AI in Digital Pathology: What the Research Actually Shows
Software reading digitized slides, from counting and grading through to estimating how a cancer will behave.
Pathology went digital later than radiology, so its AI research is younger and moving faster.
The early work targets the tedious parts: counting cells, measuring what share of tissue stains a certain way, grading. Those are jobs where two good pathologists genuinely disagree, so consistency is a real improvement rather than a marketing line.
The newer and more interesting work estimates prognosis from the image itself, which is a different kind of claim altogether. Watch the concordance studies in particular: they tell you how often software and pathologist agree, and more usefully, what kind of case makes them disagree.
What Kind of Evidence This Is
Not every study carries the same weight. This is the mix behind the list below.
What to Measure in Your Own Setting
A published result is somebody else's hospital. These are the numbers worth tracking in yours.
- Agreement with your own pathologists on your own slides, before anything else.
- Scanner and stain dependence. Pathology AI is unusually sensitive to how the slide was prepared and imaged.
- Turnaround time per case, which is where the operational return sits.
- What happens to a case the software flags as uncertain, and who owns that decision.
Who Sells This, and What the FDA Has Cleared
The research above says whether the idea works. These are the two questions a buyer asks next.
Companies listed here doing this work
- Aidoc 30 FDA clearances on record Radiology triage AI that flags suspected acute findings such as intracranial hemorrhage and pulmonary embolism to move them up the read list. One of the larger FDA-cleared portfolios in imaging triage.
- Viz.ai 8 FDA clearances on record Care coordination built on imaging AI: detects suspected large vessel occlusion stroke and alerts the intervention team directly, compressing time-to-treatment rather than only flagging the scan.
- PathAI AI for digital pathology, applied to both diagnostic support and pharmaceutical research workflows where consistent slide interpretation is the bottleneck.
- Lunit 6 FDA clearances on record Imaging AI concentrated in mammography screening and chest radiography, with a significant deployment base in national screening programs outside the United States.
- Harrison.ai Radiology 8 FDA clearances on record Radiology AI formerly marketed as Annalise.ai, now operating under the Harrison.ai Radiology name across chest X-ray and CT decision support.
- RapidAI 14 FDA clearances on record Neurovascular imaging analysis for stroke and aneurysm workflows, widely embedded in comprehensive stroke center protocols.
- GE HealthCare 9 FDA clearances on record Public healthcare technology company spanning imaging, ultrasound, monitoring, diagnostics, and AI-enabled software used across hospital workflows.
- Siemens Healthineers 12 FDA clearances on record Public medical technology company with imaging, diagnostics, cancer-care, and digital businesses that use AI across equipment and clinical workflow.
- Philips Public health technology company using AI across diagnostic imaging, ultrasound, monitoring, informatics, and image-guided therapy.
- DeepHealth 10 FDA clearances on record RadNet subsidiary combining radiology informatics, workflow, and clinical AI across breast, chest, neuro, prostate, thyroid, and other imaging work.
- Avicenna.AI 7 FDA clearances on record CT software that flags urgent and incidental findings, sold as the CINA product family. It covers brain bleeds, large vessel occlusion, pulmonary embolism, aortic dissection and spine fractures, and is used by radiology departments and teleradiology providers.
- ScreenPoint Medical 7 FDA clearances on record Transpara reads 2D and 3D mammograms to help a radiologist find breast cancer, rate breast density, and compare a scan against a woman's earlier images. Screening programs are its main buyer.
- Brainomix 6 FDA clearances on record Brainomix 360 reads brain scans to support decisions in acute stroke care, and e-Lung measures features on lung CT scans. Hospitals use the stroke product in the treatment window, and drug companies use the lung product as a trial measurement.
- Qure.ai 4 FDA clearances on record Qure.ai reads chest X-rays, CT scans and other images to flag findings and move urgent cases up the list. The company says it holds 26 FDA-cleared indications across nine products, and it sells to hospitals, screening programs and public health projects in the United States, India and Africa.
- Therapixel 5 FDA clearances on record MammoScreen reads a mammogram and marks the places a radiologist should look again for breast cancer. Therapixel is a French software company, and breast screening programs and radiology groups are its buyers.
- Koios Medical 4 FDA clearances on record Koios DS reads ultrasound images of the breast and the thyroid and gives the radiologist a second opinion on whether a lump looks suspicious. The company says the software is FDA cleared, CE marked and a billable procedure.
- JLK 4 FDA clearances on record JLK sells MEDIHUB, a family of AI tools that read brain scans and other medical images, with stroke care as the main focus. The company is based in South Korea and its shares trade on the KOSDAQ market.
- AZmed 5 FDA clearances on record Rayvolve reads X-rays and marks fractures and chest findings for the radiologist. AZmed is based in Paris and sells to hospital radiology departments in Europe and the United States, and the company says Rayvolve now covers eight FDA-cleared findings.
- BrightHeart 3 FDA clearances on record The B-Right platform watches a prenatal ultrasound as it happens and helps the sonographer check that the baby's heart views are complete and read correctly. BrightHeart is based in Paris, and its two products, B-Right Views and B-Right Screen, are both FDA cleared and CE marked.
- Sonio 2 FDA clearances on record Sonio follows a fetal ultrasound as it happens, checks that the required views were actually captured, flags findings that look abnormal, and writes the report. The company says it has five FDA clearances in under three years, and Samsung now owns it.
- Odin Vision 3 FDA clearances on record CADDIE watches a colonoscopy as it happens and points out polyps the doctor should look at again, and CADU does the same job for a gastroscopy. Odin Vision is a London company, and Olympus bought it in 2023, so the software now sits beside Olympus endoscopy equipment.
- Hologic 6 FDA clearances on record Hologic makes breast imaging, surgical and laboratory products for women's health, and its Genius AI Detection software reads 3D mammograms and marks the spots a radiologist should check for cancer. Blackstone and TPG took the company private in April 2026, so its shares no longer trade.
- Digital Diagnostics 2 FDA clearances on record LumineticsCore looks at a photo of the back of the eye and decides on its own whether a person with diabetes has diabetic retinopathy, without an eye doctor reading the image. Primary care clinics are the main buyer, and the company says the result comes back in about 30 seconds.
- Nanox.AI 6 FDA clearances on record Reads routine CT scans a patient already had and flags signs of heart, liver, and bone conditions that were not the reason for the scan, so they can be caught earlier. Formerly Zebra Medical Vision.
Device types the FDA has cleared for this
- Digital Pathology Assist Software 2 cleared products Helps a pathologist work through a digitized slide, by finding, counting or measuring what would otherwise be counted by eye.
- AI Breast Cancer Prognosis From Pathology Images 2 cleared products Estimates how a breast cancer is likely to behave from the slide image, to support treatment decisions.
- AI Cervical Cytology Slide Imaging 1 cleared products Scans cervical screening slides and uses AI to pick out the areas a human should review.
The Studies
Every entry links to the source record. Summaries are the study's own abstract, shortened but not reworded.
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Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology.
In the past decade, advances in precision oncology have resulted in an increased demand for predictive assays that enable the selection and stratification of patients for treatment. The enormous divergence of signalling and transcriptional networks mediating the crosstalk between cancer, stromal and immune cells complicates the development of functionally relevant biomarkers based on a single gene or protein.
PMID 31399699 ... doi:10.1038/s41571-019-0252-y
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Digital pathology and artificial intelligence.
In modern clinical practice, digital pathology has a crucial role and is increasingly a technological requirement in the scientific laboratory environment. The advent of whole-slide imaging, availability of faster networks, and cheaper storage solutions has made it easier for pathologists to manage digital slide images and share them for clinical use.
PMID 31044723 ... doi:10.1016/s1470-2045(19)30154-8
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Image analysis and machine learning in digital pathology: Challenges and opportunities.
With the rise in whole slide scanner technology, large numbers of tissue slides are being scanned and represented and archived digitally. While digital pathology has substantial implications for telepathology, second opinions, and education there are also huge research opportunities in image computing with this new source of "big data".
PMID 27423409 ... doi:10.1016/j.media.2016.06.037
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Deep learning in histopathology: the path to the clinic.
Machine learning techniques have great potential to improve medical diagnostics, offering ways to improve accuracy, reproducibility and speed, and to ease workloads for clinicians. In the field of histopathology, deep learning algorithms have been developed that perform similarly to trained pathologists for tasks such as tumor detection and grading.
PMID 33990804 ... doi:10.1038/s41591-021-01343-4
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The 2019 International Society of Urological Pathology (ISUP) Consensus Conference on Grading of Prostatic Carcinoma.
Five years after the last prostatic carcinoma grading consensus conference of the International Society of Urological Pathology (ISUP), accrual of new data and modification of clinical practice require an update of current pathologic grading guidelines. This manuscript summarizes the proceedings of the ISUP consensus meeting for grading of prostatic carcinoma held in September 2019, in Nice, France.
PMID 32459716 ... doi:10.1097/pas.0000000000001497
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AI-based pathology predicts origins for cancers of unknown primary.
Cancer of unknown primary (CUP) origin is an enigmatic group of diagnoses in which the primary anatomical site of tumour origin cannot be determined1,2. This poses a considerable challenge, as modern therapeutics are predominantly specific to the primary tumour3. Recent research has focused on using genomics and transcriptomics to identify the origin of a tumour4-9.
PMID 33953404 ... doi:10.1038/s41586-021-03512-4
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Deep learning in cancer pathology: a new generation of clinical biomarkers.
Clinical workflows in oncology rely on predictive and prognostic molecular biomarkers. However, the growing number of these complex biomarkers tends to increase the cost and time for decision-making in routine daily oncology practice; furthermore, biomarkers often require tumour tissue on top of routine diagnostic material.
PMID 33204028 ... doi:10.1038/s41416-020-01122-x
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A visual-language foundation model for pathology image analysis using medical Twitter.
The lack of annotated publicly available medical images is a major barrier for computational research and education innovations. At the same time, many de-identified images and much knowledge are shared by clinicians on public forums such as medical Twitter. Here we harness these crowd platforms to curate OpenPath, a large dataset of 208,414 pathology images paired with natural language descriptions.
PMID 37592105 ... doi:10.1038/s41591-023-02504-3
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A deep learning model to predict RNA-Seq expression of tumours from whole slide images.
Deep learning methods for digital pathology analysis are an effective way to address multiple clinical questions, from diagnosis to prediction of treatment outcomes. These methods have also been used to predict gene mutations from pathology images, but no comprehensive evaluation of their potential for extracting molecular features from histology slides has yet been performed.
PMID 32747659 ... doi:10.1038/s41467-020-17678-4
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A pathology foundation model for cancer diagnosis and prognosis prediction.
Histopathology image evaluation is indispensable for cancer diagnoses and subtype classification. Standard artificial intelligence methods for histopathology image analyses have focused on optimizing specialized models for each diagnostic task1,2.
PMID 39232164 ... doi:10.1038/s41586-024-07894-z
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Digital pathology and artificial intelligence in translational medicine and clinical practice.
Traditional pathology approaches have played an integral role in the delivery of diagnosis, semi-quantitative or qualitative assessment of protein expression, and classification of disease. Technological advances and the increased focus on precision medicine have recently paved the way for the development of digital pathology-based approaches for quantitative pathologic assessments, namely whole slide imaging and art...
PMID 34611303 ... doi:10.1038/s41379-021-00919-2
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Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis.
Cancer diagnosis, prognosis, mymargin and therapeutic response predictions are based on morphological information from histology slides and molecular profiles from genomic data. However, most deep learning-based objective outcome prediction and grading paradigms are based on histology or genomics alone and do not make use of the complementary information in an intuitive manner.
PMID 32881682 ... doi:10.1109/tmi.2020.3021387
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Emerging role of deep learning-based artificial intelligence in tumor pathology.
The development of digital pathology and progression of state-of-the-art algorithms for computer vision have led to increasing interest in the use of artificial intelligence (AI), especially deep learning (DL)-based AI, in tumor pathology.
PMID 32277744 ... doi:10.1002/cac2.12012
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Deep neural network models for computational histopathology: A survey.
Histopathological images contain rich phenotypic information that can be used to monitor underlying mechanisms contributing to disease progression and patient survival outcomes. Recently, deep learning has become the mainstream methodological choice for analyzing and interpreting histology images.
PMID 33049577 ... doi:10.1016/j.media.2020.101813
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Artificial intelligence as the next step towards precision pathology.
Pathology is the cornerstone of cancer care. The need for accuracy in histopathologic diagnosis of cancer is increasing as personalized cancer therapy requires accurate biomarker assessment. The appearance of digital image analysis holds promise to improve both the volume and precision of histomorphological evaluation.
PMID 32128929 ... doi:10.1111/joim.13030
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Artificial intelligence and computational pathology.
Data processing and learning has become a spearhead for the advancement of medicine, with pathology and laboratory medicine has no exception. The incorporation of scientific research through clinical informatics, including genomics, proteomics, bioinformatics, and biostatistics, into clinical practice unlocks innovative approaches for patient care.
PMID 33454724 ... doi:10.1038/s41374-020-00514-0
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Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images.
Automated nuclear detection is a critical step for a number of computer assisted pathology related image analysis algorithms such as for automated grading of breast cancer tissue specimens. The Nottingham Histologic Score system is highly correlated with the shape and appearance of breast cancer nuclei in histopathological images.
PMID 26208307 ... doi:10.1109/tmi.2015.2458702
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An artificial intelligence algorithm for prostate cancer diagnosis in whole slide images of core needle biopsies: a blinded clinical validation and deployment study.
BackgroundThere is high demand to develop computer-assisted diagnostic tools to evaluate prostate core needle biopsies (CNBs), but little clinical validation and a lack of clinical deployment of such tools.
PMID 33328045 ... doi:10.1016/s2589-7500(20)30159-x
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Pathology Image Analysis Using Segmentation Deep Learning Algorithms.
With the rapid development of image scanning techniques and visualization software, whole slide imaging (WSI) is becoming a routine diagnostic method. Accelerating clinical diagnosis from pathology images and automating image analysis efficiently and accurately remain significant challenges.
PMID 31199919 ... doi:10.1016/j.ajpath.2019.05.007
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The 2019 Genitourinary Pathology Society (GUPS) White Paper on Contemporary Grading of Prostate Cancer.
Context.—Controversies and uncertainty persist in prostate cancer grading.Objective.—To update grading recommendations.Data sources.—Critical review of the literature along with pathology and clinician surveys.Conclusions.—Percent Gleason pattern 4 (%GP4) is as follows: (1) report %GP4 in needle biopsy with Grade Groups (GrGp) 2 and 3, and in needle biopsy on other parts (jars) of lower grade in cases with at least 1...
PMID 32589068 ... doi:10.5858/arpa.2020-0015-ra
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Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images.
Pathological evaluation of tumor tissue is pivotal for diagnosis in cancer patients and automated image analysis approaches have great potential to increase precision of diagnosis and help reduce human error.
PMID 29292031 ... doi:10.1016/j.ebiom.2017.12.026
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Weakly Supervised Deep Learning for Whole Slide Lung Cancer Image Analysis.
Histopathology image analysis serves as the gold standard for cancer diagnosis. Efficient and precise diagnosis is quite critical for the subsequent therapeutic treatment of patients. So far, computer-aided diagnosis has not been widely applied in pathological field yet as currently well-addressed tasks are only the tip of the iceberg. Whole slide image (WSI) classification is a quite challenging problem.
PMID 31484154 ... doi:10.1109/tcyb.2019.2935141
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Deep Learning for Classification of Colorectal Polyps on Whole-slide Images.
ContextHistopathological characterization of colorectal polyps is critical for determining the risk of colorectal cancer and future rates of surveillance for patients. However, this characterization is a challenging task and suffers from significant inter- and intra-observer variability.AimsWe built an automatic image analysis method that can accurately classify different types of colorectal polyps on whole-slide ima...
PMID 28828201 ... doi:10.4103/jpi.jpi_34_17
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Artificial intelligence in diagnostic pathology.
Digital pathology (DP) is being increasingly employed in cancer diagnostics, providing additional tools for faster, higher-quality, accurate diagnosis. The practice of diagnostic pathology has gone through a staggering transformation wherein new tools such as digital imaging, advanced artificial intelligence (AI) algorithms, and computer-aided diagnostic techniques are being used for assisting, augmenting and empower...
PMID 37784122 ... doi:10.1186/s13000-023-01375-z
Study records come from Europe PMC, which indexes PubMed, MEDLINE, PMC and preprint servers. Titles, journals, years, identifiers, citation counts and abstracts are reproduced from the source record and are not rewritten here. Listing a study is not an endorsement of its conclusion, and citation count measures attention rather than quality. This page is a starting point for your own reading, not clinical guidance.