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Artificial intelligence (AI) is revolutionizing the problem solving of medical diagnosis, which has enduring issues, including early-stage disease, insufficient voluminous data, and diagnosis process ineffectiveness. This review demonstrates considerable progress in developing ML technologies, including monkeypox detection, Tuberculosis, and cancer diagnosis. CNNs have shown high efficiency in diagnostics; even InceptionV3, a transfer-learning model for clinicians, can reach 99.87% diagnostics. As privacy-preserving solutions, federated learning models work to improve diagnostic accuracy without increasing the exposure of individual data, and synthetic datasets derived from high-resolution techniques such as HiP-CT help deal with data scarcity by improving model construction and assessment. The hybrids of genome and metabolome integration helped enhance diagnostic accuracy measures, particularly for complex diseases like COVID-19, due to increased prognostic performance metrics using multiple biological information. However, few issues crop up even in modern society: Generalization of the model is an issue due to a lack of data, especially for rare conditions, and increased computational power requirements for most ML models pose a problem for implementation in low-resource environments. Prominent ethical issues incorporating algorithm prejudices and the ‘black box’ concept spotlight the requisite of an explainable AI (XAI) framework to provide visibility and credence in the medical facility. Possible directions in development, such as the standardization of frameworks, enhancing computational support, and integration of different fields, provide ways to address these challenges. When tackled, these challenges create the possibility of revamping global healthcare through suitable and scalable approaches informed by ML technologies that align with the patient’s needs, leading to better practices and, consequently, better health.
Read MoreDoi: https://doi.org/10.54216/MOR.010101
Vol. 1 Issue. 1 PP. 01-16, (2024)
Following this background, this review discusses the advanced technologies such as AI, micro-fluids, and automated platforms that this differentiation protocol could help achieve in regenerative medicine. Stem cell research, essential in tissue engineering, disease modeling, and drug development, is challenging through heterogeneity, scalability, and reproducibility, as observed in differentiation procedures. Machine learning and deep learning patterns have become more effective in predicting cellular behavior, tracking cellular stations, and optimizing differentiation methods for current stem cell technology. These methods also reduce observer bias, increase the throughput of high-throughput screening, and advance modeling to precise therapeutic applications. At the same time, microfluidic and automated systems provide nearly perfect control over differentiation stimuli, recreating the in vivo conditions with the ability to control spatial and temporal gradients. This integration between AI and microengineering has promoted 3D culture systems, lab-on-a-chip technologies, and biosensors, enabling reproducible and efficient differentiation results. There is still much to accomplish, such as the problem of obtaining uniform stem cell populations or decoding the tissue context. This field incorporates several interdisciplinary advancements such as stimuli-responsive systems and computational modeling; it envisages new horizons in regenerative medicine, transforming stem cell-based therapeutic technologies to their optimum level for personalized medicine and other advanced tissue engineering applications.
Read MoreDoi: https://doi.org/10.54216/MOR.010102
Vol. 1 Issue. 1 PP. 17-34, (2024)
Tuberculosis (TB) is ranked as one of the leading causes of death from infectious diseases in the present world, causing important health and economic consequences in the different developing countries. The practices of traditional diagnostic approaches, although still expected, are associated with relativity, slowness, and organs, besides being confined to visual observations and touch. The new and increased capacity in advanced machine learning is a promising area that has shown potential in improving the diagnosis of TB, as well as identifying drug resistance and disease management. This review presents various aspects of using ML in diagnosing and managing TB disease based on its various categories of models, including deep learning, hybrid approach, and the metabolomics approach. Some of these methods have been very effective, with high diagnostic performance improvements in sensitivity, specificity and accuracy; Furthermore, ML has been used to analyze the molecular picture of TB and to find drug targets of the disease toward future targeted therapies. As seen with the integration of ML, substantial benefits are provided by the solutions proposed. However, questions concerning the quality of data, interpretations of ML models and ethical problems hinder further application. This review concludes with the idea that ML can transform the diagnosis and management of TB and calls for more investment in developing this field to overcome these barriers to global health.
Read MoreDoi: https://doi.org/10.54216/MOR.010103
Vol. 1 Issue. 1 PP. 35-44, (2024)
Chronic liver disease (CLD) is a group of conditions for which up to half of the global population remains at risk and causes serious complications: liver cirrhosis and liver cancer. Therefore, early diagnosis and proper treatment of these diseases enhance the prognosis of patients suffering from CLD. This review paper explores how machine learning (ML) techniques are used in practice to diagnose, prognosis, and treat chronic liver diseases. Continuing with more specific examples of collected data from the results of several studies, their more comprehensive implementation is expected to improve the respective management processes and the detection of liver disease in patients more accurately. The review further discusses the various ML methods, including supervised and unsupervised learning, neural network, and ensemble learning, also applied to the estimation of risk felt by the patients, suggesting a course of treatment or how far the disease has progressed. While the inclusion of ML technology in the field of Hepatology is progressing well, some issues like model diversity, applicability of models, and concerns about ethics still pose challenges. This paper points out the importance of working in teams from various fields to develop appropriate mechanisms for dealing with these issues and adequately use ML for clinics. In conclusion, the results indicate that there is a possibility that ML will change the management of chronic liver diseases, which in turn will lead to the development of innovative treatment methods and better patient management.
Read MoreDoi: https://doi.org/10.54216/MOR.010104
Vol. 1 Issue. 1 PP. 45-54, (2024)
The current review summarizes the latest trends in malaria literature, emphasizing transmission ecology, new diagnostics and treatment. It stresses the additional focus on the transmission, according to the spatiotemporal models and predictive analytics, which help identify periods and the locations with the most significant risk, noting that these processes should consider the environmental factors. The change in the diagnostic approach, especially the introduction of artificial intelligence techniques such as deep learning, has improved the rate and precision at which malaria parasites are diagnosed in resource-limited countries where time is of the essence. Furthermore, there have been significant advances in drug discovery due to machine learning applications that have made it quicker to find new antimalarial drugs in the face of drug resistance. Despite these developments, there are still problems such as drug resistance, socio-economic disparities, and the environment that are being altered and still require an integrated and transdisciplinary approach. Combining these determinants is indispensable for eliminating these challenges and further promoting global efforts to control malaria.
Read MoreDoi: https://doi.org/10.54216/MOR.010105
Vol. 1 Issue. 1 PP. 55-65, (2024)