Systems trained on incomplete or biased data can generate flawed recommendations, from misdiagnosing conditions to suggesting inappropriate treatments. AI systems often lack the contextual understanding required to navigate nuanced or ambiguous situations. For example, a predictive model may flag a high-risk patient but fail to consider socioeconomic status, access to care, or personal preferences — factors that influence the feasibility of certain interventions. Without safeguards, these errors may lead to adverse patient outcomes and significant legal and financial consequences.

This bias also extends beyond clinical applications. AI-driven human resources systems used for hiring, scheduling, or staff evaluations can inadvertently discriminate against specific demographics, affecting the diversity of health care providers and, in turn, influencing patient outcomes. Despite these risks, while around two-thirds of U.S. hospitals use AI-assisted predictive models, just 44% of hospitals evaluate these models for bias, raising concerns for patient care.

A critical first step is ensuring that AI applications undergo rigorous testing and validation before deployment. This includes diverse data sourcing, bias detection audits, ongoing performance evaluations, verifying the accuracy of predictions, and assessing reliability under various scenarios. Even after implementation, periodic reviews are necessary to address emerging risks and improve performance. Additionally, healthcare organizations must establish guidelines for how AI-generated insights are used in clinical decision-making. These insights should always be reviewed by providers who can apply critical thinking and contextual judgment, ensuring that recommendations align with patient-specific needs.

A collaborative relationship between AI and health care professionals is the best way to help mitigate risk while maximizing the benefits of advanced technologies.

Privacy and growing cybersecurity threats

Health care’s reliance on sensitive patient data makes privacy one of the most pressing issues in AI adoption.

From ransomware attacks targeting hospital networks to the manipulation of AI algorithms, the consequences of a breach can be catastrophic — jeopardizing patient safety and undermining trust in healthcare institutions.

Health care organizations should adopt robust cybersecurity measures to counter these threats. Encryption, multi-factor authentication, and continuous monitoring are baseline protections, but organizations also need to vet AI vendors thoroughly. Contracts should include assurances about security standards, and insurers can help guide these negotiations by identifying critical areas of risk.

AI as a catalyst for health equity

Beyond its challenges, AI holds significant promise in advancing health equity. By leveraging large datasets and sophisticated algorithms, AI can help identify health disparities and inform targeted interventions.

One of AI’s most immediate impacts on health care access is appointment scheduling and patient navigation. AI-powered scheduling systems help streamline access to care, reducing waiting times and ensuring patients are seen more efficiently. Additionally, AI-driven scheduling has been shown to decrease no-show rates by 10% per month while increasing hospital capacity utilization by 6%, helping facilities serve more patients.

For underserved communities, AI-driven telemedicine platforms can break down barriers like geographic distance and provider shortages. Automated scheduling systems integrated with AI chatbots can guide patients to available appointments, connect them with specialists faster, and prioritize urgent cases — reducing patient wait times by up to 80%.

Moreover, AI can help analyze social determinants of health — such as income, education, and geographic location — to predict patient outcomes and tailor care plans accordingly. This technology ensures that patients who face systemic barriers to healthcare receive proactive outreach and support, rather than falling through the cracks of an overburdened system.

However, to realize AI's full potential in promoting health equity, it is crucial that the data used to train algorithms is representative of diverse populations. Without diverse datasets, AI could reinforce existing disparities rather than mitigate them. By continuously refining algorithms, integrating human oversight, and ensuring broad representation in AI training data, healthcare leaders can leverage AI to expand, not limit, access to high-quality care.

A proactive path forward

As AI continues to transform health care, benefits, HR and organizational leaders must play a crucial role in ensuring its responsible implementation. AI offers significant advantages—but these benefits can only be realized through strategic oversight and thoughtful risk management. To navigate this evolving landscape, healthcare organizations should:

1. Establish a multidisciplinary AI oversight committee: Form a team comprising clinicians, IT professionals, legal advisors, and ethicists to evaluate and monitor AI tools. This committee should conduct thorough assessments before implementing any AI solutions. 
2. Develop comprehensive AI policies and procedures: Create clear guidelines that define acceptable AI use within the organization. These policies should emphasize safety, transparency, and accountability, outlining protocols for monitoring AI performance and addressing potential issues. 
3. Implement continuous training and education programs: Regularly train healthcare staff on the proper use of AI tools, potential risks, and ethical considerations. Encourage a culture of collaboration between humans and technology to ensure AI is used effectively and responsibly. 
4. Conduct regular performance audits: Periodically evaluate AI systems for biases and inaccuracies. Implement mechanisms for continuous performance evaluation to ensure AI tools remain reliable and equitable in patient care. 
5. Secure appropriate insurance coverage: Review existing insurance policies to identify potential gaps related to AI integration. Consider obtaining specialized AI liability insurance to protect against risks such as data breaches, system failures, or algorithmic errors. 

Melissa Menard joined CAC Specialty in April of 2023 as an Executive Vice President for the Healthcare Practice. Melissa brings a wealth of experience in strategy and business development, negotiations, and collaboration. Melissa is responsible for driving business development, formulating, and implementing sales strategies, and corralling CAC Specialty resources to grow the healthcare division.