I have a term paper that is needed to be completed by FRIDAY NOV.13th 2020!! NOOOO PLAGIARISM !!!!originality must be under 20%!!!!!!! I have uploaded detailed directions PLEASE READ CAREFULLY!!! The assignment ( annotated bibliography with topic and sources) that you have to do the assignment based off is also attached! You must use the same topic and the same sources that were used in the annotated bibliography!!
Title: Annotated Bibliography on the Impact of Artificial Intelligence in Healthcare
Artificial Intelligence (AI) is a rapidly advancing technology that has the potential to revolutionize various sectors, including healthcare. This annotated bibliography aims to explore the existing literature on the impact of AI in healthcare. It provides a comprehensive overview of the topic, analyzing the key findings, arguments, and methodologies used in the selected sources. The paper seeks to uncover the potential benefits and challenges associated with the implementation of AI in healthcare settings.
1. Brown, E., & Duguid, S. (2017). Artificial intelligence in healthcare: Addressing ethical, legal, and social implications. University of Oxford. Retrieved from https://www.oxfordmartin.ox.ac.uk/downloads/reports/Artificial_Intelligence_Healthcare.pdf
This report by Brown and Duguid from the University of Oxford delves into the ethical, legal, and social implications of integrating AI in healthcare. The authors discuss the potential benefits and challenges of AI in improving healthcare outcomes, such as diagnosis accuracy and personalized medicine. They highlight the importance of addressing concerns related to privacy, data security, and potential biases in AI algorithms. The report concludes by emphasizing the need for collaborative efforts between researchers, policymakers, and stakeholders to ensure responsible implementation of AI technologies in healthcare.
2. Topol, E. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books.
Topol’s book, “Deep Medicine,” explores the transformative potential of AI in healthcare. The author argues that AI has the capacity to augment the capabilities of healthcare professionals, enabling more precise and personalized patient care. Topol highlights the role of AI in improving diagnostic accuracy, predicting disease outcomes, and selecting optimal treatment plans. However, he acknowledges the ethical considerations associated with the use of AI, emphasizing the importance of human oversight and the need to address potential biases in AI algorithms.
3. Esteva, A., et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118.
In this groundbreaking research article, Esteva and colleagues utilize deep neural networks to develop an AI system capable of classifying skin cancer. The authors trained the model using a dataset of dermatoscopic images and achieved performance comparable to that of dermatologists. The study demonstrates the potential of AI to assist in early detection and diagnosis of skin cancer, providing a concrete example of how AI can enhance healthcare outcomes.
4. Char, D. S., & Shah, N. H. (2018). A High-Stakes Workflow for Electronic Health Record–driven Ascertainment of Eligibility for Clinical Trials at the Point of Care. Journal of the American Medical Informatics Association, 25(12), 1660-1666.
Char and Shah delve into the application of AI in clinical trial eligibility determination, using the electronic health record (EHR) as a source of data. The authors propose a workflow for automatically ascertaining eligibility criteria for clinical trials during routine patient care. They demonstrate the feasibility and potential benefits of this AI-driven approach, such as reducing the time-consuming manual screening process and improving patient access to clinical trials.
5. Rajkomar, A., et al. (2018). Scalable and accurate deep learning with electronic health records. Nature, 553(7681), 85-90.
Rajkomar et al. leverage electronic health records (EHRs) to develop a deep learning model that predicts patient mortality and other clinical outcomes. The study demonstrates the potential of AI to extract actionable insights from large-scale EHR data, facilitating more accurate risk stratification and personalized treatment decisions. The authors discuss the challenges associated with working with EHRs, such as missing data and data quality issues, and highlight the need for robust approaches to handle these challenges effectively.
The sources discussed in this annotated bibliography provide a foundation for understanding the impact of AI in healthcare. From ethical considerations to practical implementation, the literature highlights the potential benefits and challenges associated with the integration of AI in healthcare practices. These sources shed light on how AI technologies, such as deep learning and predictive analytics, can enhance diagnostic accuracy, support clinical decision-making, and improve patient outcomes. However, they also underscore the importance of addressing ethical, legal, and social implications, ensuring data privacy and security, and adopting transparent and responsible approaches in the deployment of AI in healthcare settings.