Watson Technology for Oncology

This machine utilizes the mechanism of artificial intelligence to manipulate the commands from users and thereafter displaying appropriate results (Ferrucci et al, 2013). The development of this machines has been lauded by many health practitioners since all the patient’s medical history is stored in the database. Additionally, the retrieval process of these information is faster considering the sophistication level and precision of its working principle. It is also important to notice that by implementing Watson Technology for Oncology, the effectiveness of cancer treatment process is improved. One unique property of the Watson Technology for Oncology is that it suits the analysis of big data. Problem Statement Due to the increasing reports of cancer cases, it became necessary for medical researchers to devise efficient methods for diagnosing and treating cancer.

This is because, it was noted that involvement of medical professionals in the research against cancer became a slow process because it required the convention of practitioners of various specializations to agree on a single concept (Smith et al, 2012). For instance, a cancer management program for one patient required the attention of oncologists and radiologists of high specialization to administer. However, these attempts could still yield less results as compared to the initial expectations. Thus prompting for the inclusion of technological firms which could specialized in medical innovations to produce machines or drugs that destroy cancerous tissues. In higher order definitions, this machine intelligence can perform effectively just like the cognitive functionalities of human beings. As a result, it can understand and interpret human speech, compete in strategic games and also perform routine automated functions.

Based on these characteristics, various researchers made attempts towards utilizing artificial intelligence to improve healthcare provision. (Jha & Topol, 2016) conducted a research on the possibility of implementing artificial intelligence in the treatment of cancer. While the project was initially aimed at supporting doctors’ efforts, it emerged that the developed machines could perform just as effectively as the employed medical practitioners. According to (High, 2012) IBM’s initial attempts to achieve machine-assisted medications were successful. Thus, it remained an indication that the commitment to use Watson Technology in the diagnosis of cancer will also maintain its effectiveness. In an experiment to demonstrate the capability of its inventions on artificial intelligence, IBM developed an autonomous robot which effectively conducted a tissue-surgery on a pig by cutting-open the bowel and stitching it back together (High, 2012).

In another incidence, Watson technology was implemented while diagnosing a woman with leukemia, leading to successful recovery of the patient. From these cases, it is clear that artificial intelligence can be properly utilized to achieve highly efficient medical services, only comparable to the most competent doctors. Under the descriptive category, the researcher is expected to evaluate case reports and series which directly relate to the occurrences of the medical condition under study (Renehan et al, 2008). From this evaluation, it is possible to understand the medical history of patients, and also the analysis of their profiles or exposure. It is of importance to perform a critical evaluation of such secondary information because some might be misleading thus leading to a greater deviation of results.

Some patients may also provide distorted data for the sake of privacy concerns. Lastly, a descriptive design must employ a population study criterion where the participants are grouped according to the results of their medical history, or other observational characteristics such as age or sex. Therefore, it can be seen that both the treatment and observational studies are inclined towards the same conclusion, with a difference of 1. The success rate of Watson Technology in the diagnosis and treatment of cancer patients can be computed as an average of 75%. It is believed that similar results will hold even in the entire population because this study only considered a small sample from a few health facilities. Interpretations of Results From the data analysis, it is clear that artificial intelligence is effectively helping in the diagnosis and treatment of cancer.

Considering the high number of success rates, health facilities should be encouraged to adopt this new computer-assisted medication method. According to this diagram, scientific research against cancer is centered between the collaboration of patients and the government. This implies that national governments are required to provide necessary funding and support through the established organizations like the Food and Drug Administration (FDA), The Cancer Genome Atlas (TCGA), the National Cancer Institute (NCI) and the TARGET therapy. On the other hand scientific research needs to involve various aspects like animal and clinical trials, compound ideas, as well as both translational and basic research. Lastly, patients are required to corporate by providing medical and family history, previous treatments and diagnoses, genomics, test results and outcomes.