The landscape of artificial intelligence in neurodegenerative diseases: a systematic review

The rising global burden of neurodegenerative diseases underscores an urgent need for advanced research in diagnosis, prognosis, and treatment. Artificial Intelligence (AI) methods, particularly when applied to multimodal data, offer a powerful tool to address these challenges. However, a comprehensive overview and critique of the current landscape of AI methods is lacking. 4,685 records of peer-reviewed, primary research articles were screened and 1,956 articles reviewed in full text, yielding 1,186 included studies. For each included study, clinical objectives, disease focus, data modalities, modelling approach, evaluation strategy, and reporting practices were extracted. Fewer than 5% of studies integrated pharmacological treatments into their predictive models, limiting the extent to which models can directly inform clinical decision-making. Neuroimaging was the predominant input modality, while integration of other clinically relevant data types was relatively rare. Reproducibility rates remain critically low at 35%, and external validation practices fail to use geographically and demographically diverse datasets. Overall, AI research in neurodegenerative diseases suffers from significant limitations in reproducibility, data inclusivity, and clinical translatability. We provide a set of recommendations that can be adopted to address these issues and improve reliability and downstream clinical utility. Neurodegenerative diseases such as Alzheimer’s and Parkinson’s are increasing as populations age. Researchers are using artificial intelligence (AI) to support early diagnosis, predict progression, and improve care by learning from brain scans, medical records, and lab tests. We reviewed a large number of published studies to understand how close research is to real clinical use. Three gaps stand out: few models include information on medicines or treatments, limiting treatment-aware decisions; most rely mainly on brain imaging while other routine clinical data are used less often; and many studies lack sufficient data/code and independent testing on diverse populations, making results hard to reproduce and generalise. We offer practical recommendations to make future AI tools more reliable, inclusive, and clinically useful. Endrizzi et al. review over 1,100 studies using AI for neurodegenerative diseases. They identify significant limitations in reproducibility, data inclusivity, and clinical translatability, providing a set of recommendations to improve reliability and clinical utility.