Did AI drafting save doctors time? In a 17-doctor trial, not clearly
Physicians editing AI drafts produced higher-rated simulated discharge documents in most quality domains, but the pilot found no statistically significant time difference. It tested six fictional ophthalmology records—not real patients, safety or outcomes.
The short version
- What happened
- Twenty-one Kyoto University Hospital physicians were randomized and 17 completed the pilot, creating discharge summaries and referrals from six simulated ophthalmology records.
- Who it affects
- For clinicians, an AI draft may shift work from writing to checking. This pilot suggests that active review can improve document quality, but it does not show that the total workload falls or that patients receive safer information.
- What remains unproven
- Single ophthalmology department at one Japanese hospital
Keep in mind
Single ophthalmology department at one Japanese hospital
21 physicians randomized and 17 completed the intervention
Six simulated patient records rather than real clinical encounters
No statistically significant time difference for either document type
No patient safety, understanding, follow-up or health outcome
Trial registered after completion
System developer involvement and disclosed related financial interests
Source analysis · 2 linked sources. Primary analysed source date: 26 October 2026. Earliest linked supporting source: 22 September 2025.
Read the full report · 3 min The evidence, methods, implications and limitations
Editing an AI draft changed quality more clearly than speed
Researchers at Kyoto University Hospital randomized 21 ophthalmology physicians, with 17 completing the trial. Each worked from six simulated patient records. One group edited drafts generated with Gemini 2.0 Flash-Lite through the CocktailAI system; the comparison group wrote from scratch using matched templates. Unedited model drafts formed a third, non-randomized reference arm.
For discharge summaries, AI-assisted editing was an estimated 59.4 seconds faster than clinician-only drafting, but the 95% confidence interval ranged from 150.6 seconds faster to 31.7 seconds slower (p=.18). For referrals, it was 52.9 seconds slower, with an interval from 7.0 seconds faster to 112.9 seconds slower (p=.08). Neither result established a time saving. Blinded experts nevertheless rated the edited AI drafts higher in most quality domains; unedited AI output was fastest and lowest quality.[1][2]
The pilot supports supervision, not an efficiency claim
The experiment used fictional records in one Japanese specialty, not live electronic records or actual discharge decisions. It did not measure corrections after handover, harmful omissions, patient understanding, clinician burnout or health outcomes. Four randomized physicians did not complete the intervention, and the trial was registered after it finished, although the authors say the protocol and analysis plan were fixed before enrolment.
Fitting Cloud developed the tested system and paid its Gemini API fee. A company board member and employee were co-authors, and several authors disclosed related grants or honoraria; the paper says the company had no organisational role in study conduct or publication decisions. A larger, prospectively registered multi-centre trial using real workflows and downstream error checks would be needed before hospitals could treat this as evidence of saved time or safer care.[1][2]
The distinction that matters
What changed when physicians edited an AI draft
The randomized comparison separated document type, time and expert-rated quality; it did not test patient outcomes.
Summaries
Physicians edited AI drafts rather than starting from a template
59.4 seconds faster; 95% CI crossed no difference
Referrals
The same workflow produced referral letters
52.9 seconds slower; 95% CI crossed no difference
Quality
Blinded experts rated completed documents
Edited drafts led in most domains; raw AI drafts ranked lowest