With the increase in large multimodal cohorts and high-throughput technologies, the potential for discovering novel biomarkers is no longer limited by data set size. Artificial intelligence (AI) and machine learning approaches have been developed to detect novel biomarkers and interactions in complex data sets. We discuss exemplar uses and evaluate current applications and limitations of AI to discover novel biomarkers. Remaining challenges include a lack of diversity in the data sets available, the sheer complexity of investigating interactions, the invasiveness and cost of some biomarkers, and poor reporting in some studies. Overcoming these challenges will involve collecting data from underrepresented populations, developing more powerful AI approaches, validating the use of noninvasive biomarkers, and adhering to reporting guidelines. By harnessing rich multimodal data through AI approaches and international collaborative innovation, we are well positioned to identify clinically useful biomarkers that are accurate, generalizable, unbiased, and acceptable in clinical practice.

Artificial intelligence and machine learning approaches may accelerate dementia biomarker discovery.
Remaining challenges include data set suitability due to size and bias in cohort selection.
Multimodal data, diverse data sets, improved machine learning approaches, real-world validation, and interdisciplinary collaboration are required.

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This post is Copyright: Laura M. Winchester,
Eric L. Harshfield,
Liu Shi,
AmanPreet Badhwar,
Ahmad Al Khleifat,
Natasha Clarke,
Amir Dehsarvi,
Imre Lengyel,
Ilianna Lourida,
Christopher R. Madan,
Sarah J. Marzi,
Petroula Proitsi,
Anto P. Rajkumar,
Timothy Rittman,
Edina Silajdžić,
Stefano Tamburin,
Janice M. Ranson,
David J. Llewellyn | September 1, 2023

Wiley: Alzheimer’s & Dementia: Table of Contents