop? Co-Reasoning and Practical Judgment When Using Machine Learning-Driven Decision Aids,” Salloch and Eriksen (2024) propose involving patients as...

In “What Are Humans Doing in the Loop? Co-Reasoning and Practical Judgment When Using Machine Learning-Driven Decision Aids,” Salloch and Eriksen (2024) address the tension between algorithm explai...

In this article, we use the account of the “inhuman” that is developed in the work of the French philosopher Jean-François Lyotard to develop a critique of human-centred AI. We argue that Lyotard’s philosophy not only provides resources for a negative critique of human-centred AI discourse, but also contains inspiration for a more constructive account of how the discourse around human-centred AI can take a broader view of the human that includes key dimensions of Lyotard’s inhuman, namely performativity, vulnerability, and (...) novelty. We link these dimensions to questions of epistemic injustice and social recognition. In this way, we hope to develop a proposal for what a richer and more meaningful understanding of human-centred AI might look like. (shrink)

In “What are Humans Doing in the Loop?” Salloch and Eriksen (2024) argue for a collaborative decision-making approach to using machine learning-based AI decisional support systems in medicine, rece...

The common “human in the loop” narrative in artificial intelligence (AI) implementation is in critical need of analysis and explanation, as Salloch and Eriksen (2024) rightfully argue. Researchers...

The paper by Salloch and Eriksen (2024) offers an insightful contribution to the ethical debate on Machine Learning-driven Clinical Decision Support Systems (ML_CDSS) and provides much-needed conce...

When using Artificial Intelligence (AI) in medical settings, the critical role of humans in decision-making and accountability is often emphasized using terms such as “human-in-the-loop (HITL).” Sa...

Recent developments in ML driven decision support systems have played an important role in clinical decision making, whether one consider clinical decisions that involves image recognition (Berge e...

At a time in medicine where the cost of human clinical labor continues to rise, with an overall shortage of providers at all levels, improved AI performance raises critical, pressing questions with...

According to Sutton et al. a “clinical decision support system (CDSS) is intended to improve healthcare delivery by enhancing medical decisions with targeted clinical knowledge, patient information...

Machine learning-driven decision support systems (ML_CDSS) are poised for increasing presence and influence in clinical contexts. Salloch and Eriksen (2024) make two key arguments, that together bu...

Bioethical consensus promotes a shared decision making model, which requires healthcare professionals to partner their knowledge with that of their patients—who, at a minimum, contribute their valu...