Recommendations for reporting findings from analyses using artificial intelligence and machine learning in the <i>Journal of Alzheimer's Disease</i>

Abstract

We propose five recommendations to make AI-based research studies more suitable for a clinical readership. First, authors should justify the added value of complex and potentially more opaque AI approaches. Second, rigorous description of input data, diagnostic criteria, and preprocessing is essential to avoid biased or clinically irrelevant outcomes. Third, benchmarking against clinically relevant performance thresholds should be established a priori. Fourth, method sections should combine an accessible lay summary with detailed technical supplement. Fifth, model explainability is encouraged to mitigate opacity. These recommendations aim to support AI research that is methodologically robust and interpretable for AD researchers.

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