Authors:
Wouter Faber
1
;
Renske Bootsma
1
;
Tom Huibers
2
;
Sandra van Dulmen
3
and
Sjaak Brinkkemper
1
Affiliations:
1
Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands
;
2
Verticai, Utrecht, The Netherlands
;
3
Nivel: Netherlands institute for health services research, Utrecht, The Netherlands
Keyword(s):
Automated Medical Reporting, Accuracy Metric, SOAP Reporting, Composite Accuracy Score, GPT.
Abstract:
Generative Artificial Intelligence (AI) can be used to automatically generate medical reports based on transcripts of medical consultations. The aim is to reduce the administrative burden that healthcare professionals face. The accuracy of the generated reports needs to be established to ensure their correctness and usefulness. There are several metrics for measuring the accuracy of AI generated reports, but little work has been done towards the application of these metrics in medical reporting. A comparative experimentation of 10 accuracy metrics has been performed on AI generated medical reports against their corresponding General Practitioner’s (GP) medical reports concerning Otitis consultations. The number of missing, incorrect, and additional statements of the generated reports have been correlated with the metric scores. In addition, we introduce and define a Composite Accuracy Score which produces a single score for comparing the metrics within the field of automated medical
reporting. Findings show that based on the correlation study and the Composite Accuracy Score, the ROUGE-L and Word Mover’s Distance metrics are the preferred metrics, which is not in line with previous work. These findings help determine the accuracy of an AI generated medical report, which aids the development of systems that generate medical reports for GPs to reduce the administrative burden.
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