Titlebook: Biosystems, Biomedical & Drug Delivery Systems; Characterization, Re Shrikaant Kulkarni,A. K. Haghi,Sonali Manwatkar Book 2024 The Editor(s

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Artificial Intelligence and Machine Learning in Restoring and Strengthening HealthCarease outbreak prediction, and even recommend a variety of predictive treatment options such as robotic surgeries and behavior adjustments. In this chapter Ethical concerns surrounding the application of AI to healthcare are also discussed too.

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In Silico Toxicological Protocols Optimization for the Prediction of Toxicity of Drugs development. Numerous approaches are available for developing models that can predict toxicity outcomes. This chapter offers a complete summary, explanation, and comparison of the advantages and disadvantages of the current modeling techniques and algorithms for predicting toxicity. The focus is on

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Optimizing Healthcare Throughput: The Role of Machine Learning and Data Analyticstient. It powers predictive modeling in healthcare, which predicts patient outcomes, hospital readmissions, and resource allocations. This lets the healthcare professionals make choices based on data that improve patient care and streamline processes. Natural Language Processing pools important info

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Applications of AI-Based Models in the Field of Biomedicinetion, AI can predict potential drug candidates and their efficacy. This acceleration not only hastens the development of novel therapeutics but also mitigates costs. Patient care has undergone significant improvement through AI. Chatbots and virtual health assistants now offer round-the-clock suppor

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Application of New Biological Entities (NBEs) as Therapeuticssly intractable conditions. The advent of biotechnology and advanced genomics has accelerated the discovery and development of NBEs, enabling precision medicine like never before. The chapter explores into the transformative potential of NBEs in various therapeutic areas, from cancer and autoimmune

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Applications of Computational Tools in the Prediction of Toxicityology, computational tools come into play to assess the potential toxicity of nanomaterials exhibiting unique biological interactions. Computational tools are often integrated with experimental data to provide a more comprehensive understanding of toxicity. This helps the research and regulation in

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Application of Peptides for the Treatment of Diabetes: A Plant-Based Bioactive Materialion, isolation, characterization, and commercial exploitation of plant proteins due to the presence of numerous secondary metabolites that impede their functionality. The realm of peptide drugs targeting diabetes is experiencing rapid expansion, and the exploration of plant-derived bioactive peptide