Personalised Medicine: From Genomics to 3D-Printed Pharmaceuticals | Chapter 1 | Medical Science: Updates and Prospects Vol. 1

 

Personalised medicine (PM) is a patient-specific approach to treatment that integrates genetic, epigenomic, and clinical data. PM has the potential to transform traditional medical practice by tailoring therapies to individual genetic profiles. The significant advantages and limitations must be carefully considered. Manufacturers are using drug repurposing, biomarker-driven R&D, and collaborations with diagnostics and IT sectors. Personalised medicine not only enhances therapeutic precision but also advances preventive care through polygenic risk scores and early biomarker detection. The integration of digital health tools, including wearables and telemedicine, further supports patient-specific monitoring. Real-world examples, such as FDA-approved targeted therapies and CAR-T cells, illustrate its transformative potential. Innovations such as CRISPR-based interventions, AI-driven decision support, and personalised vaccines are highlighted. Liquid biopsy, single-cell omics, artificial intelligence, and healthcare digitalisation, further supporting its implementation, are cutting-edge tools. The Quality by Design (QbD) principles for safe, consistent, and effective production of personalised 3D-printed tablets have been explained. Critical material attributes (CMAs), critical process parameters (CPPs), and critical quality attributes (CQAs) together enable regulatory-compliant manufacturing by ensuring drug dosage accuracy, content uniformity, dissolution control, and robust production conditions. Beyond treatment, it raises ethical considerations related to data privacy and equitable access. Although cost-intensive, it reduces long-term healthcare burdens by minimising adverse reactions.

 

 

Author(s) Details

B. Navya Sree
Arya College of Pharmacy, India.

 

Saif Bin Salim
Arya College of Pharmacy, India.

 

Mohd Abdul Kareem
Arya College of Pharmacy, India.

 

M. Srikanth
Arya College of Pharmacy, India.

 

 

AVS Rajeswari
Department of Pharmaceutics, Arya College of Pharmacy, India.

 

Please see the book here :- https://doi.org/10.9734/bpi/msup/v1/6352

 

AI and Big Data: Pioneering the Next Generation of Health Care Solutions in Cancer Treatment | Chapter 9 | Medical Science: Recent Advances and Applications Vol. 6

 

One of the biggest causes of illness and death on the globe, cancer continues to be a problem for healthcare systems. From early detection and diagnosis to treatment planning and long-term patient monitoring, recent developments in artificial intelligence (AI) and big data technologies have brought forth revolutionary possibilities in the field of cancer care. This chapter explores the pivotal role of AI and Big Data in revolutionising oncology by providing innovative, data-driven health solutions aimed at improving patient outcomes and optimising healthcare delivery.

 

This chapter's main goal is to give an overview of how AI and Big Data are changing cancer research, diagnosis, treatment, and patient care. It starts by providing basic information about cancer, such as its definitions, the main types (solid tumours, hematologic malignancies, and rare cancers), and statistics on incidence, survival rates, and healthcare burden as of right now. Building upon this foundation, the chapter delves into the critical sources of Big Data in oncology—such as Electronic Health Records (EHRs), genomic databases, clinical trials, and patient-reported outcomes, and addresses the challenges of integrating and managing these vast data sets. The chapter further investigates the application of AI in cancer diagnosis through advanced imaging analysis, machine learning models for early detection, and risk prediction tools. It highlights how AI facilitates personalised treatment planning by enabling genomic profiling, biomarker discovery, and the development of clinical decision support systems. Patient monitoring is also examined, showcasing the role of remote technologies, wearable devices, and telemedicine in enhancing quality of life and symptom management for cancer patients. Ethical considerations, including data privacy, algorithmic bias, and the regulatory landscape, are critically analysed to ensure responsible AI implementation. The chapter concludes with a discussion on future innovations—such as Natural Language Processing (NLP), next-generation machine learning techniques, and AI’s potential role in drug development—and presents real-world case studies demonstrating successful integration of AI and Big Data in cancer care.

 

The main aim of this chapter is to emphasise the transformative potential of AI and Big Data in oncology while advocating for continuous research, interdisciplinary collaboration, and a strong patient-centric approach. It calls on healthcare professionals, technologists, researchers, and policymakers to work together in harnessing these technologies for more equitable, efficient, and effective cancer care in the future.

 

Author (s) Details

J. Sukanya
Department of Computer Science, M.V. Muthiah Government Arts College for Women, Dindigul, India.

 

A. Subramani
Department of Computer Science, Government Arts and Science College, Natham, Dindigul, India.

 

S. Vijayakumar
Department of AI/ML, Madurai Kamaraj University, Madurai, India.

 

S. Krishnaveni
Department of Computer Science, M.V. Muthiah Government Arts College for Women, Dindigul, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/msraa/v6/5674

Artificial Intelligence in Predictive Analytics of Patient Outcomes and Disease Management | Chapter 6 | An Overview of Disease and Health Research Vol. 2

Artificial Intelligence (AI) has revolutionised predictive analytics in healthcare, offering innovative approaches for patient outcome prediction and disease management. This review explores the role of AI-driven predictive models in early disease detection, prognosis estimation, and personalised treatment strategies. This review also discusses recent advancements, limitations, and future prospects of AI-powered predictive analytics in healthcare, emphasising its transformative potential in improving patient care and disease prevention. Machine learning (ML) algorithms, deep learning (DL) networks, and natural language processing (NLP) have significantly enhanced predictive capabilities by analysing vast datasets, including electronic health records (EHRs), genetic information, and real-time patient monitoring data. AI applications in disease management facilitate early intervention, optimise resource allocation, and improve clinical decision-making. However, challenges such as data privacy, model interpretability, and ethical considerations remain key concerns. Ensuring transparency and explainability in AI models is crucial to gaining clinician and patient trust while mitigating risks associated with biased or erroneous predictions.

 

Author (s) Details

Zaki Siddiqui
Department of Medicine, MLB Medical College, Jhansi, Uttar Pradesh, India.

 

Nirali D Vyas
GMERS Medical College, Vadnagar, Gujarat, India.

 

Soumya Kumar Acharya
Department of General Medicine, PGIMER and Capital Hospital, Bhubaneswar, Orissa, India.

 

Sanket Patel
GMERS Medical College, Vadnagar, Gujarat, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/aodhr/v2/5641