Discourse monitor
How the monitored publications were assessed
Discourse valence across every scored item: what the sampled sources published and how independent models read it. It is not a measure of public opinion, not a measure of the state of the world, and article volume is not agreement.
Mean discourse valence per day · medicine · last 365 days
Discourse valence, window mean
-13
Mixed or uncertain
43 items · range -79 to +55
-100
-50
0
+50
+100
-13
Adverse readingFavourable reading
43 items scored in this window
- Trump admin using AI to deny medical care for seniors in disastrous experiment
- Being judged through the record: A Rosenhan-inspired test of psychiatric label sensitivity in large language models
- AI doctors’ orders come with a sky-high bill
- Does Multi-Agent Collaboration Reduce Hallucination in Clinical Tasks?
- From Knowledge to Legitimacy: A Philosophical Problem Discovery of AI Implementation Readiness in Public Health Disease Surveillance
- AI-powered medical devices must be tested in real-world settings
- Watching What We Eat: Information Quality and Body Image in Diet-Related YouTube Videos
- Toward Fairness in Machine Learning Models for Predicting Treatment Retention and Premature Discontinuation in Medication for Opioid Use Disorder
- Prepared Or Unprepared? Evaluating Healthcare Workforce Readiness for Clinical Adoption of Artificial Intelligence in Nigeria
- Automated screening of privacy risks in medical AI
- Risk and Refusal: Mechanisms of LLM Responses to Suicidal Ideation
- Researchers use AI to design proteins that cut up other proteins
- Right Words, Wrong Moment: A Clinician-Grounded Analysis of Distress in 19,930 Conversations between Young People and ChatGPT
- British households would be £5,000 richer if we were more innovative. Let’s unleash our talents | Gordon Brown
- Evaluating Ambient Clinical Scribes in India: The Need for Multilingual Real-World Clinical Conversation Data
- Matched but Not Better: An Equivalence-Tested Benchmark of Quantum Machine Learning on Six Clinical Datasets and the QML-Health-READY Reporting Framework
- Big tech says AI can find a cure for cancer. So where is it?
- AusClinBench: a deterministically scored benchmark of large language models against current Australian clinical policy
- Scalable Oversight for AI in Mental Health: Lessons from 350,000 AI Coaching Conversations between Therapy Sessions
- Single-nucleus transcriptome-wide association study of human brain disorders
- Large Language Models Versus Physicians in Traditional Chinese Medicine: A Real-World Clinical Case Evaluation
- BioEVAL: A global, multi-institutional benchmark of large language and multimodal models for bioengineering
- A Comparative Framework for Evaluating Foundation Models on Tabular Data: A Case Study in Healthcare
- When Reconstruction Is Not Enough: Doubly Robust Counterfactual and Interventional Mediation Analysis with Learned Multimodal Representations
- Do Vision CIL Methods Transfer to Protein? A Diagnostic Benchmark of Frozen Protein Encoders
- Introducing SynthID Bio
- RFK Jr outlines expansive vision for collecting US health data at Maha event
- Global maps of travel time to emergency and tertiary hospitals
- New AI health tools need ‘L-plates’, says UK review
- Introducing Quine: An AI research system designed for the complexity of biology
- From Pattern Recognizers to Personalized Companions: A Survey of Large Language Models in Mental Health
- UK needs new laws for AI in healthcare, says watchdog
- Decision language models as affordable and rapid alternatives to LLM judges of clinical generative AI
- Didactic knowledge or Clinical Cases? How Data Types Shape Medical Large Language Models
- Early Data Indicates an A.I.-Generated Drug Could Slow Aging
- How a team of AIs discovered a promising lung-cancer drug
- Offline Apprenticeship Learning for Human-Centric Environments
- Asymmetric Decline Rather Than Backfire: A Repost-Network Analysis of Japanese Vaccine-Shedding Misinformation on X
- The AI-Enabled Scientific Frontier
- ClinGuard: a governance framework for reliable, transparent, and auditable clinical coordination
- Using thousands of AI agents to uncover new leads in biological data
- Reinforcement Learning over Patient Trajectories for Clinical Reasoning in EHR Foundation Models
- Google DeepMind Maps 9 Billion Possible DNA Variants
3560
discovered, all time
984
passed triage
2574
rejected at triage
3464
model assessments
710
multi-model panels
1780
distinct claims tracked
Across the whole window
The two tables below describe every item in the last 365 days under medicine. They are deliberately not narrowed by the reading filter, because a mean taken only over items already selected for their score would just restate the filter.
By topic
| Topic | Branch | Items | Mean | Position |
|---|---|---|---|---|
| Existential and catastrophic risk | risk | 568 | -42 | |
| Governance and regulation | structural | 459 | -31 | |
| Concentration of power | risk | 451 | -36 | |
| Loss of control and alignment | risk | 337 | -49 | |
| Human agency and epistemics | risk | 166 | -26 | |
| Cyber capability | risk | 165 | -44 | |
| Evaluation and measurement | structural | 159 | -19 | |
| Scaling and architecture | structural | 137 | -16 | |
| AGI and timelines | structural | 132 | -29 | |
| Synthetic media and manipulation | risk | 104 | -44 | |
| Employment and displacement | risk | 62 | -31 | |
| Medicine and health | benefit | 43 | -14 | |
| Scientific acceleration | benefit | 42 | -4 | |
| Education and access to expertise | benefit | 29 | -17 | |
| Biological and chemical uplift | risk | 26 | -25 | |
| Climate and energy | structural | 20 | -33 | |
| Compute and infrastructure | structural | 18 | -8 | |
| Productivity and growth | benefit | 6 | -3 | |
| Abundance and material wellbeing | benefit | 5 | -1 | |
| Hype and disillusionment | structural | 5 | -45 |
By source
A source's mean says how the items it published were read. It is not a rating of the source.
| Source | Kind | Tier | Items | Mean |
|---|---|---|---|---|
| The Guardian: Artificial Intelligence | news | 1 | 241 | -52 |
| arXiv cs.CY (Computers and Society) | academic | 1 | 217 | -24 |
| Financial Times: Artificial Intelligence | news | 1 | 112 | -40 |
| arXiv cs.AI | academic | 1 | 99 | -15 |
| The New York Times: Technology | news | 1 | 60 | -36 |
| Ars Technica: AI | news | 1 | 59 | -51 |
| MIT Technology Review: AI | news | 1 | 34 | -42 |
| Nature | academic | 1 | 31 | -25 |
| BBC News: Technology | news | 1 | 29 | -35 |
| OpenAI | lab | 1 | 24 | -8 |
| arXiv cs.LG (Machine Learning) | academic | 1 | 24 | -16 |
| IEEE Spectrum: AI | news | 1 | 19 | -19 |
| The Economist: Science and Technology | news | 1 | 10 | -37 |
| CSET, Georgetown | government | 1 | 9 | -37 |
| Microsoft Research | lab | 1 | 8 | 0 |
| Google DeepMind | lab | 1 | 6 | -4 |
| Google AI Blog | lab | 1 | 2 | -9 |