Transparent Peer Review By Scholar9
DATA SECURITY IN THE AGE OF AI: REGULATORY MEASURES FOR AI IN MEDICINE
Abstract
Artificial Intelligence (AI) is a developing system worldwide. All the sectors are using AI on a large scale; why not healthcare? When it comes to healthcare and patient management, AI has become a disruptive force in the healthcare industry. However, there are serious safety and accountability issues with the use of AI on this delicate subject. DISHA, or the Digital Information Security in Healthcare Act, when implemented in India would hopefully cover some issues. Data privacy, algorithmic transparency, and the possibility of biases in AI systems are the basic issues we examine. Compared to other countries interpretations of AI in healthcare, this scale is lesser in India. Will use of AI in healthcare raise privacy and cybersecurity issues? Yes, industries are gathering private data using AI (heartbeat, ECG, blood pressure, etc.) by employing smart appliances. With whom the data will be shared? This article will delve into data security. Moreover, this paper enquires into how maintaining safety standards requires validation procedures, regulatory frameworks, and ongoing monitoring. This study seeks to give a thorough grasp of how to strike a balance between the advantages of AI and the requirements by analysing current practices. This paper also gives ideas to use AI for future development in the healthcare industry and suggestions to implement the laws related to AI. There are no specific laws enacted to deal with AI in healthcare. Keeping responsibility and risk mitigation front and centre, this study examines the important concerns surrounding the application of AI technologies in healthcare. While using AI technology, if the patient was discharged in the hospital, who is accountable—the hospital, doctors, or AI developer?
Phanindra Kumar Kankanampati Reviewer
10 Oct 2024 10:46 AM
Approved
Relevance and Originality
The topic of Artificial Intelligence (AI) in healthcare is highly relevant given the ongoing digital transformation in the industry. The article addresses crucial aspects such as data privacy, algorithmic transparency, and biases, which are pivotal for fostering trust in AI technologies. Its originality stems from its focus on the Indian context, particularly regarding the implementation of the Digital Information Security in Healthcare Act (DISHA). By comparing India’s approach to AI in healthcare with that of other countries, the article highlights unique challenges and opportunities, making a valuable contribution to the discourse on AI's integration in healthcare systems.
Methodology
The methodology appears to be centered on qualitative analysis, exploring current practices and issues related to AI in healthcare. However, the article would benefit from more explicit details about how data was collected and analyzed to support its claims. Including case studies or examples of AI applications in healthcare would enhance the credibility of the discussion. Additionally, outlining the criteria used to evaluate the effectiveness of AI implementations in healthcare would provide clearer insights into the methodology employed in the research.
Validity & Reliability
To strengthen the validity and reliability of the study, it would be helpful to incorporate empirical evidence, such as statistics or findings from existing research, that corroborate the points made about safety concerns and accountability issues in AI. Furthermore, addressing how potential biases in AI algorithms were identified or assessed would enhance the robustness of the study. Providing recommendations for best practices or frameworks for evaluating AI systems in healthcare could also reinforce the reliability of the article’s conclusions.
Clarity and Structure
The article presents its ideas in a coherent manner, with a logical flow that guides the reader through the complexities of AI in healthcare. However, improving the structure by using subheadings to delineate sections could enhance readability. Additionally, defining technical terms and concepts related to AI and healthcare would make the content more accessible to a wider audience. Including visuals, such as charts or infographics, to illustrate key points could also improve engagement and comprehension.
Result Analysis
While the article identifies critical issues surrounding AI in healthcare, a more thorough analysis of potential outcomes and implications of AI implementations would enrich the discussion. For instance, exploring how AI can improve patient outcomes or streamline processes in healthcare settings would provide a more balanced perspective. The question of accountability is particularly significant; expanding on different stakeholders' roles—hospitals, healthcare providers, and AI developers—would clarify the complexities of responsibility in AI applications. Including hypothetical scenarios or case studies demonstrating the real-world impact of these issues would further substantiate the analysis.
IJ Publication Publisher
Thank You Sir
Phanindra Kumar Kankanampati Reviewer