AI-Driven IoT for Smart Health Care: Security and Privacy Issues

Including diverse datasets in AI model training is essential for minimizing biases and promoting equitable health outcomes.

“Including diverse datasets in AI model training is essential for minimizing biases and promoting equitable health outcomes [8,22].”

Despite efforts to anonymize data, AI's ability to identify individuals presents a significant privacy threat.

“Despite efforts to anonymize data, AI's ability to identify individuals presents a significant privacy threat.”

Such generative models would generate data not linked to actual individuals, allowing benefits of machine learning without risks of actual patient data.

“Such models would generate data that are not linked to actual individuals, allowing for the benefits of machine learning without the risks associated with using actual patient data [1,2,22,38].”

Generative models for creating synthetic patient data have been proposed to mitigate ongoing privacy concerns.

“Additionally, the development of generative models for creating synthetic patient data has been proposed to mitigate ongoing privacy concerns.”

The risk of reidentifying anonymized patient data using advanced AI algorithms is a pressing concern.

“The risk of reidentifying anonymized patient data using advanced AI algorithms is a pressing concern that underscores the need for clear communication with patients during the consent process.”

The potential for reidentification from anonymized data represents a significant privacy risk that must be addressed within informed consent.

“The potential for reidentification from anonymized data, as highlighted by research, represents a significant privacy risk that must be addressed within the informed consent process.”

The complexity of AI-driven IoT challenges patient autonomy and trust.

“The complexity of AI-driven IoT challenges patient autonomy and trust, underscoring the need for a comprehensive approach to patient education and consent.”

The continuous and dynamic nature of AI systems necessitates an equally flexible and responsive consent process.

“The continuous and dynamic nature of AI systems, which learn and adapt over time, necessitates an equally flexible and responsive consent process.”

The proliferation of AI-driven IoT in healthcare results in collection of extensive patient data, increasing risk of privacy breaches and unauthorized data exploitation.

“This concern is exacerbated by the proliferation of the AI-driven Internet of Things (IoT) in healthcare, which results in the collection of extensive patient data, thereby increasing the risk of privacy breaches and unauthorized data exploitation.”

Concerns regarding equity and bias in AI systems are integral to the informed consent dialogue.

“Concerns regarding equity and bias in AI systems are integral to the informed consent dialogue.”

AI systems can exhibit biases based on the data they are trained on, which could adversely affect patient care and outcomes.

“Patients should be informed about the potential for AI systems to exhibit biases based on the data they are trained on, which could adversely affect patient care and outcomes.”