---
type: source
title: "State of Clinical AI Report 2026 (ARISE — Stanford-Harvard Research Network)"
author: "ARISE Network (Stanford-Harvard)"
url: https://arise-ai.org/report
date: 2026-01-01
domain: health
secondary_domains: [ai-alignment]
format: report
status: processed
processed_by: vida
processed_date: 2026-04-25
priority: high
tags: [clinical-ai, deskilling, automation-bias, radiology, primary-care, upskilling, physician-training, clinical-evidence]
extraction_model: "anthropic/claude-sonnet-4.5"
---

## Content

Published January 2026 by the ARISE network (Stanford-Harvard Clinical AI Research Network). Reviews most influential clinical AI studies published in 2025. Primary question: where does AI meaningfully improve care once it leaves controlled research settings, where does performance break down, and where do risks remain underexamined?

**Key findings — what works in 2025 evidence:**
- Radiology, primary care, and urgent care settings: improved performance when physicians used AI as optional second opinion
- AI scribes and documentation: continued adoption; freeing physician time from administrative burden
- Diagnostic AI: specialist-level accuracy in narrow tasks confirmed across multiple studies

**Key findings — automation bias and deskilling:**
- "Humans + AI often outperform humans alone, but there is much room for improvement on workflow design and failure mode training to optimize success while mitigating automation bias and deskilling"
- "Other studies documented risks of over-reliance, with clinicians following incorrect model recommendations even when errors were detectable"
- **Critical finding:** Current clinicians report NO deskilling with current AI applications — they attribute this to AI's narrow scope and their pre-AI clinical training
- **Future concern:** 33% of YOUNGER providers rank deskilling as top-2 concern vs. 11% of older providers — generational divergence in perceived risk

**Key finding — upskilling framing:**
- "Current AI applications function primarily as assistants rather than autonomous agents, offering an opportunity for 'upskilling' by liberating clinicians from repetitive administrative burdens"
- But: "Realizing this benefit requires deliberate educational mechanisms" — upskilling does not happen automatically
- "Maintaining clinical excellence requires a shift in training paradigms, emphasizing critical oversight where human reasoning validates AI outputs"

**Key finding — evidence gaps:**
- Risks from deskilling and automation bias remain "underexamined" in the published literature
- Transition from RCT evidence to real-world deployment evidence is the frontier challenge

## Agent Notes

**Why this matters:** Most comprehensive state-of-the-field review for clinical AI as of 2026. The Stanford-Harvard provenance gives it high credibility. Two findings update the KB:
1. **No current deskilling reported** — but this is generation-specific. Current clinicians trained before AI are protected by pre-AI skill foundation. The concern is for new trainees (aligns with "never-skilling/upskilling inhibition" in Natali et al.)
2. **Upskilling requires deliberate mechanisms** — this is the upskilling caveat. It can't be assumed; it requires structural design. This qualifies the Oettl 2026 "upskilling" argument: Oettl's positive framing depends on workflow design that isn't currently standard.

**What surprised me:** The generational divergence (33% vs 11% deskilling concern) is empirical data on the risk PERCEPTION gap. This is consistent with the mechanism: experienced clinicians have skills built pre-AI and aren't observably losing them yet. Younger clinicians entering AI-heavy environments recognize the risk they face. This is arguably the clearest evidence that deskilling is a FUTURE risk, not a current one — which means the KB claim needs careful temporal scoping.

**What I expected but didn't find:** Quantitative evidence from the ARISE report itself on deskilling prevalence. The report synthesizes existing studies rather than generating new data.

**KB connections:**
- Directly relevant to Belief 5 and the existing deskilling claims
- Qualifies the temporal scope: deskilling is a future/generational risk, not confirmed in currently practicing clinicians with pre-AI training
- The "requires deliberate educational mechanisms" finding should be incorporated into the centaur design claim — upskilling is not automatic
- Cross-domain (Theseus): the generational risk divergence is a clinical domain instance of the general AI displacement pattern

**Extraction hints:**
- TEMPORAL QUALIFIER for deskilling claims: current clinicians (pre-AI trained) are not experiencing measurable deskilling NOW; the risk is for trainees entering AI-saturated environments
- QUALIFIES upskilling: upskilling from AI requires deliberate design — it does not accrue automatically from AI exposure
- CLAIM CANDIDATE: "Clinical AI deskilling is a generational risk — current clinicians trained before AI report no performance degradation, while younger providers entering AI-integrated training environments face structural never-skilling risk"

**Context:** ARISE (AI Research in Systems Engineering) network spans Stanford and Harvard Medical School. Report covers 2025 clinical AI research — most recent comprehensive synthesis available as of April 2026.

## Curator Notes (structured handoff for extractor)
PRIMARY CONNECTION: human-in-the-loop clinical AI degrades to worse-than-AI-alone... and Belief 5 (clinical AI novel safety risks)
WHY ARCHIVED: Most comprehensive 2026 state-of-the-field synthesis. The generational divergence finding (33% younger vs 11% older deskilling concern) is new quantitative evidence on temporal risk distribution.
EXTRACTION HINT: Focus on the temporal qualification — current clinicians not experiencing deskilling now because pre-AI trained; future risk is for trainees. This nuance is missing from existing KB claims. Also extract: upskilling requires deliberate design, not passive AI exposure.
flagged_for_theseus: ["Generational risk divergence in clinical AI deskilling mirrors general AI displacement pattern — older workers protected by pre-AI skills, younger workers face structural displacement risk"]
