AI in hospitals and care: what 287 records show

Every measured gain and problem on the record for this topic, newest first. Updated November 11, 2026.

The count287 counted · 128 context

The records

AI in hospitals and care

What 287 records show

The records

AI in hospitals and care: what 287 records show · Truvace

Where the evidence stands

The short version

Most of the evidence is chatbots tested on written questions and small single-site studies; a few randomized trials and reviews carry more weight. TRV-2026-0355 TRV-2026-1613 TRV-2026-2032 Chatbots often match or beat doctors on structured written cases, while a 7294-patient trial of an AI alert in live care found no drop in delirium. TRV-2026-1797 TRV-2026-1664

What is well measured

A randomized preclinical trial of 77 surgeons watching laparoscopic video shows AI assistance cut the time to find a bleeding site from 3.23 to 2.53 seconds on average. TRV-2026-2032 In a randomized crossover study, clinical trial staff using AI spent 2.71 fewer minutes per item reviewing charts for immunotherapy side effects, though severity grading was much weaker than event detection. TRV-2026-2318 Three randomized trials with over 90,000 US patients show that messages on personal high flu risk raised vaccination 1.7-3.5 percentage points over no message. TRV-2026-1852 A randomized trial of 130 final-year nursing students found AI-adaptive simulation raised leadership scores more than standard training, under controlled simulation conditions. TRV-2026-1262

Where it falls short

In a test of 960 responses to clinician-written cases, ChatGPT Health undertriaged 52% of true emergencies, sending diabetic ketoacidosis and impending respiratory failure to 24-48 hour evaluation. TRV-2026-0181 A randomized trial of 7294 patients at Mount Sinai found an AI-triggered alert did not reduce delirium (35.1% vs 32.7%), though only 17.2% had a documented delirium assessment. TRV-2026-1664 In 16,254 responses to 29 standardized vignettes, all 21 chatbots failed differential diagnosis at rates above 0.80 even though final-diagnosis accuracy was high, a benchmark study found. TRV-2026-0145 A test of eight speech-to-text systems found at least one clinically significant error in 30% to 68% of 800 transcripts, and 39.6% of 53 errors with potential for severe harm involved medication type or dose. TRV-2026-1687

Where the evidence is thin

A review of 314 FDA-cleared surgical AI devices found only 5.1% had higher-quality prospective evidence. TRV-2026-1693 A scoping review of 1181 reports on AI decision tools found 62.7% stopped at clinical validation and 14.4% evaluated implementation or impact. TRV-2026-1613 A scoping review found only seven eligible studies of agentic AI in healthcare, with just one trial involving patients. TRV-2026-0166 A review of 310 publications found no study that measured AI's effect on critical thinking or clinical reasoning in medical education. TRV-2026-0458

What is disputed

Studies point in different directions on whether AI scribes ease clinician burnout. TRV-2026-0478 TRV-2026-0330 An observational study of 263 clinicians in six US health systems reports that self-reported burnout fell from 51.9% to 38.8% after 30 days with an AI scribe. TRV-2026-0478 A systematic review of 6 real-world studies found clinicians reported less documentation time, but burnout on standardized scales was unaffected. TRV-2026-0330 At one cancer center, 190 of 958 clinicians used the AI scribe; after-hours time fell only from 86.9 to 81.4 minutes a week. TRV-2026-1819

The main threads

Chatbot answers to patient questions
55 gains · 63 problems
Most tests find chatbot answers to patient questions accurate on average, yet poor readability and occasional unsafe answers recur. TRV-2026-1856 TRV-2026-2452 TRV-2026-1746 TRV-2026-1715
See these 79 records
Diagnosis and triage
20 gains · 13 problems
Chatbots beat doctors on structured written cases, but results on triage and differential diagnosis are mixed. TRV-2026-1797 TRV-2026-0181 TRV-2026-0145 TRV-2026-1421
See these 26 records
AI scribes and documentation
12 gains · 8 problems
AI scribes cut documentation time for many clinicians, though evidence on burnout and adoption is mixed. TRV-2026-1897 TRV-2026-0478 TRV-2026-0330

The count

Gains: 99
Traces: 87
Problems: 101

A Trace is one record with both a gain and a problem, so it counts on both sides: 186 records show a gain and 188 show a problem. 128 context records are in the record list but not counted, because they report no measured gain or problem.

How strong the evidence is

Evidence	Gain side	Problem side
HighStrong study design, backed by at least two independent sources	3	3
ModerateA measured study or an official source	113	107
EmergingEarly, small, unreviewed or reported second-hand	70	78

By type of evidence: 95 benchmark or lab tests; 43 observational studies; 42 controlled comparisons; 33 commentaries or narrative reviews; 29 surveys; 15 interview studies; 9 systematic reviews or meta-analyses; 9 randomized trials; 4 case reports; 3 investigations by reporters; 2 news reports; 2 regulatory or legal actions. How evidence is scored

Strongest gains

French online study of 942 patient histories found AI recorded them better than GPs
In an online French study of 204 GPs and 942 patient histories, AI recorded histories more effectively than doctors, most clearly for older patients and for allergy and family cancer history.
Controlled or validated comparison · Evidence: High · 92/100 · TRV-2026-0518
In China, hypertension control rose from 68% to 82% with AI care; a 12,468-patient study found
In an observational study of 12,468 chronic disease patients in three Chinese provinces, AI-enabled care was linked to hypertension control targets met rising from 68% to 82%, with health data accuracy above 95%.
Observational study · Evidence: High · 89/100 · TRV-2026-0328
Clinicians said AI scribes cut paperwork time, a 6-study review found; burnout did not change
Clinicians using AI note-taking scribes in 6 real-world studies reported spending less time on documentation, though the evidence is sparse and based on self-reports.
Systematic review or meta-analysis · Evidence: High · 88/100 · TRV-2026-0330

Strongest problems

Doctors' exam and diagnosis skills may erode with AI decision tools, a literature review finds
A literature review linked AI decision support to vulnerabilities in doctors' physical examination, differential diagnosis, clinical judgment and patient communication skills, though magnitude and reversibility remain uncertain.
Systematic review or meta-analysis · Evidence: High · 88/100 · TRV-2026-0490
Every one of 21 AI chatbots failed to build a proper differential diagnosis in over 80% of cases
In 16,254 responses to 29 standardized clinical vignettes, all 21 AI chatbots tested failed differential diagnosis at rates above 0.80, even though final-diagnosis accuracy was high.

What reviews found when they combined studies

One review in this topic printed a pooled result: one figure calculated across many studies. They are the reviews’ own numbers, checked word for word against their abstracts. They often measure different things, so they are listed side by side, not added up.

AI predicts gallbladder surgery difficulty well, but 16 of 18 studies were at high risk of bias · TRV-2026-0787
- Pooled AUC of AI validation models for predicting difficulty of laparoscopic cholecystectomy
  AUC 0.818 (95% CI 0.797 to 0.840)
  18 studies · search to March 2, 2026
  The review’s own words
  For 32 validation models, the pooled AUC was 0.818 (95% CI, 0.797-0.840).
- Pooled AUC of AI training models for predicting difficulty of laparoscopic cholecystectomy
  AUC 0.848 (95% CI 0.829 to 0.868)
  18 studies · search to March 2, 2026
  The review’s own words
  The pooled AUC for 27 training models was 0.848 (95% CI, 0.829-0.868).