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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.12240 |
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| _version_ | 1866917144641404928 |
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| author | Mustafa, Maryam Ammara, Umme Shahnawaz, Amna Abrar, Moaiz Ahtisham, Bakhtawar Qurashi, Fozia Umber Shahin, Mostafa Ahmed, Beena |
| author_facet | Mustafa, Maryam Ammara, Umme Shahnawaz, Amna Abrar, Moaiz Ahtisham, Bakhtawar Qurashi, Fozia Umber Shahin, Mostafa Ahmed, Beena |
| contents | We present the design, implementation, and in-situ deployment of a smartphone-based voice-enabled AI system for generating electronic medical records (EMRs) and clinical risk alerts in maternal healthcare settings. Targeted at low-resource environments such as Pakistan, the system integrates a fine-tuned, multilingual automatic speech recognition (ASR) model and a prompt-engineered large language model (LLM) to enable healthcare workers to engage naturally in Urdu, their native language, regardless of literacy or technical background. Through speech-based input and localized understanding, the system generates structured EMRs and flags critical maternal health risks. Over a seven-month deployment in a not-for-profit hospital, the system supported the creation of over 500 EMRs and flagged over 300 potential clinical risks. We evaluate the system's performance across speech recognition accuracy, EMR field-level correctness, and clinical relevance of AI-generated red flags. Our results demonstrate that speech based AI interfaces, can be effectively adapted to real-world healthcare settings, especially in low-resource settings, when combined with structured input design, contextual medical dictionaries, and clinician-in-the-loop feedback loops. We discuss generalizable design principles for deploying voice-based mobile healthcare AI support systems in linguistically and infrastructurally constrained settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_12240 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | System X: A Mobile Voice-Based AI System for EMR Generation and Clinical Decision Support in Low-Resource Maternal Healthcare Mustafa, Maryam Ammara, Umme Shahnawaz, Amna Abrar, Moaiz Ahtisham, Bakhtawar Qurashi, Fozia Umber Shahin, Mostafa Ahmed, Beena Human-Computer Interaction We present the design, implementation, and in-situ deployment of a smartphone-based voice-enabled AI system for generating electronic medical records (EMRs) and clinical risk alerts in maternal healthcare settings. Targeted at low-resource environments such as Pakistan, the system integrates a fine-tuned, multilingual automatic speech recognition (ASR) model and a prompt-engineered large language model (LLM) to enable healthcare workers to engage naturally in Urdu, their native language, regardless of literacy or technical background. Through speech-based input and localized understanding, the system generates structured EMRs and flags critical maternal health risks. Over a seven-month deployment in a not-for-profit hospital, the system supported the creation of over 500 EMRs and flagged over 300 potential clinical risks. We evaluate the system's performance across speech recognition accuracy, EMR field-level correctness, and clinical relevance of AI-generated red flags. Our results demonstrate that speech based AI interfaces, can be effectively adapted to real-world healthcare settings, especially in low-resource settings, when combined with structured input design, contextual medical dictionaries, and clinician-in-the-loop feedback loops. We discuss generalizable design principles for deploying voice-based mobile healthcare AI support systems in linguistically and infrastructurally constrained settings. |
| title | System X: A Mobile Voice-Based AI System for EMR Generation and Clinical Decision Support in Low-Resource Maternal Healthcare |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2512.12240 |