Artificial intelligence methods for a Bayesian epistemology‐powered evidence evaluation

Abstract

Rationale, aims and objectives: The diversity of types of evidence (eg, case reports, animal studies and observational studies) makes the assessment of a drug's safety profile into a formidable challenge. While frequentist uncertain inference struggles in aggregating these signals, the more flexible Bayesian approaches seem better suited for this quest. Artificial Intelligence (AI) offers great promise to these approaches for information retrieval, decision support, and learning probabilities from data.Methods: E-Synthesis is a Bayesian framework for drug safety assessments built on philosophical principles and considerations. It aims to aggregate all the available information, in order to provide a Bayesian probability of a drug causing an adverse reaction. AI systems are being developed for evidence aggregation in medicine, which increasingly are automated.Results: We find that AI can help E-Synthesis with information retrieval, usability (graphical decision-making aids), learning Bayes factors from historical data, assessing quality of information and determining conditional probabilities for the so-called 'indicators' of causation for E-Synthesis. Vice versa, E-Synthesis offers a solid methodological basis for (semi-)automated evidence aggregation with AI systems.Conclusions: Properly applied, AI can help the transition of philosophical principles and considerations concerning evidence aggregation for drug safety to a tool that can be used in practice.

E‐synthesis for carcinogenicity assessments: A case study of processed meat

“…We now show how E-Synthesis [20][21][22][23][24][25] can be employed as a tool for food carcinogenicity assessment, see also the Appendix (A Primer on Bayesian Reasoning).…”

Humans, machines and decisions: Clinical reasoning in the age of artificial intelligence, evidence‐based medicine and Covid‐19

“…The papers in the opening section [1][2][3][4][5][6][7][8][9] present a diverse and highly original series of discussions regarding both the possible uses and potential problems for AI in healthcare, considering some novel ways to overcome them. Authors examine the role of AI in diagnosing and treating numerous mental health disorders, in narrative therapy, [1][2][3] in maternity care and shared decision-making.…”

“…Authors examine the role of AI in diagnosing and treating numerous mental health disorders, in narrative therapy, [1][2][3] in maternity care and shared decision-making. 4 Discussions of machine learning, decision-support systems, interpretation, bias and the limitations of AI [5][6][7][8] are supplemented by consideration of the prospects for AI in facilitating the creation of a 'physicianless' experience for patients and a broad 'reconsideration of the role of humans in medical decision-making'. 9…”

EA3: A softmax algorithm for evidence appraisal aggregation

“…E-Synthesis is a Bayesian framework developed for determining probabilities of particular drugs causing a specific adverse reaction [78,[91][92][93][94][95]. In order to facilitate the inference from real world data to a causal hypothesis a layer of so-called "indicators" has been inserted between the hypothesis of interest and the data.…”