IBM – balancing AI decision making and privacy in healthcare This is the question we are addressing: To what extent, should a Clinician (Human) no longer be across the 'Prognosis' of treatment in...

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Balancing AI decision making and privacy in healthcare
BALANCING AI DECISION MAKING AND PRIVACY IN HEALTHCARE
Introduction:
Artificial intelligence (AI) is an advanced computational technology that uses its high processing power to simulate human-like activities at an accurate level. The integrity of AI had led to huge development and transformation of different fields. Even though AI assistance in technological advancements has revolutionized how machines work and process information, their application in the healthcare sector always remains debatable. Healthcare is one of the critical sectors that requires AI assistance for improving timely imaging, diagnosis, prognosis, and the development of precision treatments (Price and Nicholson, 2019). With different advantages listed out; yet integration of AI in healthcare remains obsolete in action due to the arising privacy and ethical issues after application.
AI decision making and privacy in healthcare:
Healthcare is one of the indispensable sectors that help people to sustain diseases and combat deadly pathogens. All the diseases and infections can be prevented from being lethal if they are diagnosed within the appropriate window period. Likewise, a disease prognosis in a person requires different combinatorial analyses of medical specialists to plan out medications based on the likely condition of the disease. But for appropriate clinical management of the disease, the clinicians are required to accurately understand and predict the risks and results of the disease based on the available biomarkers and pathological assessment (Han et al., 2020). As humans, clinicians prognose a patient with their ability over an infection or disease handling experience. If the professionals are not aware of handling a certain infection, this might lead to disastrous treatment outcome. Thus, these are some of the scenarios where the ability of the person falls short in comparison to the AI (Lysaght et al., 2019).
Accurate prognosis for saving people’s life requires analysis of several patient data and analyzing them to understand the disease or infection in a better way. Analysis of the data to develop a pattern over the symptoms can help the professionals to develop ways to understand diseases with similar symptoms and their different patient outcomes after treatment. It improves the quality of healthcare given to patients with higher confidence and reliability on the results. Thus, AI can be trained to store and analyze the data precisely and accurately for helping the professionals ease their work in the treatment procedures (Han et al., 2020). Training AI efficiently with different actual test cases and other symptoms of the patient for particular diseases can develop better algorithms for better decision making. It can help clinicians to rely upon AI without thinking twice as the pattern analysis of AI would hit a true confident test case. Yet the bottleneck of developing these advancements requires quality test cases for training AI. It subsequently requires trust from the patients’ side for relying on AI without questioning the ethical and privacy issues (Han et al., 2020; Triberti et al., 2020).
AI when integrated on medical applications it is generally concerned over several ethical obligations. As a human, the patient trusts the clinician upon main principle of ethics that are; beneficence, nonmaleficence, autonomy, and justice (Gerke et al., 2020; Varkey, 2021). Beneficence defines the act of the clinicians that benefits the patients and provide moral support. Nonmaleficence suggest the act of the professionals that least harm the patients and concern about their well-being. Principle of autonomy gives power to the...