xRead - Incorporating Artificial Intelligence into Clinical Practice (March 2026)

Ng et al. BMC Medical Informatics and Decision Making

(2025) 25:236

Page 11 of 24

Key Findings Novel Features

The study noted that the computer generated HPIs were more complete and organized but still required improvements in accuracy and consistency, especially for detailed patient histories. Further development was needed for

seamless integration of AEGIS with EHRs to ensure that computer-generated

HPIs align well with real-time physician documentation workflows, which are

sensitive to time constraints and accuracy demands​.

More advanced algorithms necessary to improve the accuracy of detecting and

documenting GI symptoms. Specifically, machine learning models could be further developed to ensure the automatic detec tion of all relevant clinical features. Current systems may miss certain

symptoms due to variability in patient language; suggest enhancing systems

to handle diverse phrasing, which could prevent underreporting by AEGIS.

Blinded raters deemed the computer generated HPIs to be of higher

quality, more comprehensive, better organized, and with greater relevance compared to physician-documented

HPIs. These results offer initial proof-of principle that a computer can create

meaningful and clinically relevant HPI.

Computer generated HPIs document ed more alarm features than physician generated HPIs. Physicians may be

under reporting alarm features in GI clinics. Yet, greater documentation

of red flags is not shown to improve patient outcomes.

AI Transcription

Proficiency (paper

specific outcomes)

NR Mean of Physician HPI Ratings (SD) 2.80 (0.75) 2.73 (0.75) 3.04 (0.68)

2.80 (0.80)

3.17 (0.60)

2.97 (0.79)

5.27 (1.52)

Mean of Computer

generated HPI Ratings (SD) 3.68 (0.61)* 3.70 (0.59)* 3.82 (0.54)*

3.66 (0.63)*

3.55 (0.69)*

3.66 (0.66)*

6.05 (0.98)*

NR Median number of

positive alarm fea

tures in Physician HPIs (interquartile range) 0 (0–1) 0 (0–1) 0 (0–1)

Median number

of positive alarm

features in AEGIS HPIs (interquartile range) 1 (0–2)* 1 (0–2)* 1 (0–2)*

Metric (F1 score,

Precision, Recall, WER)

impression

- Completeness - Relevance

- Organisation

- Succinctness

- Comprehensi bility

- Number of

Medicare-recom

mended elements present in HPI

- Patients present ing for an initial visit

- Patients who

completed AEGIS

within 1 week of their clinic visit

Comparator Type Subcategories Performance Blinded comparison - Overall

Blinded comparison - All patients

Standard

Physician gener ated History

of Presenting Illness (HPI)

Number

of positive

alarm features

documented in

physician gener ated History

of Presenting Illness (HPI)

Table 2 (continued)

Study Reference Alma rio et

al., 2015 [13]

Alma

rio et

al., 2015​ [14]