ABSTRACT
This chapter critically examines the data-centric foundations of contemporary personalised medicine, tracing their roots to long-standing medical practices of measurement, classification, and statistical correlation. It explores how emerging technologies – from genomic sequencing and multi-omics to wearable sensors and electronic health records – enable unprecedented volumes and varieties of patient data, fuelling visions of precision and predictive healthcare. The discussion interrogates the cultural authority of quantification, unpacking how claims of objectivity mask value-laden choices in what is measured, how it is categorised, and how it is interpreted. Through historical and contemporary case studies, this chapter shows how diagnostic categories are created, reshaped, and imbued with social consequences, including inequities in access, representation, and benefit. It addresses the promises and pitfalls of big data and artificial intelligence, emphasising their potential to entrench bias and produce reductive ‘maps’ of health that obscure complexity. By revealing the socio-technical contingencies behind seemingly neutral data, this chapter calls for epistemic humility, equity-oriented data practices, and caution against mistaking data representations for the realities they claim to capture.
