Research argument

Annotated Bibliography

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PART 2: Annotated Bibliography

1. Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56. https://doi.org/10.1038/s41591-018-0300-7

This article focuses on the role of AI applications in the healthcare sector; they mention how it can optimize diagnostics, therapy planning, and patient surveillance. It also deals with the issue of embracing AI and associated barriers and Social impact and Ethical Issues in healthcare.

Evaluation: It mentions almost all common ways that AI can be applied in practice, therefore it will be useful to get an overview of the topic. He is a recognized authority in the field of AI and the article is published in a peer reviewed academic journal, ensuring that the information provided is both authoritative and informative on the capabilities and limitations of AI for change in healthcare.

2. Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., ... & Wang, Y. (2017). Artificial intelligence in healthcare: past, present and future. Stroke and Vascular Neurology, 2(4), 230-243. https://doi.org/10.1136/svn-2017-000101

This present review article attempts to chronologically and systematically analyze the potential and existing roles of AI in healthcare and medicine including diagnostic and prognostic abilities, personalized medicine and administration. It also contains the possibility of the future and possible developments of the technology of artificial intelligence.

Evaluation: From this source I was able to derive historical origin and present day use of Artificial Intelligence in the delivery of health care services. They are valuable in analyzing how technologies of artificial intelligence have developed over time and to gauge the current natural progression and trends in the specific area of the subject.

3. Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., ... & Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24-29. https://doi.org/10.1038/s41591-018-0316-z

The following guide highlights how deep learning works and how it has been adopted in healthcare, through object recognition, language understanding, and forecasting. It covers the details defining deep learning and its possibilities to assist in reaching better decisions in clinician’s daily practice.

Evaluation: As this source presented information about deep learning as a specific branch of artificial intelligence, it is useful for its technical content. They use clear language and illustrate it with simple real-world examples, which makes for a very informative read with regard to the practical specifics of how AI works on a technical level as well as clinical applications.

4. Reddy, S., Fox, J., & Purohit, M. P. (2019). Artificial intelligence-enabled healthcare delivery. Journal of the Royal Society of Medicine, 112(1), 22-28. https://doi.org/10.1177/0141076818815510

Thus, this article aims to discuss what role AI plays to improve the healthcare delivery systems as pertains to efficiency, patients’ results, and cost implications. It also dwells on possible limitations to the usage of AI, such as legal and ethical impediments.

Evaluation: This source provides possible advantages and limitations of using artificial intelligence in the examining of medical data. It is beneficial to gain awareness of the utility of the AI technologies and the obstacles that require a focus to optimize the AI systems.

5. Rajpurkar, P., Irvin, J., Ball, R. L., Zhu, K., Yang, B., Mehta, H., ... & Ng, A. Y. (2018). Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLOS Medicine, 15(11), e1002686. https://doi.org/10.1371/journal.pmed.1002686

This paper aims to contrast the performance of a deep learning model specifically with that of the practicing radiologists in the identification of the chest radiographs. These results prove the conceived hypothesis, showing that the application of the AI algorithm is accurate to the level of the expert and, thus, promising as a diagnostic tool.

Evaluation: This empirical study will serve us useful in establishing the fact that AI can indeed be useful in particular diagnostic tasks. It is useful for showing application of AI in hospitals and helps to make claims about need for implementation of such advances in medical field.

6. Healthcare Information and Management Systems Society. (2020). The impact of artificial intelligence on healthcare. Retrieved from https://www.himss.org/resources/impact-artificial-intelligence-healthcare

This is a HIMSS report that aims to introduce the application state of AI in healthcare, which also comes with detailed case descriptions of successful cases and sound practices. It elaborate about the opportunities and risks associated with the integration of AI technologies in healthcare and the possible development within the field in the future.

Evaluation: This report could benefit from your study because it is rich with practical recommendations and case studies of the use of AI in healthcare system. It provides a general view about the effects of AI on the whole market and can be considered a reliable source since the founding organization is HIS credited.

7. World Health Organization. (2021). Ethics and governance of artificial intelligence for health: WHO guidance. Retrieved from https://www.who.int/publications/i/item/9789240029200

This is a guideline by the World Health Organization and is focused on the ethical and governance consideration of artificial intelligence in the health sector. They make provisions for how AI should be created and deployed in a way that is ethically sound, and effective, as well as transparent.

Evaluation: It is valuable for learning about the ethics of using artificial intelligence in healthcare delivery as well as the guidelines surrounding it. That is why it is considered to be as an authoritative source which offers the crucial recommendations to the policymakers and the health care managers.

8. Maddox, T. M., Rumsfeld, J. S., & Payne, P. R. O. (2019). Questions for artificial intelligence in health care. JAMA, 321(1), 31-32. https://doi.org/10.1001/jama.2018.18932

These considerations therefore inevitably arise concerns regarding the incorporation of AI in health care, including matters like; transparency and fairness of the algorithms, and data protection. They have yet adopted a critical and reflective practice model in incorporating the use of AI in the delivery of healthcare services.

Evaluation: This source is relevant due to the critiques presented regarding the integration of AI within healthcare. It unmasks the vices to be looked at when it comes to the use of AI in various sectors and comes with key insights that are vital when discussing the innovation of the idea.

9. National Institute of Health. (2020). Artificial intelligence in healthcare: Transforming the practice of medicine. Retrieved from https://www.nih.gov/news-events/news-releases/artificial-intelligence-healthcare-transforming-practice-medicine

This review paper aims to identify and describe the role of AI in medical care and practice, as well as the existing state and the future of clinical applications and research. It reviews implications from the current research synthesis and underscores best practices in AI adoption.

Evaluation: Thus, the studied source can be deemed useful for the given paper because of the author’s attempt to provide an extensive overview of the AI contribution to medicine. Conventional publications accomplish the task of delivering topical data and expertise necessary for considering the current situation and potential development of AI in medicine.

10. European Commission. (2020). Artificial intelligence in healthcare: Opportunities and challenges. Retrieved from https://ec.europa.eu/digital-strategy/artificial-intelligence-healthcare-opportunities-and-challenges

This document from the EC concerns with the opportunities of using AI in healthcare, where it will be governed and whether there will be some new possibilities in the nearest future. It has a European focus as a way of approaching the incorporation of AI in the management of the healthcare sector.

Evaluation: They are of great help for those eager to implement or expand AI in healthcare, particularly in an international context. This issue is explained in detail with regards to the regulation and ethics, thus crucially framing the globalization repercussions of AI in medicine.

References

European Commission. (2020). Artificial intelligence in healthcare: Opportunities and challenges. Retrieved from https://ec.europa.eu/digital-strategy/artificial-intelligence-healthcare-opportunities-and-challenges

Healthcare Information and Management Systems Society. (2020). The impact of artificial intelligence on healthcare. Retrieved from https://www.himss.org/resources/impact-artificial-intelligence-healthcare

Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., ... & Wang, Y. (2017). Artificial intelligence in healthcare: past, present and future. Stroke and Vascular Neurology, 2(4), 230-243. https://doi.org/10.1136/svn-2017-000101

Maddox, T. M., Rumsfeld, J. S., & Payne, P. R. O. (2019). Questions for artificial intelligence in health care. JAMA, 321(1), 31-32. https://doi.org/10.1001/jama.2018.18932

National Institute of Health. (2020). Artificial intelligence in healthcare: Transforming the practice of medicine. Retrieved from https://www.nih.gov/news-events/news-releases/artificial-intelligence-healthcare-transforming-practice-medicine

Rajpurkar, P., Irvin, J., Ball, R. L., Zhu, K., Yang, B., Mehta, H., ... & Ng, A. Y. (2018). Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLOS Medicine, 15(11), e1002686. https://doi.org/10.1371/journal.pmed.1002686

Reddy, S., Fox, J., & Purohit, M. P. (2019). Artificial intelligence-enabled healthcare delivery. Journal of the Royal Society of Medicine, 112(1), 22-28. https://doi.org/10.1177/0141076818815510

Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56. https://doi.org/10.1038/s41591-018-0300-7

World Health Organization. (2021). Ethics and governance of artificial intelligence for health: WHO guidance. Retrieved from https://www.who.int/publications/i/item/9789240029200