Preventive medicine is largely about identification of causes of diseases and
their removal. Cervical cancer in Finland is firstly a showcase and secondly a use-case
of preventive medicine. Firstly, etiological studies on cervical cancer were for long
confounded by the fact that sexually transmitted infections are surrogates of both risktaking behaviour in adolescent and young adult population, and occurrence of cervical
cancer in middle-aged women. Identifying oncogenic human papillomaviruses (HPVs)
as the true cause of cervical cancer among the multitude of different sexually
transmitted micro-organisms required a Nobel-prize winning vision which was initially
supported only by case-series evidence. It also required a paradigm shift that was
facilitated by a correctly done epidemiological study and increased understanding on
the molecular basis of exposure misclassification. All this was understood only after
the etiological enigma had been resolved. Secondly, since the sexual revolution in
1960’s first facilitated increase in risk-taking sexual behaviour associated sexually
transmitted infections’ incidence, and subsequently resulted in an increase in the
incidence of cervical cancer. In the below Finnish use-case, the role of different causal
(HPV16/18/31/45), intervening (Chlamydia trachomatis, smoking, HLA, HPV6/11)
and non-causal (herpes simplex virus type 2) factors are put into perspective based on
longitudinal, population-based studies. The established evidence base is now available
for the evaluation of artificial intelligence/ machine learning performance in disclosing
and judging causes of a chronic disease, cervical cancer.
Keywords: Artificial intelligence, Causality, Cervical cancer, Chlamydia, Evidence hierarchy, Herpes simple virus, HLA, Human papillomavirus, Nested case-control study, Smoking.