Paper short abstract:

This paper examines the entanglements between ethics and AI in oncologic imaging, focussing on the socio-technological aspects of AI's transformative impact on medicine and healthcare, and highlights the implications for ethical governance in the case of medical AI.

Paper long abstract:

Artificial intelligence (AI) is transforming medical knowledge production, healthcare practices, and infrastructures. In response, various guidelines have been formulated to ensure that fundamental ethical principles, including transparency, fairness, accountability, and equity, are upheld throughout the development and application of medical AI. These principles are critical for safeguarding patient well-being, mitigating bias, and maintaining professional standards. Current discourses are focusing on the trustworthiness of AI and the risks associated with the adoption of AI technologies within healthcare settings. This paper presents the findings of an empirical analysis focused on unravelling the ethical and societal implications of AI in the domain of oncologic imaging. Within the realm of radiology, the advancement of AI is driven by imaginaries of enhancing diagnostic performance by increasing accuracy and simplifying expert’s decision-making, as it has been demonstrated to "outperform" humans. At the same time, concerns have been raised that AI in healthcare could amplify ethical and societal injustices. By delving into discursive conceptual ambiguities (such as explainability, interpretability, and transparency) and potential biases (particularly those related to sex and gender dimensions), employing an interdisciplinary and embedded ethics approach, and highlighting the significance of considering situated practices and stakeholder engagement as intersections where AI and ethics are entangled, the paper explores socio-technical conditions and knowledge production practices. Specifically, it reflects on algorithmic fairness, decision-making processes, and human oversight as essential components of ethical governance for medical AI.