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Algorithmic Diagnostics: Machine Learning, Cancer Detection and the Posthuman Transformation of Medical Knowledge

Year: 2025

Type: article

Abstract: This paper investigates the posthuman ramifications of machine learning (ML) in cancer cell detection, analyzing how artificial intelligence fundamentally alters the production of medical knowledge, reshapes human-machine interactions in healthcare, and contests anthropocentric notions of embodiment, agency, and mortality. This analysis examines recent advancements in multicancer early detection (MCED) technologies and deep learning methodologies, exploring how AI-mediated diagnosis challenges conventional medical epistemologies and creates novel posthuman assemblages that obscure the distinctions between human and non-human agencies in healthcare. Utilizing a posthumanist framework that acknowledges both the opportunities and risks of technological mediation, we examine the ontological, ethical, and political aspects of algorithmic medicine, interrogating how these technologies redefine our comprehension of disease, the posthuman body, and decentralized medical authority in an era of intelligent machines.

Source: International Journal of Science and Research Archive

DOI: 10.5281/zenodo.17605469

Authors: Mozumder, Abdul Quddus, Ghosh, Dipta, Raihan, Mohon, Islam, Araf, Paul, Rimon, Bishnu, Kanchon Kumar

Institutions: Stanford University, Middle Georgia State University, Westcliff University, Sonargaon University, California State University Los Angeles

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

OpenAlex ID: W7105743797

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