Artificial intelligence enabled audio-to-text transcription and translation for streamlined pharmacovigilance data...

Adverse drug reaction (ADR) reporting in pharmacovigilance is critical for patient safety but often limited by resource constraints and manual inefficiencies. The integration of artificial intelligence (AI) has the potential to address these challenges by streamlining the reporting process.

The aim of the study was to assess the performance of an AI-enabled system for audio-to-text transcription, translation, ADR form completion, and causality assessment based on the World Health Organization-Uppsala Monitoring Centre scale.

A computational comparative, cross-sectional study involving healthcare professionals and patients to evaluate the AI system's functionality in a real-world pharmacovigilance setting.

A hundred participants (50 healthcare professionals and 50 patients) provided audio-recorded ADR reports. These recordings were processed through the AI system to generate transcriptions, translations, and ADR forms. The system's performance was assessed using transcription metrics (word error rate [WER], character error rate [CER], Sentence Error Rate [SER]), translation metrics (bilingual evaluation understudy [BLEU] score, Translation Edit Rate [TER]), and ADR form accuracy. Causality assessments by the AI were compared against expert evaluations.

Descriptive and analytical statistics (unpaired t-test) were applied to evaluate the performance metrics and compare results between the two participant groups.

The AI system demonstrated high accuracy in transcription (WER <0.05, CER <0.04, and SER <0.35) and translation (BLEU >0.85 and TER <0.05). ADR form completion achieved near-perfect accuracy with minor discrepancies. Causality assessments were consistent across healthcare professional and patient data (P = 1).

The AI-enabled system effectively streamlined ADR reporting, ensuring accuracy in transcription, translation, and causality assessment while maintaining consistency across groups. Its integration into pharmacovigilance processes can reduce workloads, enhance reporting rates, and improve global health outcomes.