AI technologies are rapidly emerging and are starting to deliver promising results in different fields of aging and longevity research. One can expect that multi-disciplinary approaches combining the ability of modern AI to generalize, learn strategy, generate new models, objects and data from learned features with accurate methods for feature extraction and connection analysis will lead to new applications in every area of preventive, regenerative and restorative medicine.
AI progressively moves from the status of an overhyped technology with only a few proof-of-concept examples to a massively-adopted and accepted trend in healthcare. An example of this trend happened in early 2017 when the first DNN based platform, Arteries Cardio DL, was officially approved by the FDA. This platform is now widely used in clinics. Systems like Young.AI (http://young.ai) and aging.ai (http://aging.ai), which estimate the predicted biological age of a person using multiple data types, may provide valuable insights into the person’s health status and evolve into disease-specific applications. Multi-modal integration of the multiple aging clocks using modern AI will lead to a more holistic approach to the understanding of biology and provide a unified theory of aging and repair.
Here mobile apps can track the health system minutely using AI. It can be helpful and practically possible for a person to maintain the process.
Aging can be influenced by the complex interplay between environmental, mechanistic, biochemical and evolutionary constraints. Therefore, dysfunctions affecting only a few biological processes within the cells of one or several organs can propagate to all parts of the body. This explains why aging cannot be fully understood or controlled when monitoring only a restricted number of physiological processes.
For optimal use in aging research, AI should not only provide correct predictions, but also give information about the features used to obtain the predictions. The results provided should be interpretable in terms of initial inputs, which can be of highly diverse origins. There have already been several breakthroughs in making AI systems more interpretable, contributing to the development of new memory systems capable of capturing multi-modal continuous data and efficiently forgetting unnecessary information.
Improving the interpretability of AI-based algorithms can be done using two different complementary approaches. First, the complex data collated in many biological databases are well suited to DL but also contain challenging features, including high dimensionality, noise, and multiple, often incompatible, platforms. Consequently, while AI architectures are able to extract features from the data automatically and usually outperform other ML approaches in feature extraction tasks, it is recommended to select a set of relevant features before training DL models.
While advances in AI are already making substantial contributions to research in aging, the computational solutions specifically developed for aging research could substantially advance research in AI.
While there are multiple efforts to develop artificial general intelligence (AGI), also referred to as the sentient AI, and even transfer of the human memory and capabilities into computers, there is no proof of concept demonstrating the feasibility of any of these approaches. However, there is substantial debate on AI safety and ethics. Regardless of the winning approach to AGI and the probability of AGI emerging in the near future, it may be important to develop a values-based rules book to train AGI to maximize the number of quality-adjusted life years (QALY) for everyone in the population. Maximizing global longevity and human health span should be taught as the ultimate form of altruism to AGI.
Many age predictors are commonly referred to as “aging clocks” developed for multiple data types ranging from basic clinical blood tests, photos, videos, voice, retinal scans, and medical imaging to microbiome data. These feature selections, feature importance analysis, multimodal data analysis efforts and causal model development efforts not only help estimate the biological relevance of these data types but also help advance research in AI by making the DNNs more interpretable.