There are three pillars of evidence-based medicine (EBM): the evidence itself (e.g., data from clinical studies), clinical expertise, and patient values. EBM is therefore a systematic approach to decision-making that integrates these three inputs. It involves evidence production (design and conducting of clinical studies), synthesis (collecting, appraising, and combining data to answer clinical questions), implementation (e.g., through clinical practice guidelines based on these syntheses), and evaluation (monitoring the quality of care, including adherence to evidence-based recommendations).
EBM faces many challenges that artificial intelligence can help solve. It can help detect research gaps and avoid funding redundant studies. It can expedite the evidence synthesis process, which currently is slow and costly, leading to outdated and incomplete evidence being used in the decision-making processes. AI can help engage patients and elicit values (e.g., chatbot-based decision aids) as well as provide coordinated care for patients with multimorbidities.
However, improperly implementing AI can also exacerbate problems in EBM. For instance, if AI-enabled decision support systems fail to incorporate patient values, a return to a model of medicine characterized by low patient autonomy is possible – only this time with a computer in charge.
- Evidence-based medicine
- Natural language processing
- Systematic reviews
- Data extraction
- Living systematic reviews
- Shared decision-making
- Clinical practice guidelines
- Rapid-learning health systems
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Nowak, A.J. (2021). Artificial Intelligence in Evidence-Based Medicine. In: Lidströmer, N., Ashrafian, H. (eds) Artificial Intelligence in Medicine. Springer, Cham. https://doi.org/10.1007/978-3-030-58080-3_43-1
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