As healthcare continues to grapple with staffing shortages and margin pressures, emerging AI applications promise to revolutionize care delivery beyond today’s limitations.
Our recent webinar, co-hosted with Grounded Logic, brought to light “7 Real AI Use Cases Transforming Healthcare Today.” This session explored how innovative AI applications are already addressing these critical challenges and offered a compelling preview of emerging technologies poised to reshape healthcare delivery in the near future. Let’s take a look at some of those technologies that are on the near-term horizon.
AI-Enhanced Bed Management with Explainable AI
Traditional machine learning for bed management will get a significant upgrade through explainable artificial intelligence (XAI). The key advancement? Transparency and trust.
While current systems provide predictions, they are often opaque and function as “black boxes,” leading many clinicians to default to their own judgment.
XAI bridges this gap by providing clear, data-backed explanations for its recommendations. Some new AI models can now analyze 47 distinct patient characteristics upon admission to assess the likelihood of discharge within seven days, then — importantly— explain the reasoning behind those predictions.
This transparency increases adoption, leading to better resource management, cost savings, and improved patient care.
AI-Powered Performance Reviews in Operating Rooms
Just as sports teams analyze game footage, surgeons benefit from detailed post-operative reviews. Johnson & Johnson, for one, is pioneering AI systems that automatically generate surgical highlight reels for immediate post-op analysis.
This innovation eliminates the weeks-long wait for procedure reviews, allowing surgeons to analyze key moments right after a procedure while details are fresh in their minds. Beyond individual improvement, this technology offers tremendous potential for education and training, ultimately raising the overall quality of patient care.
Next-Generation Remote Patient Monitoring
Remote patient monitoring has existed for years, but generative AI is addressing its biggest challenge: data overload.
New systems like Validic’s patent-pending remote patient monitoring assistant (which integrates with Epic and Oracle) detect significant trends, generate summaries, and compare metrics over time. Rather than drowning clinicians in data, these solutions can now help them flag actionable insights.
These systems track changes across multiple health indicators—blood glucose, blood pressure, medication adherence, lifestyle factors—providing clinicians with concise, meaningful insights between patient visits. This holistic picture enables quicker understanding of patient health without requiring hospitalization, addressing both quality improvements and staff shortages.
AI-Accelerated Cancer Treatment Planning
Color Health has partnered with OpenAI to create a custom co-pilot designed to identify missing diagnostics and generate tailored workup plans by leveraging patient data and evidence-based healthcare guidelines. This application addresses two major factors that delay cancer care:
- Identifying missing diagnostics: AI quickly flags needed tests, preventing delays in treatment planning
- Accelerated treatment plan creation: Reduces development time from the current two-week standard
With early detection and treatment being the biggest factors in cancer costs and survival rates, this acceleration of care pathways represents a significant advancement.
The Evolution of EHR Platforms
Electronic Health Record systems are undergoing a significant transformation as AI capabilities become increasingly integrated into their core functionality and built into their future product roadmaps.
The future of EHR platforms is being shaped around four key pillars:
- Enhanced interoperability that will allow for seamless data sharing between previously siloed systems, creating more comprehensive patient records
- An improved user experience that will reduce clinician burnout by simplifying workflows and automating routine documentation tasks
- Operational efficiency advancements that will optimize scheduling, enable predictive resource allocation, and provide real-time analytics for capacity planning
- Strengthened security frameworks that are being developed to address growing data privacy concerns, with AI-powered threat detection systems that can identify unusual access patterns and potential vulnerabilities before breaches occur
More AI Applications on the Research Horizon
While not yet mainstream, these research developments hint at AI’s future potential:
- Preventative care through nonverbal analysis: Research from August 2024 demonstrated that AI can analyze facial micro expressions with 99.89% accuracy to predict health deterioration —often before symptoms become noticeable. In a time where acute staffing shortages may not allow for a deep level of patient monitoring by a clinician, this kind of capability can greatly aid with issues like stroke detection.
- Autonomous AI prescribing: The Health Technology Act of 2025 introduced legislation that would allow AI to be classified as a licensed practitioner with the ability to autonomously prescribe FDA-approved drugs. While stakeholder response has been mixed, with significant skeptics and concerns, these discussions highlight how AI-driven autonomy may reshape patient care in the future.
Navigating the Next Wave
As healthcare embraces these innovations, organizations must balance enthusiasm with strategic implementation. Key considerations include compliance with evolving regulations, integration with legacy systems, financial and resource constraints, workforce and cultural resistance, and ensuring algorithms avoid perpetuating biases.
