Global Artificial Intelligence in Healthcare: Key Takeaways from the HIMSS AI Forum 2025
Earlier this month, over 400 healthcare leaders gathered in Brooklyn, New York, for the HIMSS AI in Healthcare Forum. The event brought together senior clinical, operational, and IT professionals from diverse healthcare organizations for two days of focused discussion on artificial intelligence (AI) and machine learning (ML) applications in healthcare.
The conversations highlighted both the promise AI holds for addressing critical health system challenges and the significant obstacles still to overcome. Participants explored broad issues like executive leadership, workforce training, and organizational trust, alongside essential practical matters such as data governance, algorithm transparency, workflow integration, and cybersecurity.
Key Discussion Points
- Data Governance Challenges: Ensuring reliable, ethical management of healthcare data remains a top priority. Effective governance is crucial for trustworthy AI deployment.
- C-Suite Leadership: Strong executive commitment and strategic direction are essential to guide AI initiatives and align them with organizational goals.
- Building an AI-Enabled Workforce Culture: Training and empowering staff to work effectively with AI tools fosters adoption and maximizes benefits.
- Potential AI Regulation: Attendees considered how upcoming state and federal regulations might impact AI use in healthcare, emphasizing the need for compliance and ethical standards.
- Emerging Trends: Participants identified ongoing developments and future directions in AI applications for clinical care, patient engagement, and operational efficiency.
Insights from the Forum Emcee
Rob Havasy, senior director of informatics strategy at HIMSS and the forum’s emcee, shared perspectives on where AI in healthcare stands in 2025. He noted that despite years of AI projects, healthcare leaders continue to learn and adapt as technologies and applications evolve.
Havasy emphasized the importance of transparent algorithms and sound data practices to build trust among clinicians and patients. He also highlighted workforce readiness as a critical factor for realizing the return on investment in AI technologies.
Additional Topics of Interest
- Developing large language model (LLM)-powered clinical applications that align with doctors' workflows
- Research into AI-driven patient engagement, such as ongoing studies at NYU
- Strategies for cautious implementation of agentic AI to ensure patient safety
- Challenges faced by underserved hospitals amid the digital divide
- Evaluating health AI investments from a venture capital perspective
- Steps for building in-house AI tools tailored to specific healthcare needs
- The role of AI in enhancing digital health and improving quality and safety
- Placing people and trust at the center of AI-enabled healthcare delivery
Moving Forward with AI in Healthcare
Healthcare organizations are at a pivotal point with AI—balancing enthusiasm with caution. Success depends on clear leadership, rigorous data and algorithm oversight, workforce development, and patient-centered approaches.
For healthcare professionals looking to deepen their AI knowledge and skills, exploring dedicated training can be valuable. Resources like Complete AI Training’s latest courses offer practical guidance tailored to healthcare roles and technology trends.
As AI continues to develop, staying informed and prepared will be key to leveraging its benefits safely and effectively.
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