A Review on Artificial Intelligence and Machine Learning in a Medical Device
Abstract
Medical Device manufacturers have been interested in artificial intelligence (AI). However, there is a constant need to evaluate its use and performance due to system complexity, the variety of their architecture, as well as ethical and legal problems. This study offers a narrative commentary on the past, present, and future applications of machine learning (ML) algorithms and artificial neural networks (ANN) in medical devices. Finding challenges and issues with AI integration in medical devices was one of the study's main research goals. From clinical engineering to medical applications, artificial intelligence is transforming healthcare. Prior to realizing the full potential, though, ethical, legal, and social issues must be addressed. Its application must also be scrutinized and regulated in terms of fair access, privacy, suitable uses and users, liability, bias, and inclusivity.
Conclusion
The goal of this study is to comprehend technology's accessibility, recognize artificial intelligence's enormous potential in the healthcare industry, and keep tabs on recent scientific advancements to motivate fellow researchers. Up until now, privacy and security, trust, bias, and accountability and accountability issues have dominated ethical discourses on artificial intelligence and health. As the technology's scope continues to grow, more issues will surely surface.
Artificial Intelligence is relatively new when it comes to medical devices. Manufacturers of medical devices are predicted to abandon their conventional business models until 2030 in Favor of new digital artificial intelligence techniques. It is necessary to create a regulatory framework before introducing AI-based MDs to the market. The process of defining AI regulations and policies pertaining to MDs is still in its early stages, according to prominent regulatory bodies globally. In order to facilitate the adoption of regulatory frameworks and standardize the market, international standards pertaining to AI in MDs are required. Organizations like IEEE, ISO, and IEC are working to standardize data quality management and the use of AI in ways that impact human welfare.
Even with acknowledged barriers, it is possible to draw the conclusion that AI has already fundamentally altered the way traditional medicine is practiced, greatly raised the caliber of medical care, and ensured universal health. It remains to be seen how the human population will be affected by the potential for future development of medical AI in addressing issues like chronic illnesses, infectious pandemics, and the aging population.
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References
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