Letter to the Editor: “AI and ML in Alzheimer's disease: Transforming early detection and drug development”

Dear Editor,

I am writing in response to the research article titled “AI in Alzheimer's Disease (AD) Detection.” India is experiencing a significant rise in the prevalence of AD and related dementias (ADRDs) as a result of population expansion and the aging of its population. By 2050, projections indicate that low- and middle-income nations, including India, will account for more than 75% of cases of ADRDs. Projections indicate that this concerning pattern will affect 11.44 million individuals in India, a significant increase from 3.84 million in 2019. The predicted rise in AD incidence in India highlights the pressing need for efficient prevention efforts, early diagnostic approaches, and enhanced care for individuals living with AD. AD researchers are increasingly interested in blood-based biomarkers (BBMs) because of their ability to detect and monitor the disease at an early stage. These biomarkers provide a less intrusive and perhaps more affordable option compared to cerebrospinal fluid (CSF) or neuroimaging indicators, making them more practical in primary care settings. BBMs can forecast the future progression of AD in individuals without dementia but with cognitive problems. The fluid biomarkers that have been extensively researched for AD are Aβ 1–42, phosphorylated tau, and total tau levels. BBMs have shown promising results in predicting the buildup of cortical Aβ and tau, as well as signs of astrogliosis and neuronal damage. However, additional research is required to authenticate these markers and understand their role in AD pathophysiology (Varesi et al., 2022). Due to the variability in blood biomarker expression during AD, these biomarkers are not sufficiently reliable for precise identification of the condition. AI/Machine learning (ML) models have gained popularity as a viable choice due to their ability to provide accurate and timely predictions without being obtrusive, while also being cost-effective. The lack of consistency in the protocols for gathering, handling, preserving, examining, and documenting blood samples can affect the capacity to reproduce these evaluations of biomarkers. Therefore, we are utilizing AI and ML algorithms to forecast diseases (Perneczky et al., 2024). ML is revolutionizing the drug development process by generating predictive models based on user input. This procedure, which depends on a limited amount of biologically annotated data, is expensive and time-consuming. Meta-learning accelerates this process by allowing users to interactively request new data points, enabling the creation of ML predictors of superior quality (Aditya Shastry and Sanjay, 2023). AI and ML are transforming the field of drug development by employing a theoretical framework of graphical models to depict biological and chemical components. These techniques are essential for speeding up the process of repurposing drugs and developing therapies for AD. Although there are difficulties, AI and ML present new opportunities for the early detection and treatment of disorders such as AD. Researchers can identify initial indications of AD before the onset of clinical symptoms through effective analysis of intricate datasets. Nevertheless, it is imperative to thoroughly contemplate data protection, uphold ethical standards, and create reliable algorithms that physicians can rely on to effectively incorporate them into clinical practice (Viswan et al., 2024). AI has shown considerable promise in the identification and surveillance of AD. AI algorithms can examine extensive patient data, such as medical records, imaging scans, and genetic information, to detect patterns and biomarkers. Deep learning (DL) AI models can forecast AD up to seven years in advance with an accuracy rate of 72%. Nevertheless, there are still obstacles to overcome in terms of the explainability and interpretability of AI models, which are essential for ensuring their broader acceptance and use in the medical field (Cheng et al., 2024). Significant improvements in AI identification of AD are essential for prompt intervention and potentially mitigating its advancement. DL algorithms, specifically those used in DL, have demonstrated exceptional precision in forecasting and detecting diseases based on various types of data. These solutions are non-invasive, affordable, and easily adaptable, making them appropriate for wider implementation. However, there are still obstacles to overcome in terms of interpretability and transparency, which are essential for ensuring the broader acceptance and use of these technologies in the medical field. Scientists anticipate that AI will play a progressively significant role in combating AD as scientific investigation advances (Fig. 1).

Fig. 1.

AI and ML in early detection and treatment of Alzheimer's disease.

Ultimately, the anticipated increase in AD cases in India calls for the development of efficient diagnostic instruments. AI and ML demonstrate potential in the early detection of AD using non-invasive examination of blood biomarkers and in speeding up the development of drugs. However, strong data control, ethical utilization, and transparent modeling are essential for the integration of healthcare practices. Effective collaboration among researchers, doctors, and AI developers is crucial for the responsible deployment of AI in the treatment of AD.

Ethics approval

Not applicable.

Funding

This study received no funding.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Handling Editor: Dr W Peul

Abbreviation

AD

Alzheimer's Disease

ADRDs

lzheimer's Disease and related dementias

ML

Machine learning

DL

Deep learning