AI/ML News & Innovations Hub

Qiagen grounds drug discovery agents in curated knowledge

Qiagen N.V. is betting that drug discovery agents need a trustworthy knowledge foundation as much as capable models. For biopharma companies, that means giving agents information with clear provenance and enough context to support their answers. Knowledge graphs can provide that context layer for AI agents, according to Iman Bhattacharya (pictured), senior global product marketing […] The post Qiagen grounds drug discovery agents in curated knowledge appeared first on SiliconANGLE .

When his wife got sick, this doctor turned to an AI chatbot to navigate her rare cancer diagnosis. Then he invested.

An AI healthcare chatbot helped Dr. Munjal Shroff support his wife through her cancer diagnosis. He ended up investing in the startup.

AI drug discovery startup Basecamp Research raises $140M

Basecamp Research Ltd. today announced that it has raised $140 million in funding from a group of prominent investors. S32, a fund affiliated with Google LLC co-founder Bill Maris, led the Series C round. It was joined by more than a dozen other investors. The group included NATO, Nvidia Corp. and the Anthology Fund, a […] The post AI drug discovery startup Basecamp Research raises $140M appeared first on SiliconANGLE .

MLCommons Joins EU-Funded AIRIS Project to Build and Benchmark Next-Generation Biomedical AI

Standardizing independent evaluation, fairness, and interpretability for mechanism-informed generative AI in healthcare The post MLCommons Joins EU-Funded AIRIS Project to Build and Benchmark Next-Generation Biomedical AI appeared first on MLCommons .

Why confidential computing is essential for enterprise AI

Artificial intelligence has entered a new phase. For most large organizations, the conversation is no longer whether AI can create business value. It is how quickly AI can be deployed across the enterprise while maintaining security, governance, compliance, and control. From customer service and software development to drug discovery and financial modeling, AI is increasingly being applied to organizations’ most valuable assets: their data. The quality of AI outcomes depends directly on the quality, breadth, and sensitivity of the information used to train, fine-tune, and run models. Yet many enterprises face a fundamental dilemma. The data that creates the greatest business value is often the same data that carries the highest levels of risk. Customer records, intellectual property, financial data, healthcare information, government data, and operational insights cannot simply be exposed to new platforms or shared broadly without appropriate safeguards. As a result, many organizations find themselves caught between the desire to accelerate AI adoption and the need to protect their most critical information. This is where confidential computing is rapidly emerging as a foundational technology for the AI era. The security gap hidden in plain sight For decades, enterprise security strategies have focused on protecting data in two key states: at rest and in transit. Data stored on systems can be encrypted. Information moving across networks can be secured through encrypted comm…

Bristol researchers say medicine already knows how to handle black boxes and AI could learn from it

Researchers at the University of Bristol want to make medical AI systems safer by borrowing from how drugs get approved. Their "Learning Ensemble" framework defines three areas to check, including system limits, fairness across patient groups, and clinical fit. The goal is to catch models that work technically but can still be dangerously wrong in the clinic. The article Bristol researchers say medicine already knows how to handle black boxes and AI could learn from it appeared first on The Decoder .

India’s drug regulator forms AI expert committee: What we know, what remains unclear

DCGI Rajeev Raghuvanshi said CDSCO’s new AI committee will examine how AI can be evaluated, approved and regulated in drug discovery. The post India’s drug regulator forms AI expert committee: What we know, what remains unclear appeared first on MEDIANAMA .

Alibaba open-sources medical AI model that can detect cancer and nearly 150 conditions

Alibaba Group Holding’s research arm, Damo Academy, has open-sourced an artificial intelligence model capable of identifying nearly 150 abdominal conditions – including cancers – by reading computed tomography (CT) scans, marking the latest step in the firm’s growing medical AI efforts. The vision-language model, called Damo Radar, was designed to analyse contrast-enhanced CT scans covering 18 abdominal organs and identify a broad range of diseases and other abnormalities, such as malignant...

Novo to partner with Anthropic on drug discovery efforts

The one-time GLP-1 leader is betting AI will help it reclaim lost market share.

Anthropic Opens Claude Mythos to Vetted Biology Teams for Drug Discovery

Anthropic opens vetted access to its restricted Mythos model for biology work, swapping real-time blocking for offline monitoring under a shared-responsibility framework.

Danish pharma giant Novo to use Anthropic’s Claude to advance AI drug discovery

The deal underscores a broader push by drugmakers to integrate AI across research and development to boost new drug discovery.

AI biotech Isomorphic Labs is not slowing down, exec says

“All of our AI models are locked down and locked down in-house,” Chris Butler, who runs drug discovery at the Google-backed drug company, said at Semafor’s Future of Health Forum.

AI models need more data about biology, and OpenAI is paying to create it

Last year Ruxandra Teslo, a policy analyst who focuses on clinical trials, posted an idea for supercharging medical AI systems: Use data from failed biotech companies. By bidding at their bankruptcy proceedings, she proposed, it might be possible to obtain detailed regulatory filings, manufacturing strategies, and safety data—types of information usually considered trade secrets. She…

M42 to adopt Oracle data platform for AI healthcare

Abu Dhabi’s M42 to unify health data with Oracle’s cloud-based data platform

OpenAI Opens GPT-Rosalind to Drug Discovery Teams With 50+ Scientific Tools

OpenAI's new life sciences reasoning model moves from research preview to trusted access, with a Codex plugin connecting to 50+ scientific databases and tools.

SimpleDesign: A Joint Model for Protein Sequence and Structure Codesign

Proteins are fundamental to biological processes, with their function determined by the complex interplay between the amino acid sequence and the three-dimensional structure. Developing generative models capable of understanding this intrinsically multi-modal relationship is crucial for fields like drug discovery and protein engineering. Existing models often rely on a multi-stage training process where autoencoders that tokenize data into latent representations are trained in a first stage. Secondly, a generative model is trained on the latent representation of the autoencoder(s), i.e…

Dubai Healthcare City begins work on Dh3b Dubai Creek Gardens

Provinces race to commercialize quantum technology research

Provinces race to commercialize quantum technology research J.Heller Wed, 09/09/2026 - 11:30 picture alliance / Xinhua News Agency | Dai Rui Comment Sep 10, 2026 2 min read Provinces race to commercialize quantum technology research China aims to lead in quantum – even as many quantum technologies are not yet mature. Provinces and cities are already competing to build markets for a range of applications. Most of these experiments are unlikely to pan out commercially as the technology is still nascent, and costs remain too high. But for Beijing, even unsuccessful or financially unfeasible applications show where quantum tech can prove most useful – an early bet that may pay off as a competitive advantage in the medium to long term. Guangdong province recently introduced 100 application scenarios in eight areas, including medical diagnosis, marine exploration, and fintech, which could lay the foundation for the mass implementation of quantum technologies in China. Hospitals and the national lab in Guangzhou are already piloting quantum-enabled screening for cardiovascular disease and molecular simulation for drug discovery. The stakes for medicine are high, as Chinese researchers argue that quantum computing could cut drug development timelines from ten to three years, given quantum’s edge in precise molecular simulation. Other localities have been early supporters of commercialization. Shanghai’s new quantum computing hub offers up to CNY 20 million (EUR 2.57 million) to comp…

As China embraces AI, only 24% of US pharmaceutical execs bet on it for innovation

Pharmaceutical companies have embraced artificial intelligence in drug research almost universally, yet few company executives expect AI to actually make drugs succeed, according to a Citi survey released on Tuesday. “The biggest risk to the AI-powered drug discovery thesis is not that AI fails to accelerate discovery,” said John Yung, head of Asia healthcare research at the US investment bank. “The stronger risk is that acceleration does not translate into better drugs or higher probability of...

Google Deepmind's AI Co-Scientist now plans experiments, runs lab equipment, and writes scientific papers

Google Deepmind has expanded Co-Scientist from a hypothesis generator into a research system that's integrated into the lab. Across three disciplines, from materials synthesis to the autonomous development of a medical AI architecture, the Gemini-based multi-agent system delivered experimentally validated results. The article Google Deepmind's AI Co-Scientist now plans experiments, runs lab equipment, and writes scientific papers appeared first on The Decoder .

Comment on MedVersa: A Game-Changer Generalist Learner for Versatile Medical Image Interpretation by AI Image Extender

This is a fascinating direction for medical AI! The idea of a "generalist learner" like MedVersa, especially one using a large language model as an orchestrator, feels like a significant step beyond the specialized models we've seen so far. It reminds me a bit of how generalist AI models are starting to tackle a broader range of creative tasks, not just one specific thing. For instance, I've been working with a tool that can extend images while keeping the subject intact across different canvas sizes – it’s a different domain, but the underlying principle of versatility is the same. It makes me wonder how this generalist approach might eventually filter down to more niche AI applications, making them more adaptable. Will we see AI that can not only interpret an X-ray but also then generate a report, or even suggest treatment options, all within one adaptable framework?

All In Capital leads pre-seed round in healthcare infrastructure startup Tross

Healthcare infrastructure startup Tross has raised an undisclosed amount of funding in a pre-seed round led by All In Capital along with participation from DeVC. The proceeds will be used to accelerate product development, expand the engineering team, build additional EHR and payer integrations, and deepen its presence in the US healthcare market, Tross said in a press release. Co-founded by Padam Kataria and Meet Shah, Tross builds the infrastructure layer for AI in healthcare, enabling AI companies to connect with EHRs, payer portals and other complex healthcare systems through APIs and integrations. Its platform allows healthcare AI companies to automate critical workflows across scheduling, referrals, claims, clinical documentation and data extraction. Tross claims that it is already working with healthcare AI companies serving the US market and is on track to enable over 200,000 patient interactions this year across calls, referrals, scheduling and back-office workflows. Its platform can connect AI applications to EHRs and payer portals, enabling workflows such as retrieving claim status, uploading clinical notes and scheduling appointments directly within provider systems. Tross plans to expand the number of healthcare systems supported by its platform and increase the range of workflows that AI companies can automate through its infrastructure. AI-powered healthcare workflow automation is emerging as a significant opportunity in the Indian startup ecosystem, with comp…

How Bayer Built an Enterprise-Scale Search Engine with Qdrant

Bayer is a global life sciences company operating at the intersection of two of the most consequential fields in human life: health and nutrition. Its pharmaceutical work supports drug discovery and patient care, while its crop science work supports food production at planetary scale. The company’s guiding ambition, “Health for all, hunger for none,” frames how it thinks about technology: AI is not a side project, but a lever applied across the entire organization, from improving the productivity of colleagues to accelerating yield prediction and drug discovery.

Building the foundation for agentic AI in healthcare

The post Building the foundation for agentic AI in healthcare appeared first on Source .

SAMRC and UCT Launch AI Unit Built Around African Health Data, Targeting Cancer, Diabetes, TB and HIV

The South African Medical Research Council and the University of Cape Town have launched an AI for African Population Health Unit that will build machine learning tools trained specifically on African biomedical and clinical datasets — a direct response to the problem that most medical AI is trained on populations it will never treat. The [...]

DeepMind founder ascends to singular AI role at Google

Demis Hassabis, the driving force behind Google DeepMind, is ascending to the role of chief scientist at Alphabet, Google’s parent company, replacing Jeff Dean who is leaving to work at a start-up. The role will enable Hassabis to “put his full attention on actively shaping the future of AGI,” or artificial general intelligence, Alphabet CEO Sundar Pichai wrote on the company’s Inside Google blog . Hassabis’ attention will still be divided, however: He will continue to lead research at Google spin-off Isomorphic Labs, which works on drug discovery, and although he will no longer be CEO of DeepMind, he will be its chair. Koray Kavukcuoglu will take over DeepMind, reporting directly to Pichai. He is currently its CTO. Hassabis has been a strong promoter of AGI, defined by Google as the “hypothetical intelligence of a machine that possesses the ability to understand or learn any intellectual task that a human being can.” He has a long career in AI, having helped found DeepMind in 2010. He has been a prominent figure in the AGI field, prophesying in May that it will be a viable technology within three years . He has been keen to tackle any barriers in the way of developing the technology; just last month, he called for greater self-regulation in the market, arguing that it would help drive the technology forward. Hassabis welcomed the chance to focus on AGI development. “We have arrived at a pivotal moment in human history. I’ve been working towards AGI my whole life, and now, I…

Plucky founders, not Indian pharma giants, could reshape drug discovery in its new ‘golden age’

The post Plucky founders, not Indian pharma giants, could reshape drug discovery in its new ‘golden age’ appeared first on The Ken .

Google’s Top AI Brains Are Leaving to Launch Discovery Loop

Jeff Dean and other high-profile Google executives have founded Discovery Loop, a startup that will seek AI-powered breakthroughs in everything from drug discovery to chip design.

Will AI healthcare be China’s new global growth engine?

Artificial intelligence (AI) has transformed healthcare for hundreds of millions of people in China. AI-powered apps help them manage their health and seek care, while others help doctors better diagnose and treat their patients. AI analysis of routine scans detects cancers and other conditions physicians might have missed. Personalised cancer vaccines built on AI analysis will soon begin rolling off production lines in China. The country’s leading tech companies have poured resources into...

Why haven't organoids solved all of drug discovery?

Thank you to Jimmy Sastra and Kitchener D. Wilson for discussions relating to this piece. All opinions are my own. What are organoids anyway? If someone placed a gun to the side of your temple and asked you to correctly define what exactly an “organoid” is, or else they’ll shoot, you may get a little nervous. The whole concept is something, you suddenly realize, you never quite understood. It’s more of a you-know-it-when-you-see-it thing, nobody in their right mind would ever ask for a clear definition, but you forgot to account for those who are not in their right mind. Desperately, you rack your brain for details. It’s an aggregate of cells, right? You stammer this out, tack on ‘ it’s three-dimensional too! ’, and stare at your antagonist expectantly. They consider your answer, and squeeze their hands. If we were to interview your terrorizer later on, they would sheepishly admit that your answer is not the worst one they’ve heard. It really does encapsulate the many forms that organoids may take. Unfortunately, your given definition would also welcome blood clots, teratomas, and biofilms, all of which are indeed aggregates of cells arranged in three dimensions, but none of which would be considered an organoid by anybody on the planet. You should not feel bad about this. In fact, you are in good company. The organoid field as a whole is not known to have particularly useful definitions. One attempt at a definition comes from a 2014 Science review titled ‘ Organogenesis in…

Closing the data loop in AI-driven drug discovery

Drug discovery is a high-cost, high-risk endeavor that is under growing pressure from a market increasingly defined by first-mover advantage. Since the 1950s, the cost of developing new pharmaceuticals has roughly doubled every nine years—a phenomenon known as Eroom’s Law. Today, bringing a new drug to market takes an average of 10-15 years and costs…

MedPerf Meets Google Cloud Confidential Computing: Secure AI Benchmarking for Brain Tumor Research

At Google Cloud Next 2026 in Las Vegas, MLCommons and Google Cloud demonstrated a powerful new capability for trustworthy medical AI - one that protects patient data, model IP, and benchmark integrity all at once. The post MedPerf Meets Google Cloud Confidential Computing: Secure AI Benchmarking for Brain Tumor Research appeared first on MLCommons .

🔬Causal Models Need Causal Data - Xaira’s X-Cell model for Drug Discovery (Bo Wang & Ci Chu, Chief Discovery Officer & Chief AI Scientist)

Xaira Therapeutics is all in on data generation for model building! We talk with Bo Wang and Ci Chu about how and why.

Johns Hopkins-FDA Team Builds Tool to Catch Hidden Bias in Medical AI Training Data

Researchers at Johns Hopkins University, working with the U.S. Food and Drug Administration, have built a tool designed to uncover hidden problems in the datasets used to train medical AI — a class of research whose implications land squarely in African healthcare, where the imaging systems, patient populations and clinical workflows behind training data often [...]

Sightera Biosciences closes €3M pre-seed to expand its patient-derived AI drug discovery platform

Sightera Biosciences, a Belgiantechbio company using generative AI to develop novel small-molecule therapies,has raised €3 million in a pre-seed funding round led by Entourage, Anacura andQBIC.A spin-...

Drug discovery Is changing. Drug development must change too.

In this New Scientist CoLab podcast, experts from global life sciences leader Cytiva explain the hidden, high-stakes science of purification that is required to close the gap between drug discovery and the pharmacy shelf.

OpenAI researcher Miles Wang in talks to launch AI drug discovery startup valued at $2B

The funding discussions point to investor interest in applying AI to make breakthroughs in life sciences.

Meet Biomni: the free powerful biomed AI agent turning data into hypotheses

A Stanford University-led team including two Chinese researchers said they built the first general-purpose biomedical AI agent capable of working alongside human scientists, taking on complex tasks that once required groups of specialists. Jure Leskovec, a Stanford computer science professor who supervised the work, said the agent had been released as an open-source system with a web interface so that biologists could use it without writing code. “We have over 10,000 scientists all over the...

Anthropic launches its own drug discovery programs to tackle diseases Big Pharma considers unprofitable

Anthropic is launching its own drug development program for neglected diseases that the pharmaceutical industry considers unprofitable. Novartis CEO Vas Narasimhan thinks AI could cut development time from twelve years to seven or eight and double the success rate from 8 to 16 percent. The article Anthropic launches its own drug discovery programs to tackle diseases Big Pharma considers unprofitable appeared first on The Decoder .

Empowering biomedical evidence exploration and synthesis with deep knowledge graph research

Nature Machine Intelligence, Published online: 02 July 2026; doi:10.1038/s42256-026-01266-0 Wang et al. develop DeepEvidence, a biomedical deep research agent for exploring and synthesizing evidence across various knowledge sources to support drug discovery, clinical trials and evidence-based medicine.

🔬 The Coolest Diffusion Research Isn't in LLMs — Evan Feinberg & Sergey Edunov, Genesis Molecular AI

Why the Llama lead left Meta for drug discovery, PEARL's zero-shot OpenBind win, and what becomes possible when co-folding finally crosses the accuracy threshold.

Build an agentic AI healthcare claims pipeline with Amazon Bedrock and AWS HealthLake

In this post, we show you how to build an automated claims processing pipeline using two key Amazon Bedrock capabilities: Amazon Bedrock Data Automation for intelligent document extraction from healthcare claim forms, and Amazon Bedrock AgentCore for hosting an AI agent that validates and transforms the extracted data into FHIR (Fast Healthcare Interoperable Resources) resources in AWS HealthLake. You will learn how to combine these services to create an end-to-end workflow that reduces manual processing while maintaining accuracy through automated validation checks.

A Soweto startup’s unlikely journey from gadgets to AI healthcare

Founded in Soweto and backed largely with internally generated revenue and founder reinvestment, Khoi Tech initially built its reputation through consumer devices such as the Khoi Afripods true wireless earphones and the Khoi Afriwatch1 smartwatch.

Exclusive: JiviAI shuts down; founder Ankur Jain may rejoin BharatPe

JiviAI, an AI healthcare startup founded by former BharatPe Chief Product Officer Ankur Jain, has shut down operations, according to multiple sources familiar with the matter. The development comes less than two years after the startup entered the crowded generative AI healthcare space. The company had bet on proprietary AI models to deliver medical assistance and healthcare related services. The startup also raised an undisclosed funding in late 2024. According to sources, the shutdown came amid rising infrastructure costs, funding challenges, and failed acquisition discussions. “Building and running proprietary AI models became increasingly expensive. When you’re up against companies like OpenAI and Google, it becomes very difficult to make the economics work,” said a person familiar with the matter, requesting anonymity. According to another source, investors who had initially shown interest in backing the company did not participate in its planned funding round, putting additional pressure on its finances. “There were a few acquisition discussions as well, but none of them materialised. Once those fell through, the company had very few options left,” the person said. Sources said employees have been informed about the shutdown and have been asked to leave as the company winds down operations. Industry sources also suggest that Jain is evaluating his next move. Some industry observers have speculated about a possible return to BharatPe following the recent departure of Gr…

Comment on Healthcare Benchmarks Are Only as Good as Their Assumptions by Healthcare Benchmarks Are Only As Good As Their Assumptions - Cosmic Meta Digital

[…] benchmarks for 2026, largely due to increased costs in traditional Medicare. At the same time, new AI benchmarks for evaluating medical AI are also being […]

How a Google DeepMind Spin-off Hunts Hidden Drug Targets

For more than a decade, artificial intelligence has been touted as a way to dramatically accelerate drug discovery . Yet despite billions of dollars in investment, relatively few AI-designed medicines have made it to patients. That’s partially because the timelines for careful drug testing can’t be easily compressed—and partially because drug development is just really hard. Isomorphic Labs , the Google DeepMind spin-off that’s building on DeepMind’s Nobel Prize-winning work on protein structure prediction , may be making the most progress. The company has signed major drug-discovery partnerships with Novartis and Eli Lilly and recently raised US $2.1 billion in funding . In February, it published a technical report describing its new Isomorphic Drug Design Engine, a system created to discover the “pockets” on proteins where drugs can bind and in general to predict how proteins and drug molecules interact. IEEE Spectrum spoke with Adrian Stecuła , a group leader in the machine learning organization at Isomorphic Labs, about how close AI may be to becoming a practical tool for designing new medicines. Going Beyond AlphaFold AlphaFold2 and AlphaFold3 were massive leaps forward for computational biology. Why weren’t those models sufficient for actually designing drugs? Adrian Stecuła: AlphaFold2 was eventually recognized with the Nobel Prize , because it arguably solved the problem of protein folding. But proteins don’t exist in a vacuum, right? They interact with a wide variet…

Demis Hassabis On What AI Will Do Next

Thank you to Google DeepMind for the invite. 🙏 ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Our Patreon if you wish to support us: https://www.patreon.com/TwoMinutePapers 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Adam Bridges, Benji Rabhan, B Shang, Cameron Navor, Charles Ian Norman Venn, Christian Ahlin, Eric T, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Ryan Stankye, Shawn Becker, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi My research: https://cg.tuwien.ac.at/~zsolnai/ Thumbnail design: https://felicia.hu 00:00 Intro 00:40 Gemini Health Scans and Gemma 4 01:30 AI as a Brainstorming Partner 02:30 Second Order Nobel 03:15 DeepMind Co-Scientist 05:00 Curing All Diseases 06:30 Exponential Growth in Drug Discovery 07:45 Regulatory Bottlenecks 09:45 Accelerating Clinical Trials 11:15 EVE Online Partnership 13:15 The Einstein Test 15:30 Recursive Self-Improvement 18:15 Lightning Round 19:30 The Badge of Honor 20:10 Behind the Scenes

How Sunny Health Built an AI Healthcare Concierge with Qdrant

Most people don’t read their insurance pamphlet. The benefits are there: deductibles, copays, in-network providers, what dental covers, what dermatology covers, when an optometry visit is included in the medical plan. But the document is dense, the website is worse, and the result is that patients pay for plans they barely understand and delay care because finding an in-network provider with availability takes more energy than they have. Sunny Health is building a healthcare concierge that insurance companies and care providers offer to their members as part of the existing plan experience. When a member signs in (typically through SSO from their payer), Sunny Health already knows who they are and what their plan covers. They land in a chat experience where they can ask “show me dermatologists nearby,” get matched to in-network options, and have Sunny Health book the appointment on their behalf. Three things on one retrieval layer: benefits navigation, provider matching, and appointment booking.

Intelligence is collective, not artificial — Prof. Michael I. Jordan (UC Berkeley / Inria)

Michael I. Jordan, described by Science magazine as the most influential computer scientist alive, has never thought of himself as an AI researcher. In this conversation he explains why that distinction matters. SPONSOR: --- Cyber Fund built the Monastery to help founders ship products that were impossible a year ago. Applications for Batch 1 are now open. Apply now: https://cyber.fund --- Jordan trained as a statistician and cognitive scientist, and his career has been spent building machine learning systems that work in the real world: supply chains, commerce, healthcare, and large economic systems. When the field rebranded itself as AI and then AGI, he did not follow. Instead he argues that the framing is wrong. AI is better understood as a collective economic system than as a race to build a disembodied superintelligence. We talk about why AGI is mostly a PR term, what machine learning achieved before the LLM hype cycle, and why the assistant-on-your-shoulder vision may be less compelling than it sounds. Jordan explains why explanations need to be actionable, not merely mechanistic; why AlphaFold's missing error bars matter; how prediction-powered inference changes the picture; and why drug discovery is an incentive-design problem rather than a pure pattern-matching problem. ERRATA: Science magazine ranked him the most influential computer scientist, not Nature --- TIMESTAMPS: 00:00:00 Cold open: A demoralizing message to young builders 00:02:04 CyberFund sponsor read 00…

Innovative Solutions to Improve Health and Patient Outcomes Featured at Inaugural AI in Healthcare Technology Showcase

At the inaugural AI in Healthcare Technology Showcase today, AI Singapore exhibited 16 AI projects that are set to improve healthcare processes and patient outcomes in Singapore. The event...

OpenEvidence Powers Medical AI with Zilliz Cloud

DrugDiscoveryBench DrugDiscoveryBench: 82 expert-curated tasks evaluating how reliably frontier coding agents perform the computational work of early-stage drug discovery. View Full Ranking →

How Anthropic & Novo Will Use Claude AI for Drug Discovery

How AI LIVE will Explore the Future of AI in Drug Discovery

This Mysore-born scientist, whose great-grandfather was a mathematician and grandfather a Sanskrit scholar, helped make AI faster at weather forecasting & drug discovery

The benefits of medical AI assistance vary based on user expertise

AI in healthcare: New agentic platform automates workflows to allow more doctor-patient time

The benefits of medical AI assistance vary based on user expertise

Loud or Silent? A Reusable Framework for Per-Modality Failure Analysis in Multimodal Clinical AI

The benefits of medical AI assistance vary based on user expertise

Anthropic: AI Drug Discovery and Medical Research

Capgemini: How AI is Transforming Pharma Drug Discovery

ByteDance spins out drug discovery unit to test AI4S commercialization

Bayer Deploys Iambic AI to Accelerate Drug Discovery