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Sample Research Paper on Benefits of Artificial Intelligence in the Healthcare System Today

Benefits of Artificial Intelligence in the Healthcare System Today

      I.          Approved research question: What are the top three benefits of artificial intelligence in the healthcare system today in the United States?

    II.          Thesis: The purpose of this paper is to discuss the top three benefits of artificial intelligence on the healthcare system in the United States today. Various studies have shown that artificial intelligence has positive implications for diagnosis, early detection, and drug development (Li et al., 2019).  

  III.          Introduction: Artificial intelligence (AI) is one of the key characteristics that have been associated with the future of healthcare. The increase in the popularity of AI across several industries has led to the recognition of its relevance to the healthcare and pharmaceutical industries. Various potential benefits have been associated with the application of AI in healthcare service delivery, all with the overall objective of improving patient safety and care quality. In contemporary society, AI has been linked to positive implications in various aspects of healthcare, including diagnostic practices, drug development, and early disease detection.

  IV.          Main Point 1: The first main point is that AI improves diagnostic practices in healthcare (Li et al., 2019).  

a.      Supporting Detail 1: AI can be used for predictive modeling of biological data for predicting healthcare outcomes for patients (Cosma et al., 2017).

b.     Supporting Detail 2: Artificial intelligence finds relevance in predicting development trends and the potential applications of medical data, which are instrumental in disease diagnosis (Li et al., 2019).

c.      Supporting Detail 3: AI has found use in the control of health information management systems, some of which contribute to diagnostic processes (Cosma et al., 2019).

d.     Supporting Detail 4 (if needed): AI has resulted in the development of new diagnostic tools and methodologies, such as digital pathology, which have found relevance in oncology practice (Bera et al., 2019).

e.      Supporting Detail 5 (if needed): optional

    V.          Main Point 2: The second main point is that artificial intelligence supports drug development (Danysz et al., 2019).

a.      Supporting Detail 1: Artificial intelligence improves the efficiency of pharmacovigilance, which is important for drug safety (Danysz et al., 2019).

b.     Supporting Detail 2: Next, pharmaceutical companies are increasingly using computational biology as well as machine learning systems to predict molecular behavior, which is useful for the development of effective medications (Nagarajan et al., 2019).

c.      Supporting Detail 3: Finally, artificial intelligence promotes research and identification of primary mechanisms for biological resistance to cancer therapies, hence facilitating the development of precision medications (Nagarajan et al., 2019).

d.     Supporting Detail 4 (if needed): Research on the use of nanorobots for drug delivery has also shown the importance of artificial intelligence as a drug delivery technology (Hamet & Tremblay, 2017).

e.      Supporting Detail 5 (if needed):

  VI.          Main Point 3: The third main point is that artificial intelligence helps in revealing early disease risk (Li et al., 2019).

a.      Supporting Detail 1: Artificial intelligence has the capacity to facilitate disease staging, which is important for early detection and selection of treatment approaches (Takahashi et al., 2017).

b.     Supporting Detail 2: Next, artificial intelligence improves the timelines required for effective detection and treatment for both drug-sensitive and drug-resistance strains of diseases (Clemens et al., 2019).

c.      Supporting Detail 3: Finally, artificial intelligence has found application in the development of technological systems for risk early warning in conditions such as cardiovascular diseases and stroke (Li et al., 2019).

d.     Supporting Detail 4 (if needed):

e.      Supporting Detail 5 (if needed):

VII.          Conclusion

Artificial intelligence has found wide applications in the healthcare sector and is associated with various benefits. Some of these gains include improvement in diagnostic processes in healthcare, supporting drug development; and revealing early disease risk. AI improves diagnostic processes due to its predictive capabilities, ability to control health information systems, and the emergence of new technologies, such as digital pathology. Improved drug development courtesy of AI is attributed to the ability to perform pharmacovigilance, predict molecular behavior, and drug resistance. Early risk detection, on the other hand, is associated with disease staging ability, the capacity to improve detection timelines, and the development of risk early warning systems.


Bera, K., Schalper, K. A., Rimm, D. L., Velcheti, V., & Madabhushi, A. (2019). Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology. Nature Reviews, 16, 703- 715.

Clemens, D. L., Lee, B-Y., Silva, A., Dillon, B. J., Maslessa-Galic, S., & Nava, S. (2019). Artificial intelligence-enabled parabolic response surface platform identifies ultra-rapid near-universal TB drug treatment regimens comprising approved drugs. PLoS One, 14(5).

Cosma, G., Brown, D., Archer, M., Khan, M., & Pockley, A. G. (2017). A survey on computational intelligence approaches for predictive modeling in prostate cancer. Expert Systems with Applications, 70, 1-19.

Danysz, K., Cicirello, S., Mingle, E., Assuncao, B., Tetarenko, N., Mockute, R., Abatemarco, D., et al. (2019). Artificial intelligence and the future of the drug safety professional. Drug Safety, 42, 491-497.

Hamet, P., & Tremblay, J. (2017). Artificial intelligence in medicine. Metabolism, 69, S36-S40.

Li, B., Ding, S., Song, G., Li, J., & Zhang, Q. (2019). Computer-aided diagnosis and clinical trials of cardiovascular diseases based on artificial intelligence technologies for risk-early warning model. Journal of Medical Systems, 43(228).

Nagarajan, N., Yapp, E. K. Y., Le, N. Q. K., Kamaraj, B., Al-Subaie, A. M., & Yeh, H-Y. (2019). Application of Computational Biology and Artificial Intelligence Technologies in Cancer Precision Drug Discovery. BioMed Research International.

Takahashi, H., Tampo, H., Arai, Y., Inoue, Y., & Kawashima, H. (2019). Applying artificial intelligence to disease staging: Deep learning for improved staging of diabetic retinopathy. PLoS One, 12(6)

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