Abstract
Big data analytics and artificial intelligence are revolutionizing the global healthcare industry. As the world accumulates unfathomable volumes of data and health technology grows more and more critical to the advancement of medicine, policymakers and regulators are faced with tough challenges around data security and data privacy. This paper reviews existing regulatory frameworks for artificial intelligence-based medical devices and health data privacy in Bangladesh. The study is legal research employing a comparative approach where data is collected from primary and secondary legal materials and filtered based on policies relating to medical data privacy and medical device regulation of Bangladesh. Such policies are then compared with benchmark policies of the European Union and the USA to test the adequacy of the present regulatory framework of Bangladesh and identify the gaps in the current regulation. The study highlights the gaps in policy and regulation in Bangladesh that are hampering the widespread adoption of big data analytics and artificial intelligence in the industry. Despite the vast benefits that big data would bring to Bangladesh's healthcare industry, it lacks the proper data governance and legal framework necessary to gain consumer trust and move forward. Policymakers and regulators must work collaboratively with clinicians, patients and industry to adopt a new regulatory framework that harnesses the potential of big data but ensures adequate privacy and security of personal data. The article opens valuable insight to regulators, academicians, researchers and legal practitioners regarding the present regulatory loopholes in Bangladesh involving exploiting the promise of big data in the medical field. The study concludes with the recommendation for future research into the area of privacy as it relates to artificial intelligence-based medical devices should consult the patients' perspective by employing quantitative analysis research methodology.
Highlights
This study considers issues affecting the healthcare industry due to the adoption of big data analytics, including present trends and development in healthcare, future prospective, market environment, government policies and regulation and the challenges
Artificial intelligence-based medical devices expose considerable variance between performance setting environment and real-life practice setting due to the complexity of the system
Artificial intelligence–based medical devices or software are different from traditional medical software in terms of learning capacity, ubiquitous in medical interaction, making recommendations, and opaque nature of making such recommendations
Summary
Predictive analytics, artificial intelligence or algorithms, machine learning and deep learning can harness large volume datasets. These datasets can be used to improve diagnosis, inform preventative medicine practices and reduce adverse effects of drugs and other treatments. The impact of big data is visible across a variety of clinical settings and fields, including intensive care (Carra et al, 2020), emergency departments, cardiovascular diseases (Leopold et al, 2020), mental health (Simon, 2019), oncology (Patel et al, 2018), paediatrics (Li et al, 2020), psychiatry (Weissman, 2020), preventive care (Batarseh et al, 2020), ophthalmology (Brown et al, 2018), dementia (Ienca et al, 2018), diabetes (Musacchio et al, 2020) and asthma (Tang et al, 2020). There are several examples in the literature that discuss the analysis of large quantities of health data, using machine learning technology to diagnose and treat patients (Park et al, 2020)
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