Until recently, accurate early detection of clinical symptoms associated with Alzheimer's disease (AD) and related dementias (ADRD) has been difficult. Digital technologies have created new opportunities to capture cognitive and other AD/ADRD related behaviors with greater sensitivity and specificity. Speech captured through digital recordings has shown recent promise at feasible levels of scalability because of the widespread penetration of smartphones. One such study is described in detail to illustrate the depth in which artificial intelligence (AI) analytic approaches can be used to amplify the value of audio recordings. Another modality that has also attracted research interest are ocular scans that have near term potential for validation as a digital biomarker and a point of entry for clinical care workflows. Single modality measures, however, are rapidly giving way to multi-modality sensors that are embedded in all smartphones and other internet-of-things connected devices. Artificial intelligence (AI) driven analytic approaches are able to divine clinical signals from these high dimensional digital data streams. These data driven findings are setting the stage for a future state in which AD/ADRD detection will be possible at the earliest possible stage of the neurodegenerative process and enable interventions that would significantly attenuate or alter the trajectory, preventing disease from reaching the clinical diagnosis threshold.

Harmonizing diverse healthcare datasets is a challenging task due to inconsistent naming conventions. Manual harmonization is time- and resource-intensive, limiting scalability for multi-cohort Alzheimer's Disease research. Large Language Models, or specifically text-embedding models, offer a promising solution, but their rapid development necessitates continuous, domain-specific benchmarking, especially since general established benchmarks lack clinical data harmonization use cases.

The development of effective therapeutics for Alzheimer's Disease and related dementias (ADRD) has been hindered by patient heterogeneity and the limitations of current diagnostic tools. New treatments have no chance of working if given to patients who cannot benefit from them. This perspective explores how advances in Artificial Intelligence (AI), particularly multimodal machine learning, can solve the 'Goldilocks problem' of identifying patients for inclusion in clinical trials and support precision treatment in real-world healthcare settings. We examine the challenges of patient stratification, grounded by a conceptual framework of identifying each person's stage and subtype of dementia. We review data from several clinical trials of Alzheimer's disease therapeutics, to explore how AI-guided patient stratification can improve trial outcomes, reduce costs and improve recruitment. Further, we discuss the integration of AI into clinical workflows, the importance of model interpretability and generalizability, and ethical imperative to address algorithmic bias. By combining AI with scientific insight, clinical expertise, and patient experience, we argue that intelligent analytics can accelerate the discovery and delivery of new diagnostics and therapeutics, ultimately transforming dementia care and improving outcomes for patients around the globe.

Alzheimer's disease (AD) clinical trials continue to face major hurdles in patient identification, resulting in delayed timelines, underpowered studies, and escalating costs. This perspective explores these challenges through the lens of a memory clinic, where hundreds of cases often translate into only a handful of enrollments. We highlight the potential of artificial intelligence (AI) to address this gap by powering chatbots for awareness and pre-screening, decision support tools for case identification, and algorithms for matching patients to trial-specific criteria, automating and streamlining the recruitment process. We also examine critical considerations in developing such AI-driven tools, including data standardization, privacy protections, and ethical safeguards. With thoughtful implementation, these innovations could accelerate more inclusive and efficient AD trials, ultimately bringing therapies to patients faster.

Alzheimer's disease remains one of the most complex and contested domains in biomedicine, characterized by fragmented findings, competing hypotheses, and limited translational success. We propose that AI can offer not just technical acceleration but a deeper epistemic contribution: reconciliation. Rather than optimizing predictive performance or replicating existing assumptions, the goal is to align disparate data, methods, and mechanistic insights into coherent models that explain how the disease emerges, progresses, and can be treated. This approach centers on digital twins, not as monolithic models, but as flexible, testable architectures grounded in homeostasis, destabilization, and multiscale coherence. Through an iterative, interoperable AI architecture, digital twins integrate evidence, resolve contradictions, and highlight where critical gaps remain. This framework moves beyond incremental progress within the prevailing model to catalyzing a paradigm shift in how Alzheimer's is understood. Reconciliation, in this sense, is not a method but a guiding principle for transforming both the science and its applications.

Decades of advances unfolding in parallel across diverse domains have delivered to science rapid rises in the scale of multiplexing, population-level cohort sizes, global computational capacity, massive-scale artificial intelligence (AI) models, and advanced human cellular modeling capabilities. These have generated unprecedented volumes of data, allowing researchers to explore Alzheimer's disease (AD) biology at a depth and scale never before possible. The explosion of multi-omics datasets and computational power heralds an era in which the complexity of AD can be meaningfully dissected and reconstructed leveraging AI. These can be applied to advance our understanding of the root causes of disease, fundamentally a forward problem, tracing how dysfunction emergence from interactions across genes, cells and environments over time. On the other hand, therapeutic discovery requires addressing the inverse problem, working back from the diseased state to pinpoint upstream interventions that restore health. Human induced pluripotent stem cells (iPSCs) and other human cell models play a pivotal role in this process, naturally computing the mapping from perturbation to phenotype at scale. By recreating human-relevant biology, this cellular intelligence enables validation of targets predicted by AI and testing of interventions that drive therapeutic progress. We look to the next horizon in Alzheimer's research as a collaboration, a convergence of three forms of intelligence: human, artificial and cellular. In unison, these complementary forces will shape a new frontier for AD research where scientific innovation and human ingenuity work together bringing hope for meaningful advances and new therapies.

Artificial intelligence (AI), often seen as a harbinger of future innovation, also presents a dilemma: it can perpetuate existing human biases. However, this issue is not novel or unique to AI. Humans have long been the progenitors of biases, and AI, as a product of human creation, often mirrors these inherent tendencies. Here, we present a perspective on the development and use of AI, recognizing it as a tool influenced by human input and societal norms, rather than an autonomous entity. Modern efforts to technologically enabled data collection approaches and model development, particularly in the context of Alzheimer's disease and related dementias, can potentially reduce bias in AI. We also highlight the importance of data sharing from existing legacy cohorts to help accelerate ongoing AI model development efforts for greater scientific good and clinical care.

Despite major advances in Alzheimer's disease and related diseases (ADRD) research, the translation of discoveries into impactful clinical interventions remains slow. Overwhelming data complexity, fragmented knowledge, and prolonged research cycles hinder progress in understanding and treating neurodegenerative diseases. Artificial intelligence (AI) offers a promising path forward, particularly when developed as a scientist-in-the-loop system that collaborates with researchers throughout the scientific discovery process. This paper introduces the concept of an AI Biomedical Scientist, an intelligent platform designed to support literature synthesis, hypothesis generation, experimental design, and data interpretation. This platform aims to function as a holistic scientific partner, integrating diverse biomedical data and expert reasoning to accelerate discovery. We review commercial and academic efforts and introduce targeted Minimum Viable Products (MVPs) needed for general biomedical research lab utilization of AI, such as robust and accurate tools for literature and data analysis, negative data models, and virtual peer review, with a longer-term vision of foundation models trained directly on biomedical datasets. In AD and neurodegeneration research, such tools are anticipated to deliver efficiency gains ranging from modest improvements in specific research tasks to potential multi-fold accelerations in discovery workflows as systems mature and scale. This review examines the technical foundations, challenges, and anticipated impacts of AI and aims to inform and engage researchers in utilizing these systems to transform biomedical discovery, starting with AD and extending to other complex conditions.

Breakthroughs in biomarkers for amyloid (A), tau (T), and neurodegeneration (N) have advanced the prospects of accurate Alzheimer's disease (AD) diagnosis. However, presence of pathology does not always translate into clinical expression and there are still clear knowledge gaps as to whether someone with AD biological indicators will lead to clinically apparent disease necessary to warrant drug treatments that carry toxicity risk. Reliance on decades-old assessment tools inhibits detection and monitoring at preclinical and early disease stages when new treatments could prove most effective. Evidence has accumulated that digital measures provide accurate detection of disease at early stages. We call for a re-evaluation of the A/T/N diagnostic framework, with digital evaluation measures complementing non-AD specific neurodegeneration markers, and even potentially replacing those non-specific to AD, to provide a clinically relevant feature critical to clinical trial advances and treatment decisions. Achieving this will only be possible if further research into novel digital evaluation tools is pursued with the same support and consideration as amyloid and tau.

There is considerable opportunity to fast-track novel treatments for Alzheimer's Disease (AD) through repurposing of existing licensed medications as a way of complementing ongoing drug discovery efforts. Given the complex interplay between AD neuropathological mechanisms, there is also a strong rationale that treatment benefits may be enhanced by examining combinations of treatments to identify potential synergies that would address multiple disease-modifying mechanisms. A Delphi consensus programme combined with a pragmatic analysis of primary care data has identified a series of individual and combined therapies that warrant further investigation in pre-clinical and clinical trials. These include treatments which target well-established neurodegeneration pathways and more explorative agents, including hormonal and anti-infective agents, which align to emerging hypotheses relating to endocrine and immune pathways in AD. Whilst caution is critical when considering combined therapy due to the risks of interaction and polypharmacy, this study provides valuable indications of potential synergistic drug pairs that warrant further investigation.

Donanemab significantly slowed clinical progression in participants with early symptomatic Alzheimer's disease (AD) during the 76-week placebo-controlled period of TRAILBLAZER-ALZ 2.

Despite substantial investment in biomedical and pharmaceutical research over the past two decades, the global prevalence of Alzheimer's disease (AD) and AD-related dementias (AD/ADRD) is still rising. This underscores the significant unmet need for identifying effective disease-modifying therapies. Here, we provide a critical perspective on the application of data science and artificial intelligence (AI) to the rational design of drug combinations in AD and ADRD, addressing their potential to transform therapeutic development. We examine AI's current and prospective capabilities in therapeutic discovery, identify areas where AI-driven strategies can enhance drug combination development, and outline how multidisciplinary professionals in the field, including clinical trialists, neuropsychiatrists, pharmacologists, medicinal chemists, and computational scientists, can leverage these tools to address therapeutic gaps. We also highlight AI's role in synthesizing the rapidly growing amount of biomedical data in the field of AD/ADRD, especially clinical trials, biomarkers, multi-omics data (genomics, transcriptomics, proteomics, metabolomics, interactomics, and radiomics), and real-world patient data. We further explore AI's utility in prioritizing potential drug combination regimens and estimating clinical effect size in combination therapy trials for AD/ADRD. Lastly, we emphasize AI-powered network medicine methodologies for prioritizing drug combinations targeting AD/ADRD co-pathologies and summarize the challenges of their translation to clinical practice.

Statistical methods in clinical research tend to become entrenched. Innovations threaten the status quo. The "right way" becomes frozen in lore. This is so even when the "right way" is not best. "Statistical significance" and the associated requirement of "high power" is an example. This attitude is an impediment to efficient design. Willingness to address some design issues with moderate power enables building highly informative and highly efficient clinical trials. This article considers several types of clinical trials, including dose-finding, combinations, and factorial designs. Bayesian adaptive methods are used to show that trials can be made more efficient and more informative. Surprisingly, the approach is consistent with many attitudes of the widely regarded "Father of Modern Statistics," R.A. Fisher. Fisher was anti-Bayesian in rejecting its subjective interpretations. But Fisher and Bayes come to the same conclusion in many applied matters. Fisher invented factorial design. Its principal attraction for him was enabling addressing two or more questions with a single experiment. He complained about attitudes that hindered progress: "No aphorism is more frequently repeated in connection with field trials [and clinical trials], than that we must ask Nature few questions, or, ideally, one question at a time… this view is wholly mistaken." Fisher's primary analysis required modeling and making assumptions. For example, his first analysis in a factorial setting assumed no interactions among the factors. He investigated possibilities of interactions but he did not see the need for doing so with high power.

A new era of Alzheimer's disease (AD) research is beginning with multiple approved anti-amyloid monoclonal antibodies (mABs). These drugs are currently not widely used, but may be soon, especially at clinical trial sites. Putative disease-modifying therapies (DMTs) may alter the progression rate, potentially reducing our ability to detect effects on top of mABs. Co-administration of amyloid-targeted agents may diminish benefit (antagonism, due to the overlapping mechanism of action); alternatively, complementary treatment mechanisms may increase benefit (synergy).

To effectively combat dementia onset and progression, lifestyle-based interventions targeting multiple risk factors and disease mechanisms through a multidomain approach - tailored and implemented early in the disease process - have emerged as promising. Electronic databases and relevant websites (clinicaltrials.gov, euclinicaltrials.eu, PubMed and EMBASE) were systematically searched for randomized controlled trials (RCTs) testing the combination of multidomain lifestyle and pharmacological interventions. Studies were included if 1) lifestyle intervention was multimodal (≥2 domains); 2) it was combined with drugs, supplements, or medical food; 3) the study population was within the Alzheimer's disease (AD) and related dementias continuum, including cognitively normal individuals at-risk for dementia, people with subjective cognitive decline (SCD), mild cognitive impairment (MCI), or prodromal AD; 4) outcomes included cognitive or dementia-related measure(s), and 5) intervention lasted at least 6 months. Twelve combination RCTs were identified, incorporating 2 to 7 lifestyle domains (physical exercise, cognitive training, dietary guidance, social activities, sleep hygiene, cardiovascular/metabolic risk management, psychoeducation or stress management), combined with pharmacological components (e.g., Omega-3, Tramiprosate, vitamin D, BBH-1001, epigallocatechin gallate, Souvenaid, and metformin). Seven RCTs targeted participants with prodromal AD, MCI or early dementia, five focused on at risk individuals or SCD. Additionally, 2 studies adopted a precision medicine approach by enriching populations with APOE-ε4 carriers. Findings suggest that well-designed interventions - tailored to the right individuals, implemented at the optimal time - may effectively improve cognition. However, further refinement of the RCT methodology is warranted, for better alignment with the multifaceted nature of dementia prevention and management.

Neighborhood-level factors, measured by the Area Deprivation Index (ADI), are linked to comorbidities of Alzheimer's disease and related dementias (ADRD). However, their direct association with AD neuropathology is unclear. The accessibility of blood-based biomarkers (BBMs) like p-tau217 and Aβ42/40 offers a scalable way to investigate these relationships.

The rapid increase in the incidence of Alzheimer's disease (AD) has raised concerns, given its profound effects on both society and the economy. Despite extensive research efforts in this area, there are no existing treatments that have the ability to change the progression of the disease.