Enhancing Neonatal Public Health: AI-Based Security Strategies for Mitigating Clinical and Data Risks in Neonatal Care
DOI:
https://doi.org/10.63682/jns.v14i31S.7237Keywords:
Artificial Intelligence (AI), Neonatal Intensive Care Unit (NICU), Clinical Decision Support Systems, Healthcare Data Security, Explainable AI (XAI), Blockchain in Neonatal CareAbstract
Artificial Intelligence (AI) is increasingly the innovative driver in neonatal care, powering breakthroughs in early diagnosis, predictive analytics, surgical planning, and intense monitoring of care. AI technologies are increasingly being brought into neonatal intensive care units (NICUs), from identifying early neonatal infection signs to guiding complex surgical decision-making. But these benefits are accompanied by serious issues involving data safety, treatment reliability, and ethical compliance. Neonatal care relies significantly on sensitive patient data by AI systems and hence are vulnerable to breaches, hacking, and exploitation. Computational flaws and unexplainability can also lead to diagnostic error with significant clinical impact in susceptible neonates. The article explores the convergence of AI technology with public health security for neonatal care. It is focused on key dangers like false leakage, unauthorized access, and algorithmic bias in AI. It proposes diverse AI security methods to counteract these hazards, such as federated learning for decentralized data processing, blockchain for secure record-keeping, and explainable AI (XAI) frameworks to enhance clinical transparency. The report can be accessed freely from:. These methods are aimed at safeguarding patient information, making the model easier to comprehend, and establishing trust with caregivers and practitioners. Initial evaluations reveal that integrating AI-security models into neonatal systems has the capability to reduce exposure to data by over 60%, improve the accuracy of diagnostics, and reduce delay in decision-making. These methods' application in neonatal public health systems is technologically feasible and ethically required. This research encourages the development of comprehensive AI regulation frameworks focused specifically on neonatal care that balances responsibility with innovation to achieve the greatest public health benefits for newborns.
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