Presentation
Trust, Security, and Regulatory Compliance in AI: Literature and Practical Experience, and the Way Forward for AI in Healthcare
DescriptionTitle: Trust, Security, and Regulatory Compliance in AI: Literature and Practical Experience, and the Way Forward for AI in Healthcare
Abstract:
Artificial Intelligence (AI) is rapidly transforming healthcare, offering unprecedented opportunities to enhance patient care and healthcare systems. With AI technologies, including machine learning (ML) and natural language processing (NLP), healthcare organizations can optimize workflows, improve decision-making, and deliver more personalized care. However, to fully realize AI’s potential, it is essential to integrate human factors (HF) principles and adhere to stringent regulatory frameworks that ensure both safety and trust. This paper explores future considerations for AI in healthcare, emphasizing data management, human factors, and regulatory compliance with, for example, the National Institute of Standards and Technology’s (NIST) AI 100-01 and AI 600-1, Health Insurance Portability and Accountability Act (HIPAA), Health Information Technology for Economic and Clinical Health Act (HITECH), and Health Information Trust Alliance (HITRUST). We review the HF literature and industry developments relating to AI’s trustworthiness from the standpoints of automation trust theory and security/privacy. We extend the findings of this past work as applied to AI in general and explore the implications of these principles for the unique characteristics of healthcare applications.
AI Healthcare Applications: Data Management and Workflow Optimization
AI’s ability to handle vast and complex datasets is revolutionizing healthcare data management. From electronic health records (EHRs) to real-time diagnostic data, AI technologies streamline workflows by automating tasks such as data extraction and predictive analytics (Chen et al., 2023). Predictive models and NLP tools have demonstrated their value in reducing manual errors, enabling faster decision-making, and improving patient care efficiency. Moving forward, these AI systems must also comply with frameworks such as NIST SP 800-53, which provides security and privacy controls to safeguard sensitive healthcare information (Matheny et al., 2019). Future iterations of AI, such as generative AI tools, will enhance healthcare's ability to predict patient outcomes, enabling “personalized” medicine while complying with HITRUST and HITECH data privacy and security standards.
Human Factors in AI Adoption
The future success of AI in healthcare heavily depends on integrating human factors principles to ensure that AI systems are user-friendly, trustworthy and trusted, and aligned with healthcare professionals' cognitive and physical needs. Poorly designed AI systems can increase cognitive load, stress, and burnout among healthcare workers (Matheny et al., 2019). Therefore, it is essential to apply sound usability principles to the design of interfaces. In many AI applications much of the interaction between the user and the automation is text-based, which simplifies the UI design questions. However, the increased sophistication of some of the AI tools being explored for healthcare, such as the presentation of data extracted and analyzed by the tool, or AI’s use in high-stakes settings such as diagnostics and treatment planning (Shneiderman, 2020), will require a deeper consideration of user interface issues.
Automation trust principles are also a key HF-related component of AI design for healthcare. Schwartz et al. (2023) identified aspects of the human-AI system that influence user trust, including reliability, transparency of the AI’s behavior/model, and various characteristics of the task interaction. The aspects they identify are not unlike those covered in other trust literature. Kaplan et al. (2023) conducted a meta-analysis of trust-in-AI work addressing influences on trust, categorized broadly into three categories of factors, all of which have been shown relevant: aspects of the user, of the AI, and of “the shared context of their interaction” (p. 337). The “shared context” aspect is akin to the “system confidence” construct studied by Masalonis and Parasuraman (2003).
For all these reasons, human factors principles are essential in the design and evaluation of AI systems. We delve into the implications of the aforementioned citations and others for the application of HF principles specifically to AI in healthcare, to ensure seamless integration of AI into healthcare workflows while reducing errors and improving patient outcomes.
Privacy, Security, and Ethical Considerations
Another aspect of “trust” extends beyond user attitudes towards AI tools and their outputs to encompass privacy, security, and ethical considerations. AI systems that handle sensitive data, such as Personally Identifiable Information (PII) and Protected Health Information (PHI), must comply with stringent privacy regulations, including HIPAA, the General Data Protection Regulation (GDPR), and HITECH. The increasing reliance on AI for healthcare decision-making heightens concerns about data privacy and protection, necessitating robust security measures across all AI systems (Matheny et al., 2019).
The work of ZeroTrusted.ai, among other contributions to AI security, highlights key concerns regarding AI privacy and security, particularly in the context of Generative AI. These concerns include content anomalies, data protection, and AI application security, which are pivotal for ensuring that AI models produce reliable and unbiased outputs while safeguarding sensitive patient data from risks such as adversarial attacks, model theft, or data poisoning (Gartner, 2024). Advanced AI security strategies are essential to address these risks, and current approaches focus on anomaly detection, robust data protection measures, and AI application security. These solutions need to be specifically tailored to the unique challenges posed by Generative AI in healthcare, where ensuring the integrity of data and AI models is crucial for maintaining patient trust and safety (Krush, 2024).
Regulatory frameworks, including NIST SP 800-53, GDPR, and HITRUST, provide important guidelines for ensuring that AI systems adhere to established privacy regulations and protect against unauthorized data access, particularly in domains like healthcare, where data sensitivity is heightened. These frameworks help ensure that AI systems comply with strict security standards, offering structured approaches to safeguarding patient data. In this paper, we summarize key solutions that support compliance with these regulatory standards (Krush, 2024), while also exploring the human factors considerations that intersect with security and privacy requirements in healthcare settings.
Ethical considerations in AI development and usage are also becoming increasingly significant, particularly in relation to algorithmic bias. Biases in AI systems, often rooted in the data used to train these models, have the potential to exacerbate healthcare disparities, disproportionately affecting marginalized groups. Langer et al. (2023) explore trust issues in domains with a strong component of ethical considerations; healthcare is clearly one of these. The question of ethics intersects with the security/privacy issues covered above--and with the issue of mission criticality. Applications with ethical components are frequently those which are also high-stakes domains, and the trust literature frequently addresses the fact that humans may be slower to properly calibrate their degree of trust in an automated tool when the consequences of inappropriate trust are high. Ethical AI development requires continuous monitoring for bias and the implementation of fairness algorithms that ensure equitable treatment for all patients (Chen et al., 2023). This paper discusses methodologies for identifying and addressing bias in AI-generated outputs (Krush, 2024).
Lastly, securing AI applications is vital to protect them from adversarial attacks that can undermine the reliability and security of healthcare AI systems. In addition to technological defenses, human factors such as trust, system confidence, and UI and interaction design also play critical roles in the overall security framework. This paper addresses the intersection of AI security with human factors, exploring how confidence in AI systems can be fostered through robust security solutions tailored to healthcare (Krush, 2024).
Citations and References Ready.
Abstract:
Artificial Intelligence (AI) is rapidly transforming healthcare, offering unprecedented opportunities to enhance patient care and healthcare systems. With AI technologies, including machine learning (ML) and natural language processing (NLP), healthcare organizations can optimize workflows, improve decision-making, and deliver more personalized care. However, to fully realize AI’s potential, it is essential to integrate human factors (HF) principles and adhere to stringent regulatory frameworks that ensure both safety and trust. This paper explores future considerations for AI in healthcare, emphasizing data management, human factors, and regulatory compliance with, for example, the National Institute of Standards and Technology’s (NIST) AI 100-01 and AI 600-1, Health Insurance Portability and Accountability Act (HIPAA), Health Information Technology for Economic and Clinical Health Act (HITECH), and Health Information Trust Alliance (HITRUST). We review the HF literature and industry developments relating to AI’s trustworthiness from the standpoints of automation trust theory and security/privacy. We extend the findings of this past work as applied to AI in general and explore the implications of these principles for the unique characteristics of healthcare applications.
AI Healthcare Applications: Data Management and Workflow Optimization
AI’s ability to handle vast and complex datasets is revolutionizing healthcare data management. From electronic health records (EHRs) to real-time diagnostic data, AI technologies streamline workflows by automating tasks such as data extraction and predictive analytics (Chen et al., 2023). Predictive models and NLP tools have demonstrated their value in reducing manual errors, enabling faster decision-making, and improving patient care efficiency. Moving forward, these AI systems must also comply with frameworks such as NIST SP 800-53, which provides security and privacy controls to safeguard sensitive healthcare information (Matheny et al., 2019). Future iterations of AI, such as generative AI tools, will enhance healthcare's ability to predict patient outcomes, enabling “personalized” medicine while complying with HITRUST and HITECH data privacy and security standards.
Human Factors in AI Adoption
The future success of AI in healthcare heavily depends on integrating human factors principles to ensure that AI systems are user-friendly, trustworthy and trusted, and aligned with healthcare professionals' cognitive and physical needs. Poorly designed AI systems can increase cognitive load, stress, and burnout among healthcare workers (Matheny et al., 2019). Therefore, it is essential to apply sound usability principles to the design of interfaces. In many AI applications much of the interaction between the user and the automation is text-based, which simplifies the UI design questions. However, the increased sophistication of some of the AI tools being explored for healthcare, such as the presentation of data extracted and analyzed by the tool, or AI’s use in high-stakes settings such as diagnostics and treatment planning (Shneiderman, 2020), will require a deeper consideration of user interface issues.
Automation trust principles are also a key HF-related component of AI design for healthcare. Schwartz et al. (2023) identified aspects of the human-AI system that influence user trust, including reliability, transparency of the AI’s behavior/model, and various characteristics of the task interaction. The aspects they identify are not unlike those covered in other trust literature. Kaplan et al. (2023) conducted a meta-analysis of trust-in-AI work addressing influences on trust, categorized broadly into three categories of factors, all of which have been shown relevant: aspects of the user, of the AI, and of “the shared context of their interaction” (p. 337). The “shared context” aspect is akin to the “system confidence” construct studied by Masalonis and Parasuraman (2003).
For all these reasons, human factors principles are essential in the design and evaluation of AI systems. We delve into the implications of the aforementioned citations and others for the application of HF principles specifically to AI in healthcare, to ensure seamless integration of AI into healthcare workflows while reducing errors and improving patient outcomes.
Privacy, Security, and Ethical Considerations
Another aspect of “trust” extends beyond user attitudes towards AI tools and their outputs to encompass privacy, security, and ethical considerations. AI systems that handle sensitive data, such as Personally Identifiable Information (PII) and Protected Health Information (PHI), must comply with stringent privacy regulations, including HIPAA, the General Data Protection Regulation (GDPR), and HITECH. The increasing reliance on AI for healthcare decision-making heightens concerns about data privacy and protection, necessitating robust security measures across all AI systems (Matheny et al., 2019).
The work of ZeroTrusted.ai, among other contributions to AI security, highlights key concerns regarding AI privacy and security, particularly in the context of Generative AI. These concerns include content anomalies, data protection, and AI application security, which are pivotal for ensuring that AI models produce reliable and unbiased outputs while safeguarding sensitive patient data from risks such as adversarial attacks, model theft, or data poisoning (Gartner, 2024). Advanced AI security strategies are essential to address these risks, and current approaches focus on anomaly detection, robust data protection measures, and AI application security. These solutions need to be specifically tailored to the unique challenges posed by Generative AI in healthcare, where ensuring the integrity of data and AI models is crucial for maintaining patient trust and safety (Krush, 2024).
Regulatory frameworks, including NIST SP 800-53, GDPR, and HITRUST, provide important guidelines for ensuring that AI systems adhere to established privacy regulations and protect against unauthorized data access, particularly in domains like healthcare, where data sensitivity is heightened. These frameworks help ensure that AI systems comply with strict security standards, offering structured approaches to safeguarding patient data. In this paper, we summarize key solutions that support compliance with these regulatory standards (Krush, 2024), while also exploring the human factors considerations that intersect with security and privacy requirements in healthcare settings.
Ethical considerations in AI development and usage are also becoming increasingly significant, particularly in relation to algorithmic bias. Biases in AI systems, often rooted in the data used to train these models, have the potential to exacerbate healthcare disparities, disproportionately affecting marginalized groups. Langer et al. (2023) explore trust issues in domains with a strong component of ethical considerations; healthcare is clearly one of these. The question of ethics intersects with the security/privacy issues covered above--and with the issue of mission criticality. Applications with ethical components are frequently those which are also high-stakes domains, and the trust literature frequently addresses the fact that humans may be slower to properly calibrate their degree of trust in an automated tool when the consequences of inappropriate trust are high. Ethical AI development requires continuous monitoring for bias and the implementation of fairness algorithms that ensure equitable treatment for all patients (Chen et al., 2023). This paper discusses methodologies for identifying and addressing bias in AI-generated outputs (Krush, 2024).
Lastly, securing AI applications is vital to protect them from adversarial attacks that can undermine the reliability and security of healthcare AI systems. In addition to technological defenses, human factors such as trust, system confidence, and UI and interaction design also play critical roles in the overall security framework. This paper addresses the intersection of AI security with human factors, exploring how confidence in AI systems can be fostered through robust security solutions tailored to healthcare (Krush, 2024).
Citations and References Ready.
Event Type
Oral Presentations
TimeMonday, March 3111:00am - 11:30am EDT
LocationPier 2/3
Digital Health (DH)