Best AI Radiology Tools

Aidoc is the most complete radiology AI platform for a hospital. It started with triage, flagging urgent findings like brain bleeds and pulmonary embolism so they jump the reading queue, and now runs dozens of FDA-cleared algorithms through aiOS, its operating system for clinical AI. aiOS handles data routing, monitors each model's performance over time and adds governance tools that many hospitals lack.

In January 2026 the FDA cleared Aidoc's CARE foundation model (K252970) for comprehensive abdomen CT triage: 11 new and 3 existing acute findings in one device. Aidoc reports a mean 97% sensitivity and 98% specificity in the pivotal study and says it cuts false alerts by about an order of magnitude compared with single-condition tools; both are vendor figures. Aidoc has 34 entries on the FDA AI list, more than any other pure-play AI vendor.

The drawbacks are cost and focus. Pricing is quote-only and suited to health systems, and its core strength is acute triage rather than screening or report writing.

Pick it if you run a hospital emergency or inpatient imaging service. Skip it if your main need is breast screening or report drafting.

Harrison.ai, maker of the Annalise products, takes a comprehensive approach. Instead of one algorithm per disease, its chest X-ray model checks for 124 findings at once in markets with CE marking, and its head CT model covers many more. That matches how radiologists actually read.

The evidence is solid. A 2021 multi-reader study in The Lancet Digital Health found the chest X-ray model alone was more accurate than unassisted radiologists for 117 of 124 findings, and radiologists improved when they used it. In the US the portfolio is narrower: nine 510(k) clearances covering 13 findings. The latest, in March 2026, was a Breakthrough Device clearance for spotting early stroke damage on routine head CT, which the company says reached up to 89.2% sensitivity.

It is used at more than 1,000 sites, including over 40 NHS trusts and all public emergency departments in Hong Kong.

Pick it if you want broad X-ray and head CT support, especially in the UK, Europe or Australia. Skip it if you need a wide US-cleared menu today; Aidoc covers more.

Lunit is the best choice for a breast imaging service that wants more than a cancer detector. Its INSIGHT MMG and DBT products flag suspicious areas on 2D and 3D mammograms, and the Volpara acquisition added breast density measurement, risk prediction and image quality tools. In April 2026 the FDA cleared version 1.2 of its 3D mammography AI, which compares current and prior exams and lets sites choose operating thresholds.

The evidence is among the best in radiology AI. In the prospective ScreenTrustCAD study in Stockholm, published in The Lancet Digital Health, AI plus one radiologist found cancers at least as well as two radiologists across 55,581 screens. The hospital then used Lunit to replace one human reader. Lunit also sells chest X-ray AI outside screening.

Lunit says it runs at more than 330 US sites and over 1 million screens a year, a vendor figure.

Pick it if you run a mammography programme and want detection plus density and risk. Skip it if your priority is emergency CT triage.

Transpara has the strongest evidence of any radiology AI product on this list. The Swedish MASAI study randomised more than 105,000 women to AI-supported screening or standard double reading. Final results, published in The Lancet in early 2026, showed the AI arm found 29% more cancers at screening, cut radiologist reading workload by 44% and had 12% fewer interval cancers (cancers found between screens), with 27% fewer aggressive ones. That is the kind of outcome data most AI tools lack.

Transpara was the first AI for screening mammograms to get FDA 510(k) clearance, in 2018, and now covers 2D and 3D mammography. It is also CE-marked and TGA-approved.

Its scope is narrower than Lunit's: it focuses on detection and triage rather than density, risk or quality. MASAI also used European double reading, which differs from typical US single reading, so the workload gains may not translate directly.

Pick it if you run a screening programme and want the best-proven detection AI. Skip it if you need a broader breast platform or non-breast imaging.

Qure.ai is the strongest option for chest X-ray and TB screening, particularly in health systems with few radiologists. Its qXR software reads chest X-rays and is widely used in tuberculosis screening programmes, where the World Health Organization has endorsed computer-aided detection since 2021. Its qER software triages head CT scans for bleeds, fractures and other emergencies.

In the US, its portfolio grew in February 2026 when the FDA cleared qXR-Detect with six indications in one submission, which the company says completes its chest X-ray suite. It also has clearances for head CT triage (qER), head CT quantification (qER-Quant) and breathing tube placement (qXR-BT).

Qure.ai has a large body of published studies, many from TB programmes. Its US hospital footprint and PACS integrations are smaller than Aidoc's, and pricing is quote-only.

Pick it if you need chest X-ray or TB screening AI at scale, or you work in a lower-resource setting. Skip it if you want a broad US platform covering body CT.

DeepHealth is RadNet's AI arm, and that ownership is its main strength: its tools are built and tested inside one of the largest outpatient imaging networks in the US. It has 32 entries on the FDA AI device list, sixth among all vendors.

Its Breast Suite is the core product. It covers 3D mammography detection, breast density, risk, a second-review safety check, and, after June 2026 clearances, breast arterial calcification (a possible early sign of heart disease) and automatic comparison with prior exams. In July 2026 the FDA cleared DeepHealth Breast Ultrasound, which detects lesions and helps with BI-RADS reporting. RadNet also offers patients an out-of-pocket AI add-on through its Enhanced Breast Cancer Detection programme.

Most of its outcome data comes from RadNet's own centres, which we treat as vendor evidence until it is independently replicated.

Pick it if you run outpatient imaging centres focused on breast screening. Skip it if you need hospital acute triage.

Microsoft's radiology offer is less about one algorithm and more about the reporting desk. PowerScribe One is the dictation and reporting software used in many US radiology departments. Dragon Copilot for radiologists, announced in late 2025, adds generative AI inside it: summaries of prior reports, answers from trusted sources and, in research-grade form, draft report content from image analysis.

The Precision Imaging Network is the other half. It lets hospitals license and run third-party FDA-cleared models, such as triage or detection tools, through Microsoft's platform instead of connecting each vendor separately. Customers such as St. Luke's and the University of Rochester use the combination.

We score it lower on evidence and regulation because Microsoft's own generative features are not FDA-cleared, and we found no peer-reviewed study of Dragon Copilot for radiology. Its value is workflow and governance.

Pick it if you already report in PowerScribe and want AI with minimal new integration. Skip it if you need a proven detection product; buy that separately.

Rad AI is the leader in generative AI for radiology reports. Radiologists dictate their findings and Rad AI drafts the impression, the summary section at the end of the report, in their own style. It can also insert guideline text and works with existing speech recognition and templates. Its Continuity product tracks incidental findings, such as a small lung nodule, across more than 50 categories and helps make sure patients get follow-up.

Rad AI says it serves over 40% of US health systems and 9 of the 10 largest US radiology practices. It reports time savings, for example a 20% drop in median report time at ARA Health Specialists. These are vendor figures; we found little independent peer-reviewed evidence on accuracy or patient outcomes.

Rad AI does not detect disease on images, and its tools are not FDA-cleared devices, so it complements rather than replaces detection AI.

Pick it if your radiologists are overloaded with dictation and follow-up tracking. Skip it if you need image-based detection or triage.

Gleamer's BoneView does one job well: it marks likely fractures on X-rays. That matters because missed fractures are one of the most common errors in emergency departments, and X-rays are often read first by non-radiologists.

The FDA cleared BoneView in March 2022, and later extended clearance to children over two, which Gleamer says made it the first fracture AI cleared for both adults and children. A US multi-reader study led by Boston University found it raised readers' sensitivity and specificity and shortened reading time across the limbs, ribs and spine. Gleamer reports 91% sensitivity for pediatric fractures, a vendor figure.

Its limits are scope and platform. It is mainly a musculoskeletal X-ray tool, so most hospitals will run it alongside a broader platform or through a marketplace.

Pick it if you run an emergency department or urgent care network with high X-ray volumes. Skip it if you want one vendor for all imaging AI.