Harnessing Big Data Analysis, AI and Advanced Computing
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Harnessing Big Data Analysis, AI and Advanced Computing


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Write a proposal on Harnessing big data analysis, AI and Advanced Computing for obtaining accurate disease diagnosis and treatment solutions



In medical practice, accurate diagnostic decisions are crucial, and treatment must be appropriate to patients. It is quite difficult to ascertain whether the history of a patient is considered while the physician decides on a patient illness. However, some physicians perform a routine physical examination by doing laboratory tests to diagnose the illness and make real decisions. Technology advancements have enabled IT experts to develop intelligent tools and systems which are aimed to support medical professionals in decision making. In the medical profession, the link between symptoms and disease is not always one-to-one; rather, symptoms can overlap many different symptoms, which is always quite difficult for a physician to diagnose the disease or illness accurately. Hence, intelligent systems are needed which can emulate human reasoning skills in diagnosing patient illness and resolving the problem.

The combination of artificial intelligence (AI) approaches and big data analysis and mining using high-performance computing systems are proving to be highly effective in different application areas, including health care and medical diagnosis. AI refers to the study that will emulate human intelligence into computer technology and software having huge potential for medicine. For example, one of the AI techniques is fuzzy logic. Fuzzy logic can simulate human reasoning from fuzzy or incomplete data, which helps in medical diagnosis. This technique is developed to overcome complexities and uncertainty in medical practice. Fuzzy logic computing technology can provide precise forms of illness from what is an imprecise symptom. Further, this technique can also help in providing precise modelling using conditions that are imprecisely defined. To cite another example, IBM's Watson is an AI-based application in medicine that supports physicians to filter through a complex set of patient symptoms, laboratory data, and imaging to finally arrive at the most likely clinical diagnosis and treatment options to finally result in patient’s health outcomes and also minimise costs in health care. Therefore, it is noted that AI provides a huge potential for physicians in supporting them with accurate diagnosis (Sikchi, Sikchi, & Ali, 2012).


Big data refers to all kinds of data available on the internet. This implies big data will include social network data, emails, audio/video and other multimedia data, data collected from devices such as sensors, RFIDs, etc., newsgroups, blogs, and so on, along with different types of transactional data. Big data is often available in unstructured form. Big data in medicine is the collection of datasets that are generated from multiple sources. The growth of digital health records over the years also fall into the big data repository (for example, electronic health records (EHR) and personal health records (PHR)) (Gai, Qiu, Chen, & Liu, 2015). All data related to health care and medical diagnosis is also known as biomedical information. Further, new medical discoveries new technologies, such as mobile applications for healthcare, capturing devices, wearable technology, sensors, etc., all such sources contribute to the growth of data. As of today, the healthcare sector generates huge amounts of digital data and is estimated to increase worldwide to up to over 25 exabytes by 2020 (Sun & Reddy, 2013).

It is important to note that medical data is very diverse to include both structured and unstructured data. Unstructured data in medicine includes handwritten notes, MRI scan images, radiographic films and so on (Feldman, Martin, & Skotnes, 2012). Despite the fact that big data in healthcare provides useful insights for physicians, issues such as patient privacy, reduction of healthcare costs, quality of treatment, and security must be ensured, which is not usually addressed in traditional practice. It is important to note that all such huge volumes of data require computing systems that have capacities to capture, store and analyse diverse datasets and hence this creates a need for advanced computing systems (Hashem, Yaqoob, Anuar, Mokhtar, Gani, & Khan, 2015).

Looking into the potential of AI and big data in healthcare, computerised systems adoption in medicine is intended to support the physician with reliable knowledge obtained from processed data that is acquired from different sources. The integration of information from various sources combined with big data analysis and data mining techniques along with AI systems aims to provide critical information related to intelligent and accurate decision making by physicians while diagnosing patients.

In this proposal, the potential of artificial intelligence techniques combined with big data analytics in healthcare is explored for its potential in accurately diagnosing patient illness and suggesting further treatment. The proposal highlights qualitative research methods and does not involve the development of a new system or artifact using technology to address the topic.

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