Article Text
PDF
Statistics from Altmetric.com
Introduction
At various times in history, technology has led to abrupt and far-reaching changes in how societies communicate: printing with moveable type in the 15th century; broadcasting in the 19th century and early 20th century; electronic transfer of information in the late 20th century; and machine learning with robotic systems in the 21st century. In that context, it is timely to reflect on the past, present and future trajectory of knowledge relating to medicine and what might be lost and gained as science and society increasingly enter the digital age. Here, the topic of interest is multiple sclerosis.
Multiple sclerosis in 2020: a synopsis
Broad consensus on the pathogenesis of multiple sclerosis was reached in the late 1980s, the application of which has since yielded significant advances in therapy. In 2020, a reasonable formulation would consider multiple sclerosis to involve a distinct geographical distribution resulting from the interplay of environmental and genetic aetiological factors; inflammatory and degenerative disease mechanisms working in sequence or in parallel expressed as an evolving phenotype characterised by intermittent and then progressive symptoms and signs leading to gradual accumulation of disability; clinical features and their pathological substrate represented by various surrogate laboratory biomarkers; the availability of therapies that modify the course of the illness but varying in their risks and benefits making for complex prescribing algorithms; and, underpinning the whole scientific endeavour, the wish to settle the hopes and fears for their future of affected individuals.
Digital technology and artificial intelligence
Digital technology uses binary rather than continuous analogue variables to detect patterns in large datasets, far outstripping the capacity of human agency for memory and analysis. Artificial intelligence develops systems that function according to preset rules but, primed by training datasets, generate their own information, learn from experience and adapt in order to achieve goals through deep learning. The boundaries of artificial intelligence are provisionally mapped in …