Early identification of degenerative processes in the human brain is considered essential for providing proper care and treatment. This may involve detecting structural and functional cerebral changes such as changes in the degree of asymmetry between the left and right hemispheres. Changes can be detected by computational algorithms and used for the early diagnosis of dementia and its stages (amnestic early mild cognitive impairment (EMCI), Alzheimer’s Disease (AD)), and can help to monitor the progress of the disease. In this vein, the paper proposes a data processing pipeline that can be implemented on commodity hardware. It uses features of brain asymmetries, extracted from MRI of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, for the analysis of structural changes, and machine learning classification of the pathology. The experiments provide promising results, distinguishing between subjects with normal cognition (NC) and patients with early or progressive dementia. Supervised machine learning algorithms and convolutional neural networks tested are reaching an accuracy of 92.5% and 75.0% for NC vs. EMCI, and 93.0% and 90.5% for NC vs. AD, respectively. The proposed pipeline offers a promising low-cost alternative for the classification of dementia and can be potentially useful to other brain degenerative disorders that are accompanied by changes in the brain asymmetries.

The growth of computer power is crucial for the development of contemporary information technologies. Artificial intelligence is a powerful instrument for every aspect of contemporary science, the economy, and society as a whole. Further growth in computing potential opens new prospects for biomedicine and healthcare. The promising works on quantum computing make it possible to increase computing power exponentially. While conventional computing relies on the formula with 2n bits, the simplified vision of quantum computer power is 2N, where N is a number of logical qubits. With thousandfold or more improvements in computing performance, there will be realistic options for quick protein, genes and other organic molecules 3D fold discoveries, empowering pharmaceutics and biomedical research. Personalized blockchain-based healthcare will become a reality. Medical imaging and instant healthcare data analysis will significantly speed up diagnostics and treatment control. Biomedical digital twin usage will give useful tools to any healthcare practitioner, with options for intraoperative AR and VR micro-manipulations. Nanoscale intrabody bots will be instantly customized and AI-controlled. The smart environment will be enriched with multiple sensors and actuators, giving real control of the air, water, food, and physical health factors. All these possibilities are quickly achievable only in the case of realistic quantum computing options. Even with the ability to reach this stage, there will be questions for the stability of post-quantum society: privacy, ethical issues, and quantum computing control uncertainty. General solutions to these queries will give clues for post-quantum healthcare.

Advantages in CMOS MOSFET-based electronics served as a basis for modern ubiquitous computerization. At the same time, theoretical and practical developments in material science, analytical chemistry and molecular biology have presented the possibility of applying Boolean logic and information theory findings on a molecular basis. Molecular computing, both organic and inorganic, has the advantages of high computational density, scalability, energy efficiency and parallel computing. Carbon-based and carbohydrate molecular machines are potentially biocompatible and well-suited for biomedical tasks. Molecular computing-enabled sensors, medication-delivery molecular machines, and diagnostic and therapeutic nanobots are at the cutting edge of medical research. Highly focused diagnostics, precision medicine, and personalized treatment can be achieved with molecular computing tools and machinery. At the same time, traditional electronics and AI advancements create a highly effective computerized environment for analyzing big data, assist in diagnostics with sophisticated pattern recognition and step in as a medical routine aid. The combination of the advantages of MOSFET-based electronics and molecular computing creates an opportunity for next-generation healthcare.