Reference no: EM133579935

As has been discussed for years, Big Data in conjunction with AI and ML shows tremendous promise in improving patient outcomes in the healthcare industry. In fact, one of the early benefits of Big Data within the healthcare industry has been improved and/or speedier diagnosis of diseases (Guo, et al., 2023). However, deep learning can further improve the healthcare industry by helping determine a patient's potential response to drugs to treat their disease after diagnosis. For instance, a deep learning model has been created by Kuenzi, et al. (2020) called DrugCell which models cancer cell responses to drug lines and can be used by clinicians to choose the most appropriate drug to treat a specific type of cancer based on a patient's specific cancer cell structure.

By developing a deep learning model of drug interactions, efficacy, and side effects correlated with patient-specific information (personal health indicators such as family history, current medications, blood sample indicators, age, etc.) clinicians will be able to prescribe a more effective treatment for a disease (Personalized Treatment) while minimizing side effects and finding the best treatment without the 'let's try this and see how it goes' method. Prescription drugs are already expensive, and it is estimated that 2.8 Billion is wasted in prescribed medications just for medicare patients and the best way to reduce this waste is to reduce the need to discard the medications (Patel, et al., 2023). A deep learning model should drastically reduce this waste over time as well as speed up positive results of taking the right medicine at the right time.

Critically review the above discussion ideas feasibility.