What AI Thinks AI Will Do in Healthcare

By Scott E. Rupp, editor, Electronic Health Reporter.

In 2025, AI in healthcare is no longer a distant ambition—it’s an operational force. But as we stare down the next five years, what matters isn’t what AI could do. It’s what it will do, based on current trajectory, real-world deployment, and policy infrastructure.

Let’s cut past the marketing fluff. Below is a grounded look at how AI is reshaping healthcare now—and how it will evolve by 2030—through the lens of diagnostics, documentation, monitoring, drug development, operations, and governance. This isn’t speculation. It’s what the tech, the economics, and the outcomes are already showing us.

AI in Diagnostics: From Hype to Clinical Utility

Recent developments in diagnostic AI underscore a leap beyond narrow models. Microsoft’s Multimodal AI Diagnostic Orchestrator (MAI-DxO), for example, has shown 85.5% accuracy in diagnosing complex conditions—significantly outperforming unaided physicians in a controlled study. It isn’t replacing clinicians, but rather augmenting them by synthesizing imaging, lab values, and clinical notes into actionable differentials.

What’s next? Between now and 2030, expect diagnostic support tools to become embedded into EHR workflows. AI won’t just suggest differential diagnoses—it will flag overlooked symptoms, propose appropriate next steps, and track care adherence. Clinicians who adopt this technology will find themselves practicing “assisted medicine,” with reduced cognitive load and more consistent care across patient populations.

Clinical Documentation: The Administrative Front Line

Physician burnout continues to correlate with time spent in EHRs—often charting late into the night. AI scribes and ambient listening tools like Suki, Abridge, and Nuance DAX are making measurable inroads. One recent study found documentation time dropped by over 60% after implementing voice AI, with corresponding improvements in patient satisfaction and physician experience.

This is one of the lowest-risk, highest-yield applications of AI in healthcare, and adoption is accelerating. By 2027, we should expect clinical documentation to be mostly machine-generated and human-edited in ambulatory care and some inpatient settings. Expect significant expansion into coding, utilization review, and real-time note summarization. In revenue cycle management, this will radically improve claims accuracy and reduce denials.

AI in Remote Monitoring: Early Intervention, Not Just Passive Data

The convergence of wearables, ambient sensors, and AI analytics is quietly becoming one of the most effective tools for managing chronic conditions. What’s changing now is contextualization: AI doesn’t just measure—it interprets and flags risk. Systems are already showing promise in detecting atrial fibrillation, early-onset heart failure, and even cognitive decline through pattern recognition in voice and movement.

Expect AI to play a growing role in longitudinal care between visits. More than 35% of U.S. health systems are expected to integrate AI-driven monitoring solutions by 2026. Hospital-at-home models will increasingly rely on these tools to support early discharge, flag adverse trends, and prevent readmissions—helping address the financial strain from value-based care models.

AI in Drug Discovery and Trial Design: Time-to-Therapy Will Shrink

AI is accelerating drug discovery by optimizing target identification, simulating molecular interactions, and streamlining trial recruitment. Insilico Medicine, Recursion, and Exscientia are examples of companies slashing preclinical timelines by up to 50% using AI.

By 2030, expect AI to redesign how clinical trials are run—from adaptive designs that learn during execution, to digital twins that simulate patient responses to reduce trial size. Large language models will also aid protocol writing, patient matching, and compliance documentation. The result? Fewer failed trials, faster paths to market, and dramatically lower costs.

Back-Office Automation: The Real Cost Frontier

Administrative complexity remains one of the largest sources of waste in the U.S. healthcare system. AI is already reducing this burden through automations in prior authorizations, denial management, supply chain logistics, and call center operations.

By 2030, back-office automation powered by AI will be table stakes. Health systems will deploy intelligent agents for high-volume tasks like eligibility checks, appointment reminders, claims scrubbing, and patient financial counseling. This will reshape the workforce, reallocating humans to oversight and exception handling, rather than repetitive processing.

Estimates from McKinsey and others suggest that automation could drive over $150 billion in annual savings across the U.S. healthcare system, without touching a single clinical procedure.

Regulatory Momentum and Ethical Infrastructure

As of mid-2025, over 340 AI-enabled tools are FDA-cleared, mostly in radiology and cardiology. The regulatory environment is slowly catching up to the pace of innovation, with a push toward lifecycle oversight, real-world performance data, and post-market surveillance.

The next challenge is equity and transparency. Recent studies highlight significant performance discrepancies across demographic groups. To avoid algorithmic bias becoming clinical harm, AI developers and health systems must prioritize diverse training data, model interpretability, and explainable outputs.

We’re also likely to see a move toward mandatory algorithm audits and AI “nutrition labels”—initiatives that clarify how models were trained, tested, and validated for real-world use.

What Health IT Professionals Should Do Now

As stewards of digital infrastructure, health IT leaders are at the center of this transformation. But the task isn’t just implementation; it’s orchestration. Here’s where to focus:

• Pilot with a purpose: Start small, measure well. Focus on low-risk, high-reward areas like documentation or revenue cycle automation.
• Govern with clarity: Stand up AI review boards and build governance frameworks now—before use cases scale.
• Invest in interoperability: AI is only as good as the data it receives. Ensuring clean, accessible, and standardized data remains the most strategic move any IT team can make.
• Push for explainability: If a vendor can’t explain how their AI reaches conclusions, don’t implement it. Full stop.

Final Thought: Beyond the Buzzwords

AI in healthcare is real, impactful, and increasingly essential. But this isn’t about science fiction. It’s about systems — designed, tested, and governed by people — serving other people.

By 2030, the systems that win will be those that operationalize AI in ways that are trusted, useful, and invisible to the patient. We don’t need to marvel at AI. We need to make it mundane, baked into the background, improving care every day, without fanfare.

That’s the AI future worth working toward.