Artificial intelligence (AI) is transforming the healthcare industry from the ground up. From smarter diagnostics to predictive analytics, AI in healthcare is a bold step whose development and deployment needs strong AI governance, stringent oversight, and human-centered design to avoid risk of inadvertent data exposure, algorithmic bias, diagnostic errors, non-compliance with healthcare data privacy laws, and loss of patient trust.
McKinsey’s recent survey conducted in Q4 2024 reveals that 85% of surveyed healthcare leaders have been exploring or have already adopted Gen AI capabilities. In another McKinsey flash survey, 63% of respondents characterize the implementation of Gen AI as a “high” or “very high” priority. Yet 91% of these respondents don’t feel “very prepared” to do so in a responsible manner.
This demonstrates an unprepared environment where there’s a significant readiness gap between AI adoption and the healthcare industry’s capability in leveraging AI safely. It underscores the critical requirement of assessing the role of AI in healthcare, why patient data privacy and security matter, common risks, and mitigation strategies.
The Role of AI in Healthcare
AI is proving itself as an emerging player across industries and healthcare is no different. Since the patient care industry handles vast volumes of sensitive patient data, AI is reshaping how healthcare providers deliver specialized patient care and how patients receive such care.
The technology packs the power to perform remote robotic surgery, provide predictive analytics, anticipate disease outbreaks, enhance patient outcomes, accelerate drug discovery, minimize operational costs and human reliance, as well as improve accuracy and speed, all while enabling healthcare organizations to scale their operations without hurdles.
More patient-focused benefits include personalized medicine, early disease detection, better patient understanding through health patterns and improved engagement to address health needs.
Practical Applications of AI in Healthcare
AI is already being utilized in healthcare settings across the globe. Leading hospitals, such as the Mayo Clinic, Johns Hopkins, and Cleveland Clinic, are putting these use cases into practice.
a. Mayo Clinic and Johns Hopkins (USA)
Strong examples of AI in healthcare applications are being established by hospitals like Johns Hopkins and the Mayo Clinic, which are employing it to decrease staff workload, personalize treatments, and identify diseases sooner.
Mayo Clinic is leveraging AI in cardiology research and clinical practices, leveraging 7 million ECGs to analyze vast patient data to identify patients with potential signs of stroke and at risk of heart disease, such as left ventricular dysfunction and atrial fibrillation (AFib).
Based in Los Angeles, Cedars-Sinai is a nonprofit healthcare institution. In 2023, the organization introduced Cedars-Sinai Connect. CS Connect is a virtual platform driven by artificial intelligence that provides patients with round-the-clock access to medical assistance. Through a chatbot + EHR interface, the AI solution automates patient intake, symptom evaluation, and preliminary treatment recommendations. More than 42,000 patients have benefited from it thus far.
By letting the AI tool handle the initial evaluation of patients or casualties to ascertain the urgency of their need and their data collection, CS Connect allows doctors to allocate their time more effectively while eliminating the need for manual processes and greatly enhancing access to healthcare.
c. National Health Service (UK)
No one leverages AI as much as the National Health Service in the United Kingdom. With AI, the NHS has been able to decrease last-minute cancellations and no-shows. Deep Medical is a model that uses machine learning to predict short-notice cancellations (less than 48 hours) and patient no-shows. The system uses AI to process unstructured data, such as notes from electronic health records, and structured data, such as patient demographics and visit histories, to identify relevant patterns.
Another AI model helps transform wound care by using an AI algorithm to support clinicians at various experience levels to assess wounds confidently, consistently and safely. A different AI model improves patient triage and optimizes staff time by using analysis of the structured and unstructured data input into the questionnaire by patients, and machine learning based on the outcomes of patients who go through the pathway to predict the best course of action for future patients.
Why Data Privacy and Security Matter in AI-Driven Healthcare
After financial data, healthcare data is among the most sensitive and valuable assets that include information such as personal health records (PHRs), biometric data, financial data for billing, etc.
AI models deployed in healthcare settings heavily rely on massive volumes of patient data, making data privacy and security a core component of ensuring information doesn’t get exposed in a data breach, isn’t exploited due to unauthorized access, and doesn’t violate regulations like HIPAA and GDPR, among others.
AI also introduces new ethical questions, such as who owns patient data and what data is used to train AI models, whether AI models are transparent in prompt responses, whether the AI model may inadvertently expose patient data, and many more.
Essentially, a robust data privacy and security posture is the backbone that ensures AI models are developed and deployed with privacy by design vs privacy by default principles, enabling healthcare organizations to secure sensitive patient information and trust, prevent unauthorized access, comply with regulations, maintain data integrity, and much more.
Strategies for Data Privacy and Security Risk Mitigation
When it comes to ensuring data privacy and security, there’s no shortcut. Mitigating data privacy and security risk in a hyperscale healthcare AI setting requires the deployment of multi-layered, resilient, compliant, and trustworthy practices, including:
a. Establish a Comprehensive Data Governance Framework
Data governance introduces comprehensive policies, practices and controls for ensuring data remains secure throughout the data lifecycle. It also assigns clear ownership and other requirements, such as data minimization, cross-border data transfers, and access restrictions, aligning the healthcare institutions’ data security posture with regulatory requirements and AI standards.
b. Strong Data Encryption & Anonymization
Healthcare providers should make sure that all data, particularly sensitive patient data, is encrypted while it's in transit and at rest. Cybercriminals find it far more difficult to access or alter data when it is encrypted.
Additionally, data anonymization further helps secure data by masking personal identifiers that connect a patient to their stored information. Both data encryption and anonymization create a top-notch data security layer where patient information remains private from unauthorized access, data leaks, or cyberattacks.
c. Secure AI Model Design & Leverage Anonymization Techniques
AI models are sprawling across industries. What distinguishes one from the other is how secure, reliable, transparent and accurate the AI model is. As a best practice and industry standard, healthcare institutions should embed data protection measures directly into AI system architecture. These include Privacy by Design, Privacy by Default, and Security by Design principles that are embedded from algorithm development to deployment and minimize risks proactively.
d. Comply with Regulations & Ensure Continuous Monitoring
Regulatory requirements act as an in-depth guide that helps set a benchmark for a robust data security and privacy posture. Key regulations include HIPAA, GDPR, PIPEDA, Health Data Law and other data protection laws. Regulatory compliance not only ensures patient data privacy and protection but also mitigates noncompliance penalties and the risk of data exposure due to a data breach.
On the other hand, continuous monitoring and auditing of AI systems help detect anomalies, security threats, or compliance deviations early on before they result in sensitive data exposure. Documenting these activities helps demonstrate compliance in case a regulatory authority initiates an inquiry.
e. Strengthen Organizational Awareness and Accountability
Build a culture of data stewardship through executive oversight, staff training, and cross-functional collaboration between IT, compliance, and clinical teams.
Automate AI Governance with Securiti
Large enterprises orchestrating GenAI systems face several challenges: securely processing extensive structured and unstructured datasets, safeguarding data privacy, managing sensitive information, protecting GenAI models from threats like AI poisoning and prompt injection, and unscalable GenAI pipeline operations.
Securiti’s Genstack AI Suite removes the complexities and risks inherent in the GenAI lifecycle, empowering organizations to swiftly and safely utilize their structured and unstructured data anywhere with any AI and LLMs.
It provides features such as secure data ingestion and extraction, data masking, anonymization, and redaction, as well as indexing and retrieval capabilities. Additionally, it facilitates the configuration of LLMs for Q&A, inline data controls for governance, privacy, and security, and LLM firewalls to enable the safe adoption of GenAI.
- AI model discovery – Discover and catalog AI models in use across public clouds, private clouds, and SaaS applications.
- AI risk assessment – Evaluate risks related to AI models from IaaS and SaaS, and classify AI models as per global regulatory requirements.
- Data+AI mapping – Map AI models to data sources, processing, potential risks, and compliance obligations, and monitor data flow.
- Data+AI controls – Establish controls on the use of data and AI.
- Regulatory compliance – Conduct assessments to comply with standards such as NIST AI RMF, EU AI Act, and more than twenty other regulations.
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