In a recent interaction with Higher Education Review, Prof (Dr) Parul Gupta, Associate Professor – Business and Corporate Law, Chairperson - Global Engagements, Outreach & Communication and Incubation Centre, Management Development Institute Gurgaon shared her views on how can healthcare organizations ensure the privacy and security of patient data used in AI systems, what measures should be implemented to protect against data breaches and unauthorized access and more.
How can healthcare organizations ensure the privacy and security of patient data used in AI systems? What measures should be implemented to protect against data breaches and unauthorized access?
It is crucial to prioritize data encryption & anonymization. In order to prevent unauthorized access, data should be encrypted both at rest & in transit. Also, anonymization is important since patient privacy must be maintained unless there is a compelling reason for disclosing personal information. Organizations should focus on utilizing only demographic information and avoid collecting unnecessary personal data.
Some of the other technical measures include access control & authentication. Access should be provided based on the role requirements within the organization, as this helps in ensuring that only the authorized personnel can access sensitive information. Also, to safeguard against unauthorized access multiple layers of authentication must be implemented.
Advanced technologies which include federated learning can be used for further enhancing data protection and since this technique allows models to be trained on local servers without centralizing the data, it facilitates in restricting data accessibility across numerous servers thereby reducing the risks of breaches.
Regular auditing & monitoring must be conducted by independent teams with expertise in technical, policy as well as ethical standards at the administrative level. This helps in ensuring that the data usage is managed well, & any potential biases or breaches are addressed promptly.
Furthermore, the concept of “privacy by design” must be implemented within the company culture. Privacy considerations must be integrated into the design as well as development phases of data models, & employees must be rendered regular training in order to reinforce the company’s commitment of protecting patient privacy.
Lastly, adherence to regulatory compliances which include India’s Digital Personal Data Protection Act (DPDPA), HIPAA (Health Insurance Protection and Accountability Act) in the United States and GDPR (General Data Protection Regulation) in Europe is critical, on the policy front. All of these regulations emphasize on data minimization which means that organizations must collect only the data that is required for a particular purpose. Also, informed consent should be taken from data providers and it should state clearly the purpose of collecting data & ensure that the data is utilized only for the purpose stated. Healthcare organizations can safeguard patient data effectively as well as uphold privacy standards by adopting these measures.
How can AI be integrated into healthcare in a way that supports, rather than undermines, the autonomy of healthcare professionals? What are the potential risks of over-reliance on AI systems in clinical decision-making?
The integration of Artificial Intelligence into healthcare decision making brings both positive as well as potentially adverse impacts on healthcare professionals. Artificial Intelligence can automate routine tasks, which will help healthcare professionals to focus on more skilled, critical decision making activities, on the positive side. This shift helps in not just freeing up their time but by encouraging these clinical professionals to acquire specialized skills it also helps in creating opportunities for career advancement. However, unfortunately, one of the adverse impacts of AI integration include the risk of job displacement.
Furthermore, as Artificial Intelligence advances, it may not only take over routine tasks but it may also take over some critical decision-making roles. This may include the reduction of the requirement of certain healthcare professionals. Healthcare professionals should upgrade their skills continually & stay ahead of AI advancements in order to stay relevant.
The limitation of Artificial Intelligence in handling clinical intuition is another major challenge. AI lacks the nuanced understanding & instinct that human doctors or healthcare professionals possess while it can learn from historical data. AI may struggle to make either accurate predictions or decisions in scenarios where there is no existing data for Artificial Intelligence to analyze, emphasizing the irreplaceable role of human intuition in healthcare.
What regulatory frameworks are needed to address the ethical concerns associated with AI in healthcare? How can policymakers ensure that AI systems are developed and used in compliance with ethical standards? What legal challenges might arise from the use of AI in healthcare, and how can they be addressed?
Numerous regulatory challenges arise, when considering AI adoption in the field of healthcare, especially concerning data protection laws. Since AI systems are trained on patient data, differing privacy regulations across numerous locations pose several challenges. Since the regulations are different in different regions it creates difficulties for AI developers trying to create systems which comply with global standards. Furthermore, owing to lack of uniformity in these regulations there are complications in the development as well as deployment of AI systems that are inherently cross-border technologies. Therefore, in order to address this, there is a pressing need for global regulatory frameworks which align expectations across diverse jurisdictions.
Another critical issue is accountability. Determining who is responsible for the errors is crucial particularly given the life-and-death stakes in healthcare, with AI playing a role in the healthcare industry. Current data privacy laws have not addressed accountability for Artificial Intelligence systems adequately and regulators should consider how to establish clear lines of accountability when Artificial Intelligence systems are utilized in the healthcare industry.
IP rights also present challenges and there is an ongoing debate on who owns the rights to AI generated outcomes. Is it the the healthcare professional providing the data, developer of the AI system or potentially both. Regulators should develop clear guidelines on IP ownership in resolving disputes as AI relies more on data and often proprietary for generating outcomes.
Lastly, a collaborative approach is crucial for effectively addressing these regulatory challenges. Regulators must work closely with healthcare professionals as well as AI developers, combining medical, legal as well as technical expertise. This multi-stakeholder collaboration is important for creating regulatory frameworks which are both practical & effective in managing the AI complexities in healthcare.
