Impact of Artificial Intelligence in Pharmacy

However, the negative effects of AI seem to have been overlooked. These effects have to be addressed in order to ensure value-based outcomes through the application of AI in the pharmaceutical industry. This research pays attention to the negative as well as the positive effects of AI in the pharmaceutical industry as a way of improving the knowledge base on the subject. In doing so, the research will utilise a mixed research method. This kind of research methodology gives greater chances of gathering more relevant information than when utilising either qualitative or quantitative on their own. To establish the positive impact of AI in the pharmaceutical industry 3. To examine the negative impact of AI in the pharmaceutical industry Research Questions 1. What are the ways of incorporating AI in the existing robots in the pharmaceutical industry? 2.

What are the positive effects of AI in the pharmaceutical industry? 3. What are the negative effects of AI in the pharmaceutical industry? Research Significance This research handles one of the current and most influential subjects cutting across various industries. The AI-designed drugs are yet to reach the market and remain a subject of contention among the stakeholders due to the risks that may be attached to the development (Smalley, 2017). This makes it clear that despite the fact that AI is deemed to be revolutionary achievement in the development of technology, there are some fears that have to be addressed. However, these fears are not so clear and therefore, more research is required to inform the process of decision making. With the increasing number of chronic disease cases across the globe, AI adoption seems to be a panacea in the pharmaceutical industry (Clark, 2007).

Technologies such as biotechnology, genomics, artificial intelligence, and wearable sensors are some of the developments that have been adapted to aid in handling most cases of chronic disease cases across the world (Piriyaprasarth, Patomchaiviwat, & Sriamonsak, 2009). Moreover, the approach will be useful in generating ideas and/or hypothesis that could be used in a quantitative study on the same subject in future. It will also uncover thoughts about the subject. Quantitative research design is a case whereby the researcher is concerned about logic, objective stance, and/or numbers (Williams, 2007). Sampling This research will utilise total population sampling. Total population sampling is a technique of sampling whereby the researcher would examine the whole population possessing specific set of characteristics such as a particular expertise, skills, and knowledge about the subject matter (Palinkas et al.

Apart from creating room for big data, artificial intelligence has improved handling of services in almost every department in the industry. This research, however, will give more attention to the negative effects of artificial intelligence due to the fact that they have been mostly overlooked in the previous research. References Brown, F. Saving big pharma from drowning in the data pool.  Drug Discovery Today, 11(23-24), 1043-1045. How data analytics and artificial intelligence are changing the pharmaceutical industry. Retrieved from https://www. forbes. com/sites/forbestechcouncil/2018/05/10/how-data-analytics-and-artificial-intelligence-are-changing-the-pharmaceutical-industry/#51dac3d03644 Clark, T. Adopting health care informatics and technologies. nature. com/articles/d41586-018-05267-x Hollingsworth, S.  J. Precision medicine in oncology drug development: a pharma perspective.  Drug Discovery Today, 20(12), 1455-1463. 005 Ibric, S.