Clinical AI is moving quickly from operational use into decision support, bringing a growing array of predictive and generative tools to the bedside. Yet expecting every clinician to select, apply and interpret complex models reliably is unrealistic. Evidence shows that handing model outputs directly to physicians does not consistently improve judgement, and common fixes such as model labels or visual explanations rarely close the gap. A pragmatic answer is a new specialist role that combines clinical insight with algorithmic fluency to guide day-to-day use and govern an organisation’s portfolio of models. Mirroring how radiology translates imaging and pharmacy stewards medicines, these physician-algorithm specialists would help turn technical performance into safe clinical impact.
Why Direct Physician–AI Use Falls Short
Multiple studies reported that clinicians do not reliably improve predictions when given algorithmic outputs, even when the underlying model outperforms human performance. The pattern extends to large language models whose independent accuracy can exceed physicians but whose use as an aid does not necessarily raise human diagnostic accuracy. The underlying barrier is familiar: asking non-specialists to incorporate novel technical signals into nuanced clinical reasoning without targeted support is a fragile strategy. Most clinicians recognise potential in AI but do not feel adequately prepared to use it directly.
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Common remedies also have limits. Probabilistic reasoning in medical education is necessary but not sufficient when indications, datasets and deployment contexts vary widely across models. Explainability artefacts, from “facts” labels to heatmaps, can fail to mitigate harm when an algorithm is wrong and can be interpreted in inconsistent ways. Static documentation cannot anticipate the breadth of real clinical scenarios and places an unrealistic burden on developers to foresee how and where models will be applied. The conclusion is not to abandon AI at the point of care but to recognise that safe, effective use demands a role designed to bridge technology and practice.
What An Algorithmic Consultant Would Do
The proposed specialist mirrors the two core responsibilities of clinical pharmacists: point-of-care guidance and system-level governance. At the bedside, they would advise on model selection and interpretation for specific patients and questions. The focus is not on the minutiae of model parameters but on aligning training cohorts with local populations, understanding strengths and weaknesses from technical publications and local validation and translating outputs into clinically actionable reasoning while adjusting for model limitations and human cognitive biases. In short, they would help clinicians update judgement accurately when AI is consulted.
At organisational level, the consultant would help build and maintain the hospital’s ecosystem of models much as pharmacists manage a formulary. Tasks include vetting third-party tools from academia or industry, implementing guardrails that define which patients, scenarios and user types can access a model, auditing fairness, monitoring real-world performance and deciding when to retrain or retire models as data or practice shifts. The role also creates a pathway for two-way communication between clinical teams and the bioinformatics community, identifying unmet needs and steering development accordingly. A recent example of a transcription tool that hallucinated clinical content illustrates the value of a gatekeeper capable of evaluating performance on local data and withholding deployment until safe use is demonstrated. The role would put into practice the AI strategy set by leadership roles such as the chief health AI officer or AI-focused informatics leadership.
Training, Adoption and Liability
The identity of the algorithmic consultant is bi-literate: clinically grounded and technically fluent. A practical training path sits within existing clinical informatics fellowships, enhanced with a subspecialty track. Emphases include advanced data science and probabilistic reasoning, strengths and weaknesses of explainability methods, structured point-of-care consultation skills, lifecycle management for models, governance and post-deployment surveillance and communication that translates complex technical concepts into actionable clinical guidance. A curriculum outline aligns these competencies with recognised domains in informatics training, adapting content rather than inventing an entirely new framework.
Introducing any new service brings cost and adoption questions. The argument parallels clinical pharmacy: while some activities have direct financial effects, much of the value lies in preventing adverse events and enabling safe operations. By improving the efficacy of the models they manage, consultants can raise the return on AI systems, reduce risk from misuse and increase clinician confidence. Adoption can be supported through proactive surveillance workflows that alert consultants to intervene when patterns in the electronic health record suggest suboptimal model use, similar to pharmacist notifications that flag problematic dosing.
Liability for algorithmic output remains unresolved, but the presence of a consultation service lowers risk exposure during a period when AI tools are entering practice rapidly. Physicians gain expert guidance rather than bearing responsibility for integrating complex tools alone, and health systems benefit from a curated, monitored portfolio aligned with governance principles. This does not obviate the need for broad AI literacy among clinicians, which remains essential for productive collaboration with specialists.
Clinical AI’s promise depends on more than model accuracy. Without practical translation at the bedside and disciplined governance at system level, performance gains may not change decisions or outcomes. A workforce of algorithmic consultants offers a credible path to bridge models and practitioners, improve decision-making and strengthen satisfaction with AI while managing risk. Anchored in enhanced informatics training and embedded across point-of-care and organisational workflows, the role positions health systems to convert technical advances into safe, effective patient care.
Source: npj digital medicine
Image Credit: iStock
