OVID – Future of AI in healthcare

Navigating the future of evidence-based AI in healthcare

By Wolters Kluwer

AI has reached a critical turning point in healthcare, fundamentally changing how clinical decisions are made and research is conducted. With progress in machine learning and data science, AI is already demonstrating value in disease detection, treatment customization, and patient outcomes. Still, this marks only the beginning.

 

AI is no longer a passive instrument—it is becoming an active contributor to clinical reasoning. Algorithms can synthesize vast datasets in seconds, surfacing insights that support diagnosis and therapy selection. In imaging, AI can detect subtle patterns invisible to the human eye, enabling earlier and more precise diagnoses.

 

In research, AI is accelerating drug discovery, pinpointing candidates for clinical trials, and forecasting disease trends. By interpreting genomic and clinical data, AI empowers researchers to unravel complex conditions and develop novel therapies.

The need for evidence-based AI

 

Despite its promise, AI’s integration into clinical care depends on one thing: credible evidence. That’s where NEJM AI comes in. The journal is dedicated to delivering the rigorous, peer-reviewed research needed to responsibly bring AI from lab to bedside. Bridging innovation and practice requires a publication focused solely on evidence in medical AI—and NEJM AI fills that void. It prioritizes studies that confirm AI’s safety, reliability, and clinical utility. By enforcing high evidentiary thresholds, NEJM AI helps ensure that only trustworthy technologies reach patients.

 

AI innovation is moving fast—often faster than the frameworks used to assess it. NEJM AI champions stricter evaluation standards, ensuring that new tools are carefully scrutinized before entering clinical environments.

 

In a recent discussion, Arjun Manrai, PhD, of Harvard Medical School stressed the importance of grounding AI in evidence:

“For AI to truly transform healthcare, we must ensure that our algorithms are not just innovative, but also rigorously tested for efficacy and safety. This integration will ultimately lead to better patient outcomes and instill trust within the medical community.”

His remarks underscore the critical role evidence plays in linking computational progress to real-world care. Watch the full webinar on demand.

Challenges in evaluating AI technologies in healthcare

 

Assessing AI’s effectiveness remains one of the steepest hurdles to clinical adoption. Conventional evaluation models often lag behind AI’s rapid evolution. To close this gap, the field needs stronger validation frameworks—including randomized controlled trials (RCTs) that offer definitive evidence of AI’s benefits and limitations. Collaboration across researchers, clinicians, and policymakers is equally vital. Only through coordinated effort can evaluation standards evolve alongside innovation.

 

NEJM AI is centered on one goal: producing evidence that confirms AI’s readiness for clinical use. That means publishing high-impact research—especially RCTs—that meets exacting methodological standards.

• Prioritizing safety and reliability. NEJM AI emphasizes studies that validate AI’s safety profile and dependability, ensuring tools adopted in clinical settings are both effective and worthy of trust.

• Publishing randomized control trials. RCTs remain the benchmark for assessing new medical interventions. NEJM AI applies this same rigor to AI, publishing trial results that offer the evidence base needed for clinical adoption.

• AI in clinical trials. AI is poised to overhaul how trials are run—reducing costs, shortening timelines, and improving precision.

• AI as an intervention. In select studies, AI functions as the intervention itself—comparable to a therapeutic agent—and is evaluated for its direct impact on patient outcomes.

• Improving trial design efficiency. AI can also refine how trials are built. By mining historical data, it helps identify strong candidates and streamline protocols, accelerating timelines and lowering costs.

Importance of datasets and benchmarks

 

High-quality datasets and shared benchmarks are foundational to progress in medical AI. They offer the infrastructure needed to train, test, and compare models consistently. Community-defined benchmarks ensure that evaluation is both uniform and meaningful. When datasets are robust and metrics are standardized, researchers can build more capable algorithms—and regulators and clinicians can assess them with confidence.

AI and the patient-physician relationship

 

Far from replacing human connection, AI can strengthen it—by cutting through administrative clutter and freeing up time for meaningful interaction.

• Simplifying documentation. AI-powered tools can automate clinical note-taking and record management, reducing burnout and giving physicians more time at the bedside.

• Increasing patient engagement. Personalized insights, reminders, and educational content powered by AI help patients stay informed and engaged in their own care.

• Enhancing communication. When communication flows more smoothly, trust builds. AI can support clearer, more responsive dialogue between patients and providers, improving both satisfaction and outcomes.

Potential biases in AI models

 

AI systems—including large language models like GPT-4—can reflect and amplify biases present in their training data. This is particularly concerning in clinical contexts involving race, ethnicity, or socioeconomic status. Ongoing research seeks to identify, measure, and reduce these biases. Acknowledging the source of algorithmic bias is the first step toward correction. With continued investigation and transparent methodology, the field can move toward fairer, more equitable AI. Embedding accountability and ethical rigor into AI development will be essential to earning—and keeping—the trust of the medical community.

Addressing the need for clinical-grade evidence

 

NEJM AI was founded to fill a persistent gap: the shortage of high-quality evidence in medical AI. The journal is committed to surfacing the data behind the headlines and fostering rigorous, inclusive dialogue across the field. Every study published is held to clinical-grade standards. This commitment to quality is what makes AI adoptable—and trustworthy. For clinicians and institutions, this evidence provides the clarity needed to make informed decisions. NEJM AI champions responsible innovation, ensuring that AI serves patients and providers alike—not as a replacement for clinical judgment, but as a complement to it.

What’s next

 

The potential of AI in medicine is extraordinary—from sharper diagnostic insight to more personalized, efficient care. But potential alone is not enough. Realizing AI’s promise depends on evidence, rigor, and accountability. NEJM AI is building the bridge between innovation and application. Through high-caliber research and a steadfast commitment to ethical practice, the journal is helping to shape an AI-enabled future that is both effective and safe.

 

For healthcare leaders and practitioners committed to remaining at the leading edge, understanding AI’s trajectory is no longer optional. By staying current with the evidence—and contributing to the conversation—you become part of the movement shaping medicine’s next chapter.

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