Clinical Evidence · OneRx

How Oncologists Are Using AI for Faster Evidence Lookup: From 6 Minutes to 47 Seconds

The volume of cancer research has outpaced any individual clinician's capacity to track it. Cancer-related publications indexed in PubMed have more than doubled over the past two decades, and the guidelines that distil them move just as fast.

The National Comprehensive Cancer Network maintains 88 cancer guidelines and revised them 241 times in a single recent year. Its non-small cell lung cancer guideline alone changed 11 times, close to once a month. For a practising oncologist seeing 30 to 50 patients a day, staying current is not a knowledge problem. It is a time problem.

This is the specific problem that medical AI platforms for evidence summary are designed to address. Not diagnosis. Not treatment selection. The narrower, more practical task of retrieving and summarising published evidence at the point of care, so the clinician can spend those minutes on the patient instead of the search bar.

What evidence lookup actually costs in a clinic day

Consider a typical scenario. An oncologist is reviewing a metastatic colorectal cancer case with an uncommon mutation profile. The question is straightforward: what do current NCCN and ESMO guidelines say about this presentation, and are there recent trial data worth knowing?

Answering that question the conventional way means opening the NCCN portal, navigating to the correct algorithm, cross-checking ESMO, and possibly running a PubMed search to see whether anything has changed since the last guideline update. Done properly, this takes several minutes per query. Done between patients, it often does not happen at all, and the clinician relies on memory instead.

In an internal prospective multicentre evaluation conducted across three tertiary oncology centres with 110 participants, the average time for conventional evidence lookup was 389 seconds. Using an AI evidence summary platform, the same queries took 47 seconds. That is roughly an 8 times improvement, and across a full clinic day it compounds into meaningful time returned to patient care.

Speed means nothing without accuracy

Any tool can be fast. The question clinicians rightly ask is whether the output can be trusted, and this is where most general-purpose AI tools fail in clinical settings. A chatbot that produces a fluent but uncited answer is worse than no tool at all, because verifying it takes longer than doing the original search.

The same evaluation was reported to SPIRIT-AI, CONSORT-AI and APPRAISE-AI standards, and measured accuracy directly. Platform responses showed high clinical accuracy and a strong safety profile, with 94.7% concordance against current guidelines. Every response was traceable to its source: NCCN, ESMO, ASCO, ICMR, PubMed or ClinicalTrials.gov.

Citation transparency is what separates a clinical evidence tool from a general chatbot wearing a medical interface. An evidence summary is only useful if the clinician can see exactly where each statement comes from and read the primary source in one click.

What this looks like in practice

Oncologists using evidence summary platforms describe a consistent set of use cases:

• Guideline verification during clinic. Confirming current first-line and subsequent-line options for a specific presentation, with the relevant guideline section cited, while the patient is still in the room.
• Rare presentations. Uncommon histologies and mutation profiles are where memory is least reliable and literature search is most time-consuming. This is where the time savings are largest.
• Tumour board preparation. Pulling current evidence for multiple cases in minutes rather than the evening before.
• Keeping pace with updates. When a guideline changes, the summary reflects it. The clinician does not need to have caught the announcement.

In the evaluation, physicians reported a confidence lift of 1.69 points on a 5-point scale when evidence was available at the point of care. That is not confidence in the AI. It is confidence in their own decisions, backed by current, cited evidence rather than recall.

The line that should never move

It is worth being precise about what these platforms do and do not do, because the distinction is the foundation of safe adoption.

An evidence summary platform organises and summarises published evidence for physician interpretation. It does not generate patient-specific treatment recommendations. It does not rank regimens. It does not tell an oncologist what to prescribe. The clinician always decides.

This is not a limitation to be engineered away. It is the correct division of labour. Guidelines are population-level documents. The patient in front of the oncologist has comorbidities, preferences, prior toxicities and circumstances that no summary can weigh. Clinical judgment stays with the physician, and any platform that blurs this line should be treated with caution regardless of how impressive its output looks.

What to check before adopting any evidence platform

For oncologists evaluating tools in this category, four questions separate credible platforms from the rest:

• Is every statement cited to a verifiable source? If you cannot click through to the guideline or paper, the answer is not evidence.
• Is there rigorous, standards-based validation? Prospective clinical evaluation reported to recognised AI study standards, not internal benchmarks alone.
• What happens to your data? Look for platforms that store no patient data, encrypt end to end, and hold recognised certifications such as ISO 27001, ISO 13485, SOC 2 Type 2 and compliance with HIPAA and India's DPDP Act.
• Does the platform respect the clinical boundary? Summary and citation, not recommendation.

The practical takeaway

Evidence lookup is one of the few parts of oncology practice where a well-designed tool can return time without changing how medicine is practised. The clinician asks the same questions and makes the same decisions. The difference is that the evidence arrives in 47 seconds instead of 6 minutes, cited and current.

More than 1,000 oncologists across 8 countries now use OneRx, a medical AI platform for evidence summary for clinicians, with over 10,000 clinical queries answered to date.

For oncologists who want to see how it fits their own workflow, the platform is available at onerx.ai.