Strategic Plan for Artificial Intelligence

Last Updated: March 24, 2025

Vision

To integrate and drive innovation in artificial intelligence (AI) across WVSOM’s operations and efforts.

Mission

To leverage AI technologies in infrastructure, operations, academics, research, community engagement and healthcare delivery while upholding ethical standards and ensuring equitable access.

Strategic Priorities & Success Metrics

AI Infrastructure and Workforce Development

Priority

Build robust AI infrastructure and train faculty, students, and healthcare professionals in AI competencies.

Metrics

  • Creation of a position to manage and lead the establishment of AI efforts at the institution.
  • Outline of investments in AI infrastructure, including computing resources and data platforms.
  • Number of AI-focused workshops and training programs.
  • Percentage of faculty and students proficient in AI applications in medicine.

Priority

Collaboration among constituents and stakeholders across campus to ensure appropriate processes, policies, and guidelines to support appropriate AI integration across campus

  • Development of an AI taskforce to support AI efforts across the institution.
  • Collaboration with the Office of Research and the IRB to develop mechanisms to ensure proper oversight of AI projects (this could be through the AI taskforce).
  • Number of stakeholders represented on the taskforce.
  • Number of new processes, policies, and guidelines support by stakeholders as part of the AI taskforce.

Enriching Operations and Business Affairs through AI

Priority

Investigate AI-driven tools to improve the efficiency and processes to support operations and business affairs.

Metrics

  • Number of AI-driven tools developed
  • Performance improvements through AI-driven tools and time/cost saved on those products
  • Stakeholder satisfaction ratings on AI-driven tools supporting operations and business affairs

Enhancing Academics with AI

Priority

Investigate AI-driven tools to develop educational materials, personalize learning and improve education efficiencies and innovation.

Metrics

  • Number of AI-integrated educational materials developed.
  • Number of AI supported processes to improve efficiency.
  • Student performance improvement on AI-assisted materials.
  • Student and faculty satisfaction ratings on AI-driven educational tools.

Advancing Research in AI and Medicine

Priority

Foster interdisciplinary AI research to develop novel healthcare solutions.

Metrics

  • Number of AI-related research grants secured.
  • Number of peer-reviewed publications and citations in AI and medicine.
  • Number of collaborative research projects with industry and other institutions.

Community Engagement and Public Health AI Initiatives

Priority

Utilize AI to address public health challenges and enhance community healthcare access.

Metrics

  • Number of AI-driven public health initiatives launched.
  • Impact assessment of AI interventions on community health outcomes.
  • Community engagement levels in AI-driven health programs.

Integrating AI into Clinical Practice

Priority

Implement AI solutions to enhance diagnostics, treatment, and patient care.

Metrics

  • Number of AI-driven tools developed for the clinical setting.
  • Adoption rate of AI-driven decision support tools in clinical settings.
  • Improvement in diagnostic accuracy and treatment outcomes.
  • Reduction in administrative burden for clinicians using AI automation.

Ethical AI and Bias Mitigation

Priority

Ensure AI applications adhere to ethical guidelines and mitigation of bias especially in medical decision-making.

Metrics

  • Development and implementation of AI ethics training for students and faculty.
  • Regular audits of AI tools for bias and fairness.
  • Policies and guidelines established for ethical AI use in medicine.

AI Policy and Regulatory Compliance

Priority

Ensure AI adoption aligns with medical regulations and institutional policies.

Metrics

  • Establishment of AI governance committees.
  • Compliance rate with national and international AI regulations.
  • Frequency of policy updates in response to evolving AI applications.

Implementation Timeline:

Year 1:

Develop the AI infrastructure, implement initial training, create the AI taskforce, work toward the development of processes, policies, and guidelines

Year 1-2:

Establish foundational AI tools for operations and academics, initiate pilot research projects, and assess both community and clinical AI integration feasibility.

Year 2-3:

Expand AI-driven tools for operations and academics, scale research initiatives, develop AI technologies for community engagement and implement AI solutions in clinical settings.

Year 4-5:

Evaluate and refine AI strategies based on impact metrics, expand AI applications, and determine the need for a full AI research center.

Conclusion

This strategic plan outlines a comprehensive roadmap for AI integration at West Virginia School of Osteopathic Medicine. By aligning AI initiatives with operational and academic excellence, community engagement, research, clinical impact, and ethical standards, the institution will become a leader in AI-driven innovation.