What responsible AI should look like in 2026

Prior authorization has become one of the leading burdens for physicians and their teams, often resulting in unnecessary delays, costs and fragmentation within day-to-day clinical workflows. As expectations for faster decisions, clearer visibility and better payer-provider collaboration accelerate in 2026, prior authorization sits at the center of growing pressure to modernize how clinical decision-making is supported and connected across the care continuum, from automation to clinical assistance.

As federal prior authorization and interoperability requirements begin to take effect this year — alongside recent industry-wide commitments — health plans are increasingly turning to AI-powered automation to meet these expectations. Physicians want greater efficiency, but not at the expense of clinical judgment, care quality or patient safety. Skepticism remains high, with 61% of physicians in the annual American Medical Association survey expressing concern that artificial intelligence (AI) could increase denial rates, making transparency and clinical oversight nonnegotiable as AI becomes more embedded in the prior authorization process.

Physicians aren’t skeptical of AI because they fear technology, but because too many automation solutions introduced into utilization management workflows have been designed to optimize speed or cost, rather than to enhance transparency and support clinical judgment. With AI playing a growing role in clinical decision-making, responsible deployment is a necessity, reinforcing clinical reasoning, not replacing it.

In practice, responsible AI means clinically grounded automation that supports transparent, collaborative decision-making, makes requirements easier to interpret and accelerates appropriate, quality care while keeping clinicians in the loop and the focus on safe, timely patient care.

AI’s role in clinical decision-making

Health plans are increasingly using AI to modernize outdated, time-consuming prior authorization workflows. But systems trained without clinician oversight or health care-specific data often produce inconsistent or inappropriate recommendations, misalign with policy and force physicians to spend more time on administration than patient care.

Responsible AI is redefining this. It makes clinical rationale transparent, surfaces relevant clinical insights and policy in real time, and ensures care recommendations are evidence-based and traceable. Responsible, clinically trained AI streamlines decision-making, reduces administrative burden and strengthens consistency and collaboration throughout the care journey — ultimately enhancing the speed, quality and cost of care.

How AI earns physicians’ trust

For AI to truly earn physicians’ trust, clinical oversight is non-negotiable. AI-powered automation should function as evidence-backed support — surfacing relevant clinical insights and policy-aligned recommendations — while physicians remain in control of final decisions. With this approach, AI acts as a trusted assistant, helping clinicians make more informed decisions without replacing judgment. Importantly, AI should never be used to deny care; instead, it should only be used to enhance decision-making for timely approvals of care.

Responsible health care AI must be clinically trained to the domain — including specialty area — which differs fundamentally from general-purpose large language models. Health care is uniquely complex, with evolving evidence-based guidelines, nuanced medical policies, regulatory scrutiny, and strict clinical and coding requirements.

Responsible, clinically trained models are built on rich health care data and continuously refined with physicians to understand these complexities with precision and depth. This enables AI to generate insights and recommendations that are explainable, traceable and grounded in clinical evidence, which helps to identify missing documentation early, reduce friction and delays, and minimize downstream appeals.

Consistency, accuracy and fairness must also be monitored continuously. Ongoing validation detects performance drift, makes sure models behave consistently across relevant cases and reinforces alignment with the latest evidence-based guidelines and policies. Without the necessary clinical oversight, AI-powered automation can create inconsistencies in care, added burden and unnecessary clinical and compliance risks. Responsible AI systems require ongoing vigilance to maintain reliability, fairness and trustworthiness as a physician’s second opinion.

Where clinically trained AI is making an impact today

Clinically trained AI is already improving burdensome utilization management and payment workflows in measurable ways for physicians and their teams:

• Reducing care delays and downstream disruption: Authorization delays often stem from incomplete or missing clinical documentation, resulting in avoidable peer-to-peers, denials and appeals. Clinically trained AI can prompt submitters for missing information during intake and extract structured clinical details from patient records, ensuring that requests align with coverage criteria and include all necessary supporting evidence before submission. Upstream guidance reduces unnecessary back-and-forth requests and minimizes downstream bottlenecks that delay care and increase administrative burden.
• Improving decision accuracy and consistency: Responsible health care AI consistently applies the latest evidence-based guidelines and medical policies across requests, reducing the variability that can lead to confusion, delays and appeals. For physicians, this means clearer expectations, more accurate decisions and predictable outcomes, and faster reimbursement.
• Streamlining the path from care delivery to payment: Using clinical insights captured upstream during prior authorization in downstream payment processes can unlock newfound transparency and efficiency and connect historically siloed parts of the care continuum. Precision AI — trained specifically on reimbursement methodologies — can match authorization data to claims data and perform real-time clinical and coding validation before payment, improving payment accuracy while decreasing the need for retrospective audits, denials or lengthy appeals. A unified approach helps physicians receive faster, more consistent reimbursement and eliminates the friction that comes from fragmented operations.

What physicians should expect in 2026

Health care AI is at a turning point. The industry is beginning to separate general-purpose automation from precision, health care AI, which is continually fine-tuned with feedback from expert human reviewers. That distinction is especially important in prior authorization, wherein poorly designed automation and inaccurate decisioning can delay patient care, overburden physicians and create unnecessary costs and risk.

In 2026, physicians should expect that truly responsible health care AI becomes defined not by speed, but by its ability to enhance physician judgement, intelligently automate complex workflows and deliver decisions that match, or exceed, the accuracy of expert reviewers. In turn, this enables more than just prior authorization modernization. AI can help bridge the gap between pre-care planning and downstream payment processes by leveraging upstream clinical data to improve accuracy, consistency and precision in clinical and coding validation, and ultimately replacing siloed workflows with a more cohesive, clinically aligned care continuum.

Responsible AI is a clinical imperative

AI will continue to play an increasingly central role in how health care operates, including care approvals, delivery and reimbursement. Though not all AI approaches are created equal, especially in health care. Industry analysts note that shifting from general-purpose to domain-specific models promises high impact, but that there must be proper development, integration and governance.

It’s imperative that physicians are actively involved in training and guiding the application of AI technology. The value of AI depends on responsible deployment, including transparency, deep clinical training, ongoing validation and continued physician oversight, to ensure decision-making stays aligned with evidence-based guidelines, medical policies and regulatory requirements.

When these elements are built in by design, clinical intelligence becomes a trusted extension of expert clinical reasoning, enabling appropriate care to move forward more efficiently, minimizing delays that disrupt care delivery and reimbursement, and reducing administrative burden across the care continuum.

Brian Covino, M.D., FAAOS, is chief medical officer at Cohere Health.