AI in Cancer Drug Development and Treatment
On March 13, 2026, new AI systems were developed to reduce pathologist workload in cancer diagnosis, accelerate drug discovery by predicting chemical reaction outcomes, and detect early cancer by analyzing micronuclei for chromosomal defects. As of March 13, 2026, AI is significantly advancing cancer drug development and treatment, with systems like MAGIC identifying chromosomal abnormalities and Google AI outperforming human radiologists in breast cancer detection. Recursion Pharmaceuticals used AI to advance a cancer drug candidate into clinical testing in 18 months, a 57% reduction from the typical 42-month timeline. AI is also being used to optimize drug delivery, interpret genomic mutations for precision oncology, and predict tumor spread with nearly 80% accuracy in colon cancer.
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100 updatesA new AI system has been developed to reduce the workload of pathologists in cancer diagnosis while maintaining high accuracy. Additionally, a machine learning system is accelerating drug discovery by accurately predicting chemical reaction outcomes, and the MAGIC AI system is being used for early cancer detection by analyzing micronuclei for chromosomal defects.
Recursion Pharmaceuticals utilized its AI platform to advance a cancer drug candidate into clinical testing in 18 months, significantly shortening the typical 42-month timeline. Tools like DeepMind's AlphaFold are also aiding in the search for viable compounds. This acceleration is crucial for faster delivery of new cancer treatments to patients.
Servier is forming strategic partnerships with companies like Insilico Medicine and Iktos to accelerate therapeutic innovation and identify new oncology drug candidates using AI. A review also highlights how AI is optimizing drug delivery and pharmacokinetic modeling in polymer-based cancer therapies, enabling precise drug release and individualized treatment.
A review in the Journal of Hematology & Oncology explores how generative AI can assist oncologists in precision oncology by interpreting genomic mutations and identifying suitable clinical trials. The review highlights AI models' strong performance in clinical trial matching, with one model, TrialGPT, achieving 87.3% agreement with expert assessments.
via news-medical.net
A Google AI system has demonstrated superior performance in detecting breast cancer compared to human radiologists in an NHS study, identifying more invasive cancers and 25% of interval cancers. Separately, a new AI system called MAGIC can automatically identify cells with early signs of chromosomal abnormalities linked to cancer development, speeding up research. Additionally, a medically trained AI system achieved 96% accuracy in identifying eligible patients for a rare disease clinical trial by analyzing EHR data.
Scientists have developed an AI system, MangroveGS, that analyzes gene-expression signatures to predict tumor spread with nearly 80% accuracy in colon cancer, and this system also identified gene signatures that predict metastatic potential in other cancers. Separately, agentic AI tools are being utilized to autonomously accelerate drug development processes, from candidate design to reducing clinical trial bottlenecks, aiming to improve research accuracy and reduce failure rates.
A new AI imaging biomarker, the Quantitative Vessel Tortuosity (QVT™) score, can predict patient outcomes and detect early signs of treatment response in non-small cell lung cancer. Additionally, Medidata has unveiled a suite of AI-based virtual companions designed to accelerate drug development and improve patient care in the life sciences industry.
via Cleveland Clinic Newsroom·Dassault Systèmes·holyrood.com
New research indicates that AI in breast cancer screening can detect more invasive cancers and overall cases, with fewer false positives and recalls compared to human readers, and reduced scan reading time. Additionally, a new AI-based technology called DocTr has demonstrated superior performance in matching doctors and clinical trial sites to studies.
A UK study published in Nature Cancer found that AI can increase breast cancer detection by 10.4% and potentially reduce healthcare worker workload by over 30%, with notification times reduced from 14 days to 3 days. Google's AI system also demonstrated improved breast cancer detection accuracy and reduced radiologist workload in UK trials. These advancements aim to assist radiologists in earlier and more accurate cancer detection.
A study presented at the European Congress of Radiology indicates that AI assessment of screening mammograms can predict the likelihood of breast cancer detection in subsequent screenings. Separately, artificial intelligence is being used to unravel complex cancer mechanisms, leading to new targeted therapies and personalized treatment plans by analyzing vast amounts of cancer data to uncover hidden patterns.
via diagnosticimaging.com·vwcnews.com·pathologyinpractice.com
PathAI has received U.S. FDA Breakthrough Device Designation for PathAssist Derm, an AI tool designed to analyze skin lesions. Additionally, Medidata is deploying its AI Study Build technology to accelerate oncology clinical trials for The Menarini Group, aiming to reduce trial startup times.
Scientists have developed an AI system called MangroveGS that analyzes gene-expression signatures to predict tumor metastasis with nearly 80% accuracy in colon cancer. Separately, an AI-powered "electronic nose" developed at Linköping University can detect early signs of ovarian cancer in blood samples with 97% accuracy by analyzing volatile substances. USC researchers also created an AI algorithm that automates the detection of rare cancer cells in blood samples in approximately 10 minutes.
via SciTechDaily·Healthcare in Europe·USC Viterbi School of Engineering
Xlue Inc., a startup from Carnegie Mellon University, is using artificial intelligence to identify patients at high risk for lung, liver, and pancreatic cancers. Their AI tool, CATCH-FM, is trained on millions of patient medical records to detect early signals of cancer development. The predictive technology aims to allow doctors to recommend earlier screenings, improving the chances of successful treatment, and has shown a 50% accuracy rate for predicting cancer in patients with no prior history.
via jacksonville.com
Northeastern University has developed an AI tool that can diagnose acute myeloid leukemia (AML) and suggest treatments in as little as one night, potentially cutting down diagnosis-to-treatment time from weeks to a single day. Stanford Medicine has also created Nuclei.io, an AI-based tool designed to increase pathologists' speed, collaboration, and diagnostic accuracy. Additionally, Indian medtech firms are advancing AI diagnostic tools for conditions like brain damage, tuberculosis, and early breast cancer detection.
Indian medtech firms are developing AI diagnostic tools for conditions like brain damage, tuberculosis, and early breast cancer detection. Additionally, an AI-driven system from Cleveland Clinic and Dyania Health has demonstrated a 96.2% accuracy in identifying patients for rare disease clinical trials, significantly improving diversity in participant recruitment.
Researchers at the Arc Institute have developed Evo 2, a biological foundation model trained on 9 trillion DNA base pairs, capable of decoding DNA rules and generating new functional sequences. This AI model accurately predicts the effects of complex mutations and shows promise in analyzing cancer-related genes such as BRCA1. The development opens possibilities for programmable biology and enhanced cancer research.
Researchers at MIT and Microsoft have developed an AI model to design molecular sensors for early cancer detection, potentially usable in a urine test. New research from the University of Warwick warns that many AI pathology tools may rely on 'shortcut learning' rather than genuine biological understanding, raising concerns about reliability. Additionally, a Northeastern University AI tool can analyze patient samples to map genetic mutations and suggest treatments for acute myeloid leukemia (AML) in as little as one night.
Researchers in Taiwan have developed PanMETAI, an AI-powered platform that detects early-stage pancreatic cancer with up to 94% accuracy using metabolic fingerprints from blood samples. Published in Nature Communications, the tool combines AI with NMR metabolomics to identify subtle metabolic shifts indicative of the disease. This non-invasive method aims to improve the notoriously low survival rate of pancreatic cancer through earlier diagnosis and treatment.
Russian researchers have developed a neural network capable of detecting early-stage breast cancer from CT scans in minutes. The AI system, created by St. Petersburg Electrotechnical University and the Almazov National Medical Research Center, highlights potential cancer signs for physician review and developers claim it reduces clinical error probability by approximately 20%.
via ascopost.com
A new AI tool from Northeastern University can map AML genetic mutations to potentially reduce diagnosis-to-treatment time, while a study warns that some AI pathology models may use unreliable 'shortcut learning.' Separately, an AI blood test developed in Taiwan, PanMETAI, detects early-stage pancreatic cancer with over 90% accuracy.
Researchers at Fred Hutch Cancer Center are testing a collaborative AI research platform designed to accelerate cancer research and develop AI models for predicting cancer progression, treatment effectiveness, and resistance mechanisms. The platform uses de-identified clinical data from member institutions to train these models, aiming for faster diagnoses and more precise therapies, particularly for rare cancers, while safeguarding patient privacy.
Researchers have developed AI-designed molecular sensors for early cancer detection, with potential for at-home urine tests. The FDA granted breakthrough designation to PathAI's AI-powered dermatopathology solution, PathAssist Derm. However, new research suggests some AI pathology tools may rely on 'shortcut learning,' raising concerns about their reliability.
Researchers at MIT and Microsoft have developed AI-designed molecular sensors for early cancer detection, with potential for home use. PathAI received FDA breakthrough designation for its AI-powered dermatopathology solution, PathAssist Derm. Additionally, a new AI tool from Northeastern University can map AML genetic mutations and predict drug resistance, aiming to significantly reduce diagnosis-to-treatment time.
The FDA has granted pre-market approval to Claire, the first AI-imaging device in the U.S. for intraoperative breast cancer margin assessment during surgery. Separately, an Australian AI tool named BRAIx has shown higher accuracy than traditional factors in predicting breast cancer risk within four years using mammograms.
An Australian AI-based tool, BRAIx, can now predict a woman's breast cancer risk within four years using mammograms with higher accuracy than traditional factors. Separately, Vanderbilt Health and Bertis have formed a collaboration to advance cancer drug discovery using AI-driven proteomics and molecular AI initiatives.
via warwick.ac.uk
Australian research indicates an AI-based tool, BRAIx, can predict a woman's risk of developing breast cancer within the next four years using mammograms with higher accuracy than traditional factors. A world-first trial in Sweden has shown that AI can help doctors identify more breast cancer cases during routine screenings by analyzing mammograms and flagging abnormalities. However, new research suggests many AI cancer pathology tools may rely on 'shortcut learning' rather than genuine biological signals, raising concerns about their reliability.
PathAI received U.S. FDA Breakthrough Device Designation for PathAssist Derm, an AI tool designed to analyze skin lesions. LG CNS is expanding its AI applications in the pharmaceutical and digital health sectors, investing in CHA Biotech and developing an AI-based clinical trial design platform. These advancements aim to accelerate drug development and improve diagnostic workflows.
via Chosun Ilbo
Perimeter Medical Imaging AI's 'Claire' has become the first FDA-approved AI-enabled imaging device for breast cancer surgery. The device uses AI and wide-field OCT imaging for real-time evaluation of excised tumor margins. The pivotal trial demonstrated an 88.1% margin accuracy and a statistically significant reduction in patients with residual cancer post-surgery.
via AdvaMed
Vanderbilt Health and Bertis have launched a collaboration to advance cancer drug discovery by integrating proteomics and AI. A new AI tool from Northeastern University aims to drastically reduce acute myeloid leukemia (AML) diagnosis and treatment planning time from weeks to a single night by mapping genetic mutations. Additionally, research from the University of Warwick highlights concerns that AI pathology models may rely on 'shortcut learning,' potentially leading to unreliable predictions in patient care.
via Northeastern University·BioWorld·Vanderbilt University Medical Center
Northeastern University researcher Kiran Vanaja has developed a new AI tool designed to significantly reduce treatment determination time for acute myeloid leukemia (AML). The tool can diagnose AML, map genetic mutations, suggest potential drugs, and predict drug resistance, potentially cutting the time from diagnosis to treatment from weeks to a single night.
New research indicates an AI pipeline using large language models can accurately predict the future risk of advanced neoplasia in patients with colitis-associated low-grade dysplasia. Separately, research from the University of Warwick warns that many AI tools for cancer pathology may rely on 'shortcut learning' rather than genuine biological signals, potentially impacting diagnostic reliability.
PathAI announced its AI-powered pathology solution, PathAssist Derm, received U.S. FDA Breakthrough Device Designation for analyzing skin lesions. Separately, research highlights concerns that AI pathology tools may use 'shortcut learning' rather than genuine biological signals, potentially impacting reliability. AI is also transforming drug development, with AI-enabled discovery workflows projected to reduce early timelines by up to 40% and costs by 30%.
Generate:Biomedicines has completed a $425 million IPO on the Nasdaq to advance AI-powered drug development, with CEO Mike Nally emphasizing biology's role in unlocking AI's potential. Northeastern University has developed a patented AI tool that can diagnose acute myeloid leukemia, map its genetic mutations, and suggest treatments, potentially reducing diagnosis-to-treatment time from weeks to a single night.
via eurekalert.org
Vanderbilt Health and Bertis have launched a collaboration to advance AI, spatial biology, and translational cancer research for drug discovery. Northeastern University has developed a new AI tool that can diagnose acute myeloid leukemia, map its genetic mutations, and predict drug resistance, potentially reducing diagnosis-to-treatment time. Additionally, MD Anderson Cancer Center will host a discussion on the responsible implementation of AI innovations in oncology.
via nature.com
Researchers have developed AI-generated sensors using peptides and nanoparticles that can detect specific cancer types based on protease markers, potentially enabling simple at-home urine tests. Scientists are also using AI to design custom proteins that guide immune cells to target cancer, a promising form of immunotherapy. Additionally, an AI algorithm can now automate the detection of rare cancer cells in blood samples within approximately 10 minutes, significantly speeding up liquid biopsy processes.
Researchers have developed AI-generated sensors using peptides and nanoparticles for earlier cancer detection, potentially identifying specific cancer types based on protease markers. A new study warns that many AI cancer diagnostic tools may rely on 'shortcut learning' rather than genuine biological signals, raising concerns about their reliability for patient care.
Scientists have used AI to design custom proteins that guide immune cells to target cancer cells, showing promise in immunotherapy. A new AI system can recommend cancer treatments based on tumor genetics with over 90% accuracy compared to expert clinicians. However, research also warns that some AI cancer tools may rely on 'shortcut learning' rather than true biological signals, potentially impacting reliability.
Merck Sharp & Dohme (MSD) is using AI models TEDDY and KERMT to accelerate drug design, doubling their promising drug candidates with two new molecules entering clinical trials. Researchers at USC have developed an AI algorithm that automates the detection of rare cancer cells in blood samples via liquid biopsy, identifying cancer cells in approximately 10 minutes. UCLA researchers created an AI tool, AQuA, to detect errors in digital pathology images, achieving 99.8% accuracy.
Generate Biomedicines, an AI-driven drug developer, has raised $400 million through its U.S. initial public offering to advance its platform for protein-based therapeutics. Concurrently, Jeeva Clinical Trials is urging the life sciences industry to modernize infrastructure to fully leverage AI in drug development, emphasizing that unified systems are crucial for AI's effectiveness.
Artificial intelligence is being integrated into healthcare in Estonia for diagnostics in neurology, pathology, and ophthalmology. A review highlights AI's promise in improving oral cancer diagnosis, while Roche is leveraging AI and machine learning to accelerate drug discovery timelines and reduce costs.
Clearnote Health has launched its enhanced Avantect Pancreatic Cancer Test, an AI-powered blood test designed to detect pancreatic cancer in high-risk individuals. The test analyzes blood samples for cancer-related molecules, using AI models to calculate a patient's risk level. In high-risk patients, the Avantect test shows an 82.6% sensitivity and 97.5% specificity for cancer detection.
Researchers have developed AI models to design molecular sensors for early cancer detection, potentially detectable through urine tests. Additionally, AI is being used to design proteins that guide cancer-fighting immune cells, and AI assistance has been shown to improve the detection of cancers on digital breast tomosynthesis images. Biorce also secured $52.5 million in Series A funding to advance its AI technology for clinical trials.
UC San Diego researchers have developed a new AI tool that precisely maps the urethra on MRI scans to improve the safety and reduce urinary side effects of prostate cancer radiation therapy. The AI tool demonstrated performance comparable to or exceeding human experts, accurately identifying 81% of the true urethra compared to 34% by physicians in testing.
WuXi XDC and Earendil Labs have entered a strategic collaboration potentially valued at $885 million, combining WuXi XDC's antibody-drug conjugate (ADC) technology with Earendil's AI-driven antibody discovery platform. Earendil will license WuXi XDC's proprietary WuXiTecan-2 payload-linker technology to develop ADC candidates for cancer and autoimmune diseases. This partnership aims to accelerate the development of next-generation ADCs by leveraging Earendil's AI capabilities for antibody discovery.
Researchers are exploring AI-based tools to identify women at higher risk for breast cancer, potentially detecting cancers missed by standard mammograms by analyzing subtle imaging features. Additionally, AI models are being developed to design molecular sensors for early cancer detection through urine tests and to guide cancer-fighting immune cells to target cancer cells more effectively.
Northwell Health has developed an AI clinical tool named iNav that significantly accelerates the detection and treatment of pancreatic cancer. A study published in The Oncologist revealed that iNav can cut the time from biopsy to diagnosis in half, from 12 days to six days, and also reduced the wait time for an oncologist appointment and treatment initiation.
Researchers have developed an AI system called MAGIC to track genetic mishaps within living cells that may lead to cancer, combining microscopy, AI image analysis, and genomic sequencing. Additionally, AI models are being used to design molecular sensors for early cancer detection via at-home urine tests. Labcorp is also expanding its collaboration with PathAI to deploy an AI-powered digital pathology platform across its U.S. labs.
An international team of scientists has developed a new method using AI and 3D technology to improve the detection of cancer cells, particularly for cervical cancer. This AI-driven approach automates the analysis of cervical cell samples, offering a more precise and efficient alternative to traditional methods like the Pap smear test. The method promises to revolutionize cervical cancer diagnosis by accelerating the process and potentially leading to earlier life-saving treatment.
Researchers have developed an electronic nose utilizing machine learning and AI to detect early signs of ovarian cancer from blood samples, a method that could be adapted for various cancers. Additionally, an AI-based tool is being investigated at UMass Chan Medical School to identify women at higher risk for breast cancer by analyzing subtle imaging features missed by standard mammograms.
via Healthcare in Europe·UMass Chan Medical School·sfgate.com
Generative AI has demonstrated the ability to process vast medical datasets significantly faster than human research teams, potentially yielding stronger results. In Australia, AI is poised to offer productivity gains in pathology, including augmented diagnostics and faster turnaround times for complex testing. Furthermore, an AI-powered electronic nose has achieved 97% accuracy in detecting early signs of ovarian cancer from blood samples.
Rakovina Therapeutics Inc. has increased its financing to approximately CA$2.0 million to advance its AI-driven cancer drug programs. The company's collaboration with NanoPalm combines Rakovina's AI-enabled drug discovery with NanoPalm's delivery platform. Planned milestones for 2026 include joint venture funding and pursuing partnerships for antibody drug conjugate payloads.
iCAD's ProFound AI Suite, a new AI-powered mammogram technology, has demonstrated a 23% increase in cancer detection rates and a reduction in false positives. Radiologist Dr. Kenneth Meng stated that AI is revolutionizing mammography and early breast cancer detection. The AI tool analyzes mammograms to highlight suspicious areas and reassure radiologists about benign regions, with a study involving over 100,000 breast imaging exams.
via prnewswire.com
Caris has launched a proprietary AI Insights Signature that uses AI and machine learning to better understand patient responses to oral chemotherapy drugs for breast cancer. Researchers at IIT Indore have developed an AI system that analyzes medical images to improve early detection of breast and cervical cancer, identifying suspicious areas and reducing missed diagnoses. A study published in the Journal of the American College of Radiology indicates that AI assistance significantly increases cancer detection rates on screening digital breast tomosynthesis images, leading to a nearly 22% rise in detection.
Researchers have developed AI models to create molecular sensors for early cancer detection, potentially enabling at-home urine tests for lung, ovarian, and colon cancers. AI has also been utilized to design new molecules with potential antitumor activity, identifying compounds that show significant cytotoxic activity against tumor cells. Additionally, advanced AI systems are being developed to rapidly analyze medical images for improved early detection of breast and cervical cancer.
Epredia and Mindpeak have entered into a distribution agreement to offer Mindpeak's AI image recognition software to Epredia's digital pathology customers in the European Union, aiming to enhance precision and speed in diagnostic image review. Concurrently, a review highlights the integration of AI and machine learning with chemoinformatics to identify and validate natural products for treating cancer metastasis and chemoresistance.
Researchers have developed AI-generated sensors using peptides that can signal the presence of cancer-linked proteases, potentially leading to at-home urine tests for early cancer detection. Additionally, an AI-powered electronic nose can detect early signs of ovarian cancer in blood samples with 97% accuracy by analyzing volatile substances emitted by cancer cells.
AI assistance in digital breast tomosynthesis (DBT) has shown an increase in detecting invasive and lobular cancers, as well as smaller tumors. Researchers have also developed AI-generated sensors that can detect cancer-linked proteases, paving the way for early-stage detection through simple urine tests. Additionally, a new AI-based tool from MIT can rapidly annotate medical images, potentially accelerating clinical research.
Labcorp announced an expanded collaboration with PathAI to deploy the FDA-cleared AISight® Dx digital pathology platform across its national network of labs and hospital collaborations. This platform utilizes AI to support diagnostic processes, enhance case management, and improve slide review and collaboration. The expansion includes AI-driven clinical trial support.
via prnewswire.com
A real-world study found that AI tools, used with 3D mammography across four sites, increased cancer detection rates by nearly 22% among breast radiologists without raising false positives. Telefónica, Fundación Vithas, and Francisco de Vitoria University are pioneering a project using quantum computing and AI to design cancer drugs targeting the BRAF V600E mutation, showing preliminary results of improved molecular candidates. Additionally, AI is being used to design proteins that act as a 'GPS' for T cells to more effectively locate and target cancer cells.
Researchers have developed a new AI 'fingerprint' technology that analyzes changes in cancer cell shape to assess drug responses, potentially halving cancer drug development time with up to 99.3% accuracy. Additionally, a low-cost AI model can screen cervical cancer samples in 30 seconds, and human-AI teams have shown improved accuracy in identifying eligible patients for cancer clinical trials.
via The Times of India·OncoDaily·The Institute of Cancer Research, London
A study published on February 20 in the Journal of the American College of Radiology indicates that AI assistance significantly improves the detection of invasive and lobular cancers on screening digital breast tomosynthesis (DBT) images. The AI-enhanced interpretation led to the identification of more small-sized cancers, particularly in dense breasts.
Evogene and Queensland University of Technology (QUT) have partnered to accelerate the discovery of AI-driven small molecule cancer therapeutics, focusing on overcoming resistance to chemotherapy and targeted therapies. Insilico Medicine and Eli Lilly have also published a vision for fully autonomous "Prompt-to-Drug" pharmaceutical R&D, outlining how AI can streamline the entire drug discovery pipeline.
Researchers at the University of Maine have developed a new AI tool called the Context-Guided Segmentation Network (CGS-Net) to enhance early breast cancer detection by analyzing digital breast tissue images and considering surrounding tissue for a more comprehensive analysis. Pathology News also highlights recent developments in AI-enhanced imaging for cancer detection and new digital pathology methods, focusing on improving accuracy and efficiency in analyzing tissue samples.
An international research team has developed M-PACT, a new AI-based analysis method that accurately classifies brain tumors and monitors disease progression using genetic material from cerebrospinal fluid. The tool analyzes cell-free DNA fragments to identify characteristic molecular patterns for tumor classification. This development was reported on February 20, 2026.
via news-medical.net
Isomorphic Labs, an Alphabet company, has launched its AI-driven IsoDDE engine, a significant advancement in cancer drug discovery. This engine reportedly achieves double the accuracy of AlphaFold 3 in predicting protein-ligand structures and surpasses traditional methods in predicting binding affinity. Furthermore, IsoDDE can identify previously hidden binding pockets on proteins solely from their amino acid sequences, a capability that previously required extensive experimental work.
via OncoDaily
Researchers at the University of Arizona are collaborating with Quantoom Biosciences to develop an AI and mRNA-based framework for personalized cancer vaccines. This platform will identify neoantigen candidates to train the immune system against mutated tumor proteins. Separately, Stanford Medicine has developed Nuclei.io, an AI tool that enhances the speed and accuracy of pathologists in identifying cells, which is crucial for clinical trial enrollment, particularly in cancer.
The PANORAMA study has shown that an AI system achieved a higher diagnostic performance than radiologists in detecting pancreatic cancer on CT scans, with an AUROC of 0.92 compared to the radiologists' pooled performance of 0.88. The AI detected 38% more cancers at matched specificity and reduced false positives by 26% at matched sensitivity, suggesting it can augment radiologist capabilities.
via OncoDaily
Evogene and Queensland University of Technology (QUT) have announced a collaboration to develop AI-driven small molecule cancer therapeutics. This partnership will focus on therapy-resistant non-small cell lung cancer (NSCLC) and other cancers, utilizing Evogene's ChemPass AI platform to generate and prioritize potential drug candidates.
Insilico Medicine has partnered with Memorial Sloan Kettering Cancer Center (MSK) to discover novel therapeutic targets for gastroesophageal cancers. This collaboration will integrate MSK's clinical data with Insilico's AI-driven drug development platform to advance treatment strategies.
via insilico.com
Bristol Myers Squibb is implementing Evinova's AI-enabled clinical development platform globally to enhance trial design and accelerate timelines. Evogene and Queensland University of Technology (QUT) are collaborating to advance AI-driven cancer therapeutics, focusing on chemotherapy and targeted therapy-resistant non-small cell lung cancer.
Johns Hopkins Medicine researchers are employing AI to expedite and refine cancer drug discovery, aiming for a faster, more precise, and cost-effective process. Scientists have created an AI system that designs custom proteins to guide cancer-fighting immune cells more effectively. Additionally, MIT chemical engineers have developed a new AI model that optimizes protein manufacturing processes in industrial yeasts, potentially lowering the cost of protein drug development.
In AI drug development, Thermo Fisher Scientific's PPD has partnered with Datavant to enhance real-world data integration in clinical research. Evogene is expanding its alliance with Google Cloud to integrate AI agents. Additionally, Lantern Pharma showcased its ZETA AI platform, demonstrating its capability to design new cancer drugs by analyzing vast datasets.
Scientists have developed a new AI-powered blood test capable of detecting brain cancer with approximately 75% accuracy by analyzing DNA and immune system signals. Additionally, an updated AI blood test, DELFI, can now detect liver cancer with over 80% accuracy, including in its early stages.
Dr. Regina Barzilay, an MIT professor, has developed an AI model named MIRAI capable of predicting a patient's risk of developing breast cancer within five years. The model excels at identifying subtle changes in mammograms that are difficult for human eyes to discern, advancing early cancer detection capabilities.
via youtube.com
Takeda Pharmaceutical has entered into a significant partnership with Iambic Therapeutics, aiming to utilize artificial intelligence for drug discovery in cancer and other diseases. This collaboration grants Takeda access to Iambic's AI-driven platform and a predictive model for protein-receptor interactions. The deal has a potential value exceeding $1.7 billion.
Researchers have developed AI models for designing peptides that act as sensors for cancer-linked proteases, potentially enabling early detection anywhere in the body. Additionally, a new AI algorithm automates the detection of rare cancer cells in blood samples for liquid biopsies, significantly reducing analysis time. Johns Hopkins Medicine is also advancing cancer care with AI-based liquid biopsies showing promise for early detection of brain and liver cancers.
New research indicates AI-assisted mammography can significantly improve the early detection of aggressive breast cancers, reducing interval and aggressive cancer rates. Additionally, AI-generated sensors using peptides are being developed for early cancer detection by signaling the presence of cancer-linked proteases. Advances in AI-based liquid biopsies and blood tests also show promise for detecting brain and liver cancers, respectively.
via medicalnewstoday.com·hopkinsmedicine.org·technologyreview.com
GV20 Therapeutics has utilized AI to discover and develop a new antibody drug, GV20-0251, which targets a novel immune checkpoint, IGSF8. This drug showed promising results in a phase one clinical trial for advanced solid tumors, helping to shrink tumors or stabilize disease progression.
via scotscoop.news
A new multimodal artificial intelligence model has demonstrated superior accuracy in predicting breast cancer recurrence compared to the Oncotype DX genomic test. This AI model integrates molecular, histopathologic, and clinical data from patients to provide more precise recurrence risk scores for HR-positive, HER2-negative breast cancer.
Generative AI is showing significant promise in revolutionizing drug discovery by accelerating timelines and reducing failure rates, particularly in designing antibody candidates for previously undruggable targets. Additionally, researchers are utilizing AI to design molecular sensors for early cancer detection, aiming to identify the disease in its initial stages.
Investigators from Mass General Brigham have developed a new AI foundation model named BrainIAC. This tool can extract multiple disease risk signals from routine brain MRIs, enabling it to estimate brain age, predict dementia risk, detect brain tumor mutations, and forecast cancer survival rates.
via news.harvard.edu
New research suggests that AI-assisted mammograms can enhance the early detection of breast cancers and decrease the occurrence of interval diagnoses. A study involving 100,000 women in Sweden found that AI-supported screening led to fewer aggressive or advanced cancers being diagnosed between screenings.
An AI algorithm has demonstrated the ability to predict outcomes for oropharyngeal carcinoma by identifying extranodal extension (ENE) from CT scans. A study involving over 1,700 patients found that the number of ENE nodes detected by AI was significantly associated with overall survival and disease control. This application of AI offers a powerful biomarker for assessing patient prognosis.
via healio.com
A large trial in Sweden suggests AI can significantly improve breast cancer screening, leading to a 12% reduction in interval cancers and better detection of aggressive subtypes. Additionally, Norwegian hospitals are implementing AI diagnostic tools like PROVIZ for prostate cancer, enabling quicker and more accurate assessments.
The UK has launched its National Cancer Plan, which will prioritize technology, data, and AI with substantial investment to enhance cancer care. The plan includes a transition to digital and robotic automation for histopathology, aiming for significant productivity gains.
via htn.co.uk
ConcertAI has launched Accelerated Clinical Trials (ACT), an enterprise agentic AI platform aimed at automating and optimizing the entire clinical trial lifecycle. Unveiled at SCOPE 2026, ACT integrates real-world data with advanced AI workflows to potentially shorten trial timelines by 10 to 20 months and reduce costs.
via hlth.com
BostonGene has announced a significant independent validation of its AI and machine learning capabilities for assessing HER2 expression in breast cancer. A blinded, multi-vendor HER2 benchmarking study showed high agreement rates for BostonGene's foundation model, as published in Modern Pathology.
via morningstar.com
Massive Bio introduced its AI-based TrialRelay platform at the SCOPE 2026 conference, designed to prevent patient loss during oncology clinical trial referrals.
The FDA granted several critical designations for novel cancer therapies, including an orphan drug designation for an AI-related imaging agent for pancreatic cancer, highlighting the ongoing shift towards precision medicine.
via pmc.ncbi.nlm.nih.gov·recursion.com·oncodaily.com·oncodaily.com·labiotech.eu
The AI cancer diagnostics market in Asia Pacific is projected to grow from $41.7 million in 2023 to $247.4 million by 2030, with 80% of FDA-approved AI oncology devices focusing on diagnostics.
A new AI algorithm has been invented to automatically detect rare cancer cells in blood samples within approximately 10 minutes, a crucial step for liquid biopsies. Additionally, a large trial in Sweden found that using AI in breast cancer screening reduced the rate of later diagnosis by 12% and increased early detection rates.
The NHS has launched a pioneering pilot program that uses AI and robotic technology to detect lung cancer earlier, complementing an expanded screening initiative. This new approach employs AI software to rapidly analyze lung scans and identify potentially cancerous lumps.
via england.nhs.uk
Pharmaceutical companies are increasingly leveraging AI to streamline clinical trials and accelerate regulatory submissions, optimizing time-consuming processes in drug development. This application focuses on improving efficiency rather than new molecule discovery.
The FDA and EMA jointly issued Good AI Practice principles for drug development, establishing regulatory expectations for responsible AI use in pharmaceuticals.
via pmc.ncbi.nlm.nih.gov·recursion.com·oncodaily.com·oncodaily.com·labiotech.eu
Researchers have developed AI models to design peptides for sensors that detect cancer early and custom proteins to guide immune cells to target cancer cells more effectively. Additionally, AI is revolutionizing clinical trials by accelerating drug development, improving patient recruitment, and reducing trial failures through tools like causal inference and digital twins.
Researchers have developed AI-generated molecular sensors for early cancer detection via urine tests and an AI tool to automate cancer cell detection in blood samples. A study also found that AI significantly improves cancer detection rates for breast radiologists. These advancements aim to enhance early diagnosis and treatment of various cancers.
Researchers have developed AI-designed peptide sensors for early cancer detection via urine tests, and generative AI is now processing complex medical datasets significantly faster than human experts. Additionally, Jeeva Clinical Trials is urging the life sciences industry to modernize infrastructure to fully leverage AI in drug development, emphasizing the need for unified systems and regulatory compliance.
Researchers have developed an AI model to design peptide-based sensors for early cancer detection, potentially enabling at-home tests for various cancers. Separately, MIT chemical engineers created a new AI model that optimizes protein manufacturing processes, potentially reducing costs for cancer-treating drugs like monoclonal antibodies.
via MIT News·PNAS·news.usc.edu
Researchers have created an AI model capable of designing peptides that can act as sensors for cancer-specific proteases. These peptides, when incorporated into nanoparticles, can detect the presence of these overactive enzymes throughout the body. This breakthrough holds promise for developing new methods for early cancer detection, potentially even for at-home use.
via news.mit.edu
MIT and Microsoft researchers developed an AI model to design molecular sensors for early cancer detection, potentially leading to at-home urine tests.
via pmc.ncbi.nlm.nih.gov·recursion.com·oncodaily.com·oncodaily.com·labiotech.eu
2025
8 updates
2025
8 updatesCity of Hope experts predicted that by 2026, AI will be an integrated driver of improved patient care, with digital pathology and multi-omics AI becoming standard.
via pmc.ncbi.nlm.nih.gov·recursion.com·oncodaily.com·oncodaily.com·labiotech.eu
Microsoft Research developed GigaTIME, a new AI platform that accelerates cancer research by analyzing pathology slides to create detailed digital maps of tumor environments and reveal immune cell interactions.
via dig.watch
Scientists are using AI to design custom proteins that act as a 'GPS' for cancer-fighting immune cells, guiding them to target and destroy cancer cells. Russian researchers have also created a neural network capable of detecting early-stage breast cancer from CT scans in minutes, reducing clinical error probability by approximately 20%.
via sciencenews.org
Scientists are using AI to design custom proteins that act as a 'GPS' for cancer-fighting immune cells, guiding them to targets and killing melanoma cells in lab experiments. Stanford Medicine developed an AI tool, Nuclei.io, to enhance pathologists' efficiency and diagnostic accuracy by rapidly identifying specific cells in biopsy samples. A trial in Sweden showed AI can help doctors identify more breast cancer cases during routine screenings by analyzing mammograms.
via sciencenews.org
Scientists have utilized AI tools, including generative AI model RFdiffusion, to design custom proteins that act as a "GPS" for cancer-fighting immune cells. These AI-designed proteins, when engineered onto T cells, have shown the ability to rapidly kill melanoma cells in lab experiments. This approach represents a new method for utilizing AI in cancer treatment.
via science.org
Researchers have developed an AI model to design peptide-based sensors for early cancer detection, potentially enabling at-home tests. Separately, a novel AI model named ECgMPL has demonstrated near 100% accuracy in identifying endometrial cancer and can be adapted for other cancer types. Additionally, a new AI algorithm called RED can automate the detection of rare cancer cells in blood samples within 10 minutes.
via New Atlas·USC Viterbi School of Engineering·MIT Technology Review
A new AI model, ECgMPL, has demonstrated 99.26% accuracy in identifying endometrial cancer from microscopic images, significantly surpassing human diagnostic capabilities. Additionally, an AI system named AQuA has been developed to detect errors in digital pathology images with 99.8% accuracy. AI-assisted mammography trials suggest a reduction in aggressive breast cancer detection rates.
Insilico Medicine announced a breakthrough with AI-designed CDK12/13 inhibitors showing promise against treatment-resistant cancers, moving towards clinical trials.
via pmc.ncbi.nlm.nih.gov·recursion.com·oncodaily.com·oncodaily.com·labiotech.eu
2024
3 updates
2024
3 updatesResearchers at UChicago Medicine received significant funding to use AI and supercomputing to identify new targets for drug-resistant cancer therapies.
via pmc.ncbi.nlm.nih.gov·recursion.com·oncodaily.com·oncodaily.com·labiotech.eu
A new AI tool (CHIEF) achieved nearly 94% accuracy in cancer detection, guided treatment, and predicted patient survival across multiple cancer types.
via pmc.ncbi.nlm.nih.gov·recursion.com·oncodaily.com·oncodaily.com·labiotech.eu
The National Cancer Institute (NCI) reported using AI to improve cervical and prostate cancer screening, and for drug repurposing and predicting patient responses to treatment.
via pmc.ncbi.nlm.nih.gov·recursion.com·oncodaily.com·oncodaily.com·labiotech.eu
2023
2 updates
2023
2 updatesResearch demonstrated AI's capability to predict pancreatic cancer incidence from patient records, offering a less invasive and potentially more accurate screening method.
via pmc.ncbi.nlm.nih.gov·recursion.com·oncodaily.com·oncodaily.com·labiotech.eu
A study highlighted AI's critical role in precision medicine, enabling personalized treatment plans and predicting treatment effects for cancer patients based on genomic data.
via pmc.ncbi.nlm.nih.gov·recursion.com·oncodaily.com·oncodaily.com·labiotech.eu
2022
1 update
2022
1 updateThe Advanced Research Projects Agency for Health (ARPA-H) was established, later becoming a key funder for AI projects in cancer research.
via pmc.ncbi.nlm.nih.gov·recursion.com·oncodaily.com·oncodaily.com·labiotech.eu
2021
1 update
2021
1 updateEvotec and Exscientia announced an AI-developed oncology candidate entering Phase 1 clinical trials, significantly accelerating the drug discovery timeline.
via pmc.ncbi.nlm.nih.gov·recursion.com·oncodaily.com·oncodaily.com·labiotech.eu
2020
1 update
2020
1 updateGoogle's deep learning system demonstrated superior performance over radiologists in breast cancer screening, reducing false positives and negatives.
via pmc.ncbi.nlm.nih.gov·recursion.com·oncodaily.com·oncodaily.com·labiotech.eu
2019
2 updates
2019
2 updatesExscientia showcased AI's potential by developing a preclinical drug candidate in significantly less time than traditional methods, highlighting accelerated drug discovery.
via pmc.ncbi.nlm.nih.gov·recursion.com·oncodaily.com·oncodaily.com·labiotech.eu
AI applications in cancer imaging demonstrated effectiveness in screening for lung nodules on low-dose CT scans, contributing to earlier diagnoses.
via pmc.ncbi.nlm.nih.gov·recursion.com·oncodaily.com·oncodaily.com·labiotech.eu
2018
1 update
2018
1 updateAI-powered automated segmentation techniques were developed, improving reproducibility and efficiency in tumor analysis for treatment planning.
via pmc.ncbi.nlm.nih.gov·recursion.com·oncodaily.com·oncodaily.com·labiotech.eu
2013
1 update
2013
1 updateRecursion Pharmaceuticals was founded with the vision of utilizing AI to understand cellular biology and accelerate drug discovery, aiming to reduce the high failure rate of traditional methods.
via pmc.ncbi.nlm.nih.gov·recursion.com·oncodaily.com·oncodaily.com·labiotech.eu
1970
Story began · 57 years ago