In recent years, the advancement of artificial intelligence (AI) technology has permeated nearly every aspect of human society, ushering us into an era characterized by immense computational power, large models, vast quantities of data, and an array of applicationsOne area where AI has made significant inroads is healthcare, where it aids in epidemic monitoring, drug development, medical imaging recognition, and mental health assessmentsYet, as the relevance of AI technologies expands within this domain, so too do the challenges posed by data security vulnerabilities associated with it.

The current shift towards networked, digitized, and intelligent healthcare systems brings with it a myriad of security risks, including but not limited to data breaches and privacy violations, network attacks and hacking incidents, data tampering and fabrication, unauthorized access and mismanagement of permissions, risks concerning the sharing of medical data, third-party service risks in data storage, insider threats, data redundancy and incorrect preservation, and legal compliance risks

Understanding these concerns, we can categorize the risks into three primary areas.

First, we must address the changes in cybersecurity that accompany the integration of AI technologiesThe evolving landscape of network security incidents shows a more complex interplay affected by AI, whereby traditional security issues have become more severe and multifacetedCyber crimes targeting the healthcare sector are alarmingly prevalent, with numerous cases of medical data being attacked or compromisedFor instance, in 2023, the All India Institute of Medical Sciences in New Delhi faced a ransomware attack on its medical equipment; similarly, in September of the same year, a configuration error at Confidant Health, a U.SAI healthcare company, resulted in the exposure of 5.3 terabytes of sensitive mental health records.

According to IBM's "2024 Cost of Data Breach Report," the healthcare sector has an average data breach cost amounting to approximately $9.77 million, marking it as the industry with the highest data breach costs for the 14th consecutive year

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Furthermore, reports from a domestic cybersecurity firm revealed that 900 million data leaks impacted the healthcare industry within the same yearThe gravity of the situation calls for immediate and effective responses.

The second area of concern pertains to model securityThe efficacy of AI applications in healthcare is profoundly linked to the safety of the underlying modelsThere have been multiple documented issues in recent years, including personal data leaks, model and algorithm attacks, bias within models, and vulnerabilities that could lead to further security issuesA significant contributor to these problems lies in the inadequacies of the AI models themselves, particularly concerning reliability and interpretabilityFor example, minor disturbances to input data may lead to erroneous outputs, or inherent flaws within the data set might yield inaccurate results.

The third critical concern revolves around ethical issues associated with AI deployment in healthcare

Various countries globally have issued guidelines regarding AI ethics, emphasizing the need for collaboration, transparency, fairness, accountability for malfeasance, and the protection of privacyIn the healthcare context, there are pressing requirements to safeguard individual autonomy, overall health and safety, as well as public interestsNonetheless, as the practical use of AI expands, ethical dilemmas—including bias, discrimination, misuse of technology, and liability in case of accidents—have become increasingly prominent.

To tackle the risks associated with the application of AI technologies in the healthcare sector, it is imperative to approach this challenge from angles of data integrity, security measures, and management strategies.

From a data perspective, enhancing security protections is essential to ensure both data integrity and the privacy of individuals, thereby fostering the transformation and modernization of AI technologies within the healthcare landscape

Data is a cornerstone of contemporary healthcare, representing a highly valuable assetHowever, despite the increasing volume of healthcare data, the quality has not necessarily improved in tandem, and problems like information silos continue to persistThere is a pronounced deficiency in collaborative data efforts and a myriad of obstacles that hinder cross-border data flows, necessitating a comprehensive breakthrough in these areas.

Concurrently, the safety measures implemented must balance development with security, thereby reinforcing the framework that supports security assurance in the sectorSpecifically, software should be a focal point of cybersecurity services to ensure the stability and reliability of intelligent healthcare systemsProtecting healthcare data from the risk of privacy loss for patients is vital while also enabling healthy collaborations and data exchanges within research parameters