DIAGNOSTICS
1. Tempus AI can now flag pulmonary hypertension from the ECG your GP already ordered.
Source: BusinessWire / Tempus AI press release, August 24, 2026
Source: Google Research / Google DeepMind multimodal AMIE study
The signal: Tempus AI (NASDAQ: TEM) received FDA 510(k) clearance for Tempus ECG-PH, software that analyzes a standard 12-lead ECG for signs of elevated pulmonary artery pressure, an indicator of pulmonary hypertension (PH). It is Tempus' third FDA-cleared cardiovascular AI device, following previous clearances for atrial fibrillation and low ejection fraction detection.
The details:
PH affects roughly 1% of the population and up to 10% of adults over 65. Diagnosis has historically required right heart catheterization (an invasive procedure entering through the right side of the heart) plus echocardiography.
Tempus ECG-PH analyzes resting 12-lead ECG recordings from patients 40 and older with cardiac symptoms (breathlessness, fatigue, chest pain, or swelling) who have no known PH history. It outputs a binary risk flag for elevated mean pulmonary artery pressure above 20 mmHg.
Brandon Fornwalt, MD PhD, Tempus' SVP of Cardiology, said the tool is designed to surface PH risk at the point of routine testing, before the specialist referral pathway begins. assesses diagnostic accuracy, clinical history taking, and communication empathy across complex simulated video patient visits.
Why it matters: Pulmonary hypertension kills slowly because no one catches it early: the symptoms are vague, the gold-standard test is invasive, and most patients see a primary care doctor long before they see a cardiologist. A binary flag on a routine ECG changes when the question gets asked. Tempus is building a cardiovascular AI portfolio with real diagnostic leverage at the earliest point of contact.
CLINICAL AI
2. Reasoning AI models are generating more clinical bias, not less.
Source: Fierce Healthcare / Getty Images (AI and precision medicine concept image)
Source: Duke-Margolis Institute for Health Policy / FDA Workshop
The signal: A preprint study published August 25 in medRxiv evaluated 107 LLMs across 35 real-world clinical tasks, scanning 3,745 model-task pairs for stigmatizing language in the models' clinical reasoning. Lead team includes Harvard/MIT-affiliated researchers Leo Anthony Celi and David Bates.
The details:
84.06% of model-task pairs produced stigmatizing terms. Stigma rates ranged from 0% to 33.33% across models.
Reasoning models (designed for deeper chain-of-thought) generated more stigmatizing language than standard models: 2.35% vs. 1.70% average stigma rate. Medical-specific models performed no better than general-purpose ones.
19.76% of pairs actively amplified bias already present in the original patient notes. A correlation of -0.304 between stigma rate and task accuracy shows this bias directly degrades clinical reasoning quality. Targeted prompt engineering cut stigma rates by up to 91.91% without degrading performance.
Why it matters: Every health system deploying LLMs for clinical documentation is sitting on a documented bias amplification risk. The finding that reasoning models perform worse is counterintuitive and directly relevant, because those are exactly the models being positioned as the next upgrade in clinical AI. Guardrails work. Health systems need to require them before contract sign-off, not after an incident review.
PROVIDERS
3. Cityblock just bought rural care expertise it did not have, and $116M to deploy it.
Source: Fierce Healthcare / Homeward Health (Homeward mobile clinic press photo, 2026)
Source: Rock Health Digital Health Venture Investment Report
The signal: Cityblock Health signed a definitive agreement to acquire Homeward Health in an all-stock deal, pairing Cityblock's urban Medicaid AI platform with Homeward's rural Medicare Advantage expertise. Cityblock simultaneously closed a $116M Series E led by General Catalyst, bringing total raised to approximately $1 billion. The combined company targets 120 million Americans on government-funded insurance.
The details:
Cityblock, founded in 2017 out of Alphabet's Sidewalk Labs and led by CEO Toyin Ajayi, serves nearly 200,000 urban Medicaid and dual-eligible members across 10+ states. Annual revenue is $2.2 billion, up 77% year over year.
Homeward Health, founded in 2022 by Dr. Jennifer Schneider (former CMO and president of Livongo, the digital chronic care company) and Amar Kendale, has served 50,000 rural patients across 50 counties. It uses mobile care units, remote monitoring, and an AI-native platform built for rural Medicare Advantage.
The deal arrives as Medicaid faces spending cuts under the One Big Beautiful Bill Act and Medicare Advantage reimbursement tightens. CEO Ajayi's framing was direct: the answer to funding pressure is not to retreat, it is to get radically more efficient at delivering outcomes for government-funded populations.
Why it matters: Urban value-based care companies have historically avoided rural markets, where unit economics are harder and populations more complex. Cityblock just decided the complexity is the point. If Homeward's rural AI model and Cityblock's outcomes data can be combined into a single platform, this is one of the most serious bets yet that government-funded care can actually bend the cost curve.