Papers by Vidya Rajasekhara Reddy Tetala
AEME, 2024
Personalized medicine is transforming healthcare practices by tailoring specific treatment plans ... more Personalized medicine is transforming healthcare practices by tailoring specific treatment plans related to a patient's genetic profile, life patterns, and case history. The move to precision medicine finds its foundation in enhancements in the development of artificial intelligence and integration in health data. Generative AI, a type of AI trained from existing data to make new insights and predictions, has significant applications in analyzing complex data like genomic sequences, electronic health records, and realtime patient monitoring. AI integrated with health information will lead to earlier disease detection and better diagnostics, thus enabling customized treatment plans to improve the patient's outcomes and reduce health expenditures. This article will explore the currently ongoing technological transformation of healthcare, driven by AI and big data. It examines how personalized medicine empowers patients with real-time data and feedback, while various challenges keep arising with regard to data privacy, interoperability, and ethical concerns. It concludes with insight into the future of healthcare, where this segment of AI-driven personalized care has the possible propensity to transform how patients are treated.
International Journal of Computer Engineering and Technology, 2024
Personalized medicine is transforming healthcare practices by tailoring specific
treatment plans ... more Personalized medicine is transforming healthcare practices by tailoring specific
treatment plans related to a patient's genetic profile, life patterns, and case history. The
move to precision medicine finds its foundation in enhancements in the development of
artificial intelligence and integration in health data. Generative AI, a type of AI trained
from existing data to make new insights and predictions, has significant applications in
analyzing complex data like genomic sequences, electronic health records, and real-
time patient monitoring. AI integrated with health information will lead to earlier
disease detection and better diagnostics, thus enabling customized treatment plans to
improve the patient's outcomes and reduce health expenditures.
This article will explore the currently ongoing technological transformation of
healthcare, driven by AI and big data. It examines how personalized medicine
empowers patients with real-time data and feedback, while various challenges keep
arising with regard to data privacy, interoperability, and ethical concerns. It concludes
with insight into the future of healthcare, where this segment of AI-driven personalized
care has the possible propensity to transform how patients are treated.
International Journal For Multidisciplinary Research, 2024
Artificial Intelligence has now taken a full-fledged role in healthcare and has started driving i... more Artificial Intelligence has now taken a full-fledged role in healthcare and has started driving innovations not only in diagnostics and treatment planning but also in patient monitoring and operational efficiency. This will enable complex medical data analysis, extracting patterns and insights that no human is capable of. However, most of these models are per se opaque-that is, the so-called "black-box" problem-there are still great challenges in areas such as transparency, trust, and ethical applications in a clinical setting. This lack of interpretability can stand in the way of acceptance or integration for AI technologies when issues of understanding and accountability are relevant. Explainable AI solves these problems by making real artificial intelligence decisions understand the decisions made to humans. XAI techniques offer well-understandable and interpretable explanations of the models with minimum degradations in performance. This review article explains in detail the critical role of XAI in healthcare, underpinning how this field can bring more transparency into AI applications. We explain some of the current methods of XAI: modelagnostic techniques like LIME and SHAP, interpretable models relating to decision trees and linear models, and visualization techniques like saliency maps and mechanisms of attention.
International Journal of Science and Research (IJSR), 2024
The shift to digital platforms has absolutely aided the healthcare industry in healthcare deliver... more The shift to digital platforms has absolutely aided the healthcare industry in healthcare delivery; however, it has attached a very huge risk concerning data protection. This work discusses in detail challenges in managing healthcare data within the context of regulatory compliance with standards laid down by HIPAA, GDPR, and other regional regulations. It explains the predicament of healthcare providers in protecting the data, how technologies such as data analytics, , artificial intelligence (AI), and Site Reliability Engineering (SRE) can be utilized to enhance data security, and enumerates best practices that healthcare organizations can undertake in order to achieve regulatory requirements for cybersecurity.
International Journal of Science and Research (IJSR), 2024
Data analytics has now become a critical differentiator for innovation in healthcare R&D, affordi... more Data analytics has now become a critical differentiator for innovation in healthcare R&D, affording unparalleled opportunities to improve patient outcomes while optimizing clinical trials and overall costs. Integrating large volumes of patient data with advanced analytics such as ML, AI, and big data platforms, healthcare systems are transforming their capabilities to predict, prevent, and treat diseases. The article discusses how the role of data analytics is mushrooming in Healthcare R&D-from clinical trials to drug discovery, personalized medicine, and population health. It covers ethical concerns and privacy, challenges of data integration and quality, and future directions for real-time analytics, AI-driven predictions, and genomics integration. Diagrams and graphs depict ways in which Data Analytics processes and technologies are being put into action in solving problems from complex healthcare challenges.
International Journal of Science and Research (IJSR), 2024
Difference-inDifferences is a powerful means of obtaining an estimate for the causal effect of in... more Difference-inDifferences is a powerful means of obtaining an estimate for the causal effect of interventions from observational data, especially in health care, economics and social sciences where random controlled trials (RCT) are often impracticable. This paper explores how DID can be applied to healthcare cost-saving interventions by balancing treatment and control groups with propensity score matching and weighting with the inverse propensity score. It also discusses the recent development of this method, integrating machine learning into DID, which strengthens the capability of DID. Case study on cost reduction of hospital readmission illustrates the usefulness of the methodology. It is further elaborated with a detailed explanation of the calculation process and the application of the propensity score to remove the confounding biases.
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Papers by Vidya Rajasekhara Reddy Tetala
treatment plans related to a patient's genetic profile, life patterns, and case history. The
move to precision medicine finds its foundation in enhancements in the development of
artificial intelligence and integration in health data. Generative AI, a type of AI trained
from existing data to make new insights and predictions, has significant applications in
analyzing complex data like genomic sequences, electronic health records, and real-
time patient monitoring. AI integrated with health information will lead to earlier
disease detection and better diagnostics, thus enabling customized treatment plans to
improve the patient's outcomes and reduce health expenditures.
This article will explore the currently ongoing technological transformation of
healthcare, driven by AI and big data. It examines how personalized medicine
empowers patients with real-time data and feedback, while various challenges keep
arising with regard to data privacy, interoperability, and ethical concerns. It concludes
with insight into the future of healthcare, where this segment of AI-driven personalized
care has the possible propensity to transform how patients are treated.
treatment plans related to a patient's genetic profile, life patterns, and case history. The
move to precision medicine finds its foundation in enhancements in the development of
artificial intelligence and integration in health data. Generative AI, a type of AI trained
from existing data to make new insights and predictions, has significant applications in
analyzing complex data like genomic sequences, electronic health records, and real-
time patient monitoring. AI integrated with health information will lead to earlier
disease detection and better diagnostics, thus enabling customized treatment plans to
improve the patient's outcomes and reduce health expenditures.
This article will explore the currently ongoing technological transformation of
healthcare, driven by AI and big data. It examines how personalized medicine
empowers patients with real-time data and feedback, while various challenges keep
arising with regard to data privacy, interoperability, and ethical concerns. It concludes
with insight into the future of healthcare, where this segment of AI-driven personalized
care has the possible propensity to transform how patients are treated.