AI & Computation
Software that reads scans and slides, predicts outcomes, designs drugs, and matches patients to trials.
FDA-cleared digital pathology risk tools (ArteraAI), radiology triage and screening models, pathology and radiology foundation models, multimodal patient-level models, AI-driven target discovery and ADC design, and LLM-based trial matching and tumour-board support.
Software that draws organs and tumours on scans automatically, saving hours per patient and making daily plan adaptation practical.
AI compute platforms are the GPUs, model libraries, and cloud services that pathology, radiology, and drug-design AI run on.
Software that reads scans alongside radiologists, catching cancers earlier and predicting who is at risk.
Software, increasingly LLM-based, that reads a patient's record and finds trials or guideline options they qualify for.
Using machine learning to pick targets, design molecules and antibodies, and predict which ADC will work.
Aidoc CARE is a single foundation model behind many FDA-cleared triage alerts in emergency radiology.
Predicts the 3D shape of proteins together with DNA, RNA, small molecules and antibodies, the starting point for much modern drug design.
Reads a million letters of DNA at once and predicts how a mutation changes gene regulation, splicing and chromatin.
Scored all 71 million possible single-letter protein changes in humans as likely harmful or benign.
Atlas is a pathology foundation model trained on 1.2 million slides from two of the world's largest hospitals.
Predicts the many shapes a protein moves between, not just one, thousands of times faster than simulation.
Open-source structure models that match AlphaFold 3, with Boltz-2 also predicting how strongly a drug binds.
The public systems that count every cancer diagnosis and death in a country, which tell us whether incidence and survival are improving.
Curated databases that say what each mutation means for treatment, and the expert meetings that use them to decide on therapy.
Scheduling and tracking software that makes sure each patient's cells come back to that patient, on time.
Turns a cell's gene expression into a sentence so a normal language model can reason about it; a 27-billion-parameter version proposed a cancer immunotherapy idea that was confirmed in the lab.
CellFM is an 800-million-parameter single-cell model trained on 100 million human cells.
Structure and antibody-design models from Chai Discovery, with Chai-2 reporting high zero-shot antibody hit rates.
A pathology model trained across 19 cancer types that predicts survival and mutations from slides.
Software that turns raw sequencer output into a report of which mutations matter and which drugs they point to.
Clinical trial software is the set of systems that collect trial data, randomise patients, and keep every form auditable.
Companies that run clinical trials for sponsors: sites, monitoring, data, and regulatory filing.
A model pretrained on 148,000 CT scans to segment organs and triage findings.
Running parts of a trial at home or locally, with telehealth, home nursing, and remote monitoring, so patients far from big centres can take part.
Magnified skin imaging and whole-body photo mapping, increasingly read by algorithms, to find melanoma early and avoid unnecessary biopsies.
Scanning microscope slides and letting software measure things a pathologist cannot see, including predictions of who will benefit from a treatment.
Using a model of what would have happened to a patient on standard treatment, so fewer people have to be randomised to it.
Patients report symptoms weekly through an app or web form, and nurses respond to alerts. Randomised trials showed this simple system improved quality of life, cut emergency visits and, in one trial, extended survival by five months.
Apps and sensors that let patients report symptoms between visits, which in trials improved survival and cut emergency visits.
Models that predict how DNA sequence controls gene activity, used to interpret non-coding cancer mutations.
ESM3 is a generative protein model that designed a working fluorescent protein far from any natural sequence.
A DNA language model trained on 9.3 trillion bases that can flag cancer-causing BRCA1 variants without being told about them.
Training AI models across many hospitals without moving patient data, so the model learns from everyone while the data stay put.
A model trained on millions of hospital records that forecasts a patient's next diagnoses.
GEARS is a graph model predicting the effect of gene knockouts; the perturbation benchmarks around it showed how hard the problem is.
The first widely used transformer trained on millions of single cells, able to predict which genes matter in a disease.
Uses text embeddings of gene descriptions from a general LLM to represent cells, and performs surprisingly well.
Cloud systems where hospitals and researchers store and analyse genomic data securely at petabyte scale.
An open 1.1-billion-parameter pathology model from a French startup, among the strongest on public benchmarks.
Hibou is a family of open pathology foundation models under a permissive licence.
Google's medical versions of its Gemini models, able to reason over text, images, and long records.
Adaptations of Meta's Segment Anything model that outline tumours and organs on any scan with a click.
Merlin is a model trained on 15,000 CT scans with their reports that can find and describe hundreds of findings.
Midnight is a pathology model that matched the leaders while training on far fewer slides.
Reads a mammogram to estimate five-year breast cancer risk, consistently across races and devices.
A model that reads slides and clinical text together to predict who will respond to immunotherapy.
Nicheformer is a model trained on both dissociated and spatial data so it learns how a cell's neighbourhood shapes it.
DNA language models trained on thousands of genomes for variant and regulatory prediction.
BioNeMo is the software stack many biology foundation models are trained and served with.
Databases built from millions of real patient records, used to see how treatments work outside trials and to run studies without new trials.
The ordering and record systems oncologists use every day, including built-in treatment pathways that steer drug choice.
Very large AI models trained on millions of slides or scans that can be adapted to almost any diagnostic question.
A model trained on billions of cell microscopy images to read what a drug or gene knockout does to a cell.
Owkin's open pathology models trained on TCGA and its federated hospital network.
PathAI's compact pathology foundation model designed to run across many tasks and resolutions.
An open pathology model trained on 1.3 billion image tiles from a US health system, modelling whole slides at gigapixel scale.
An open generalist model that answers questions about 2D and 3D scans.
The software that calculates exactly how radiation beams should be shaped and checks the machine delivered it.
The tools that design entirely new proteins to bind a chosen target, now used for cancer binders and antibodies.
scFoundation is a 100-million-parameter model trained on 50 million cells, from China's BioMap.
A GPT-style model for single-cell data that predicts cell types, perturbation responses, and gene networks.
Choosing treatment from a map of where each cell type sits in the tumour, not just from a list of its mutations.
Predicts how cells will respond to a drug or gene knockout, trained on over 100 million perturbed cells.
Predicts a person's six-year lung cancer risk from one low-dose CT, even when no nodule is visible.
Video visits, remote monitoring and home delivery of some cancer treatments expanded massively during COVID-19 and have stayed; they reduce travel burden, especially for rural patients, without evidence of worse outcomes.
Models trained on Tempus's paired genomic, pathology, imaging and outcome data to predict response and prognosis.
TITAN is a model that summarises a whole slide, not just tiles, and can write a draft pathology report.
CZI's open cross-species cell models and a reasoning model trained on them.
Two open academic pathology models: UNI reads tissue images, CONCH links images with pathology text.
Universal Cell Embedding maps any cell from any species into one shared space without retraining.
A pathology foundation model trained on millions of slides that can detect cancer and predict biomarkers from an ordinary H&E slide.
The scanners that turn glass slides into gigapixel images, and the software that stores and serves them, without which pathology AI cannot run.
AI can take over one reader's work in double-reading screening programmes while finding more cancers. Whether the extra cancers found are ones that would have harmed women, and whether interval cancers fall, is the question the trial's primary endpoint will answer.
The shape of nearly every protein is now available to any researcher in seconds instead of years, which shortens the path from a cancer target to a designed molecule. It does not by itself produce drugs: binding pockets, dynamics and cellular context still need experiment.