# Automated Clinical Report Generation for Remote Cognitive Remediation: Comparing Knowledge-Engineered Templates and LLMs in Low-Resource Settings

arXiv: https://arxiv.org/abs/2605.06594 · HTML: https://mlsanity.com/paper/2605.06594/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 39 references that are in the map.
- First posted on arXiv 7 May 2026.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Large Language Models](https://mlsanity.com/paper/2307.05782/) (2023)
- [Instruction Tuning for Large Language Models: A Survey](https://mlsanity.com/paper/2308.10792/) (2023)
- [Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation](https://mlsanity.com/paper/1703.09902/) (2017)
- [A Review of Automated Speech and Language Features for Assessment of Cognitive and Thought Disorders](https://mlsanity.com/paper/1906.01157/) (2019)
- [Making effective use of healthcare data using data-to-text technology](https://mlsanity.com/paper/1808.03507/) (2018)
- [Towards Adapting Open-Source Large Language Models for Expert-Level Clinical Note Generation](https://mlsanity.com/paper/2405.00715/) (2024)
- [Consultation Checklists: Standardising the Human Evaluation of Medical Note Generation](https://mlsanity.com/paper/2211.09455/) (2022)
- [The Impact of ChatGPT and LLMs on Medical Imaging Stakeholders: Perspectives and Use Cases](https://mlsanity.com/paper/2306.06767/) (2023)
- [PSentScore: Evaluating Sentiment Polarity in Dialogue Summarization](https://mlsanity.com/paper/2307.12371/) (2023)
- [Can GPT models Follow Human Summarization Guidelines? A Study for Targeted Communication Goals](https://mlsanity.com/paper/2310.16810/) (2023)
- [Natural Language Generation in Healthcare: A Review of Methods and Applications](https://mlsanity.com/paper/2505.04073/) (2025)
- [THERADIA WoZ: An Ecological Corpus for Appraisal-based Affect Research in Healthcare](https://mlsanity.com/paper/2405.06728/) (2024)

## Abstract (arXiv)

The growing demand for cognitive remediation therapy, combined with limited speech therapist availability, has accelerated the adoption of remote rehabilitation tools. These systems generate large volumes of interaction data that are difficult for clinicians to review efficiently. This paper investigates automated clinical report generation for avatar-guided, home-based cognitive remediation sessions in a low-resource setting with no reference reports. We present and compare two approaches: (1) a rule-based template system encoding speech therapy domain knowledge as explicit decision rules and validated templates, ensuring clinical reliability and traceability; and (2) a zero-shot LLM-based approach (GPT-4) aimed at more fluent and concise output. Both systems use identical pre-extracted, expert-validated structured variables, enabling a controlled factual comparison. Outputs were evaluated by eight speech therapists and final-year students using a nine-criterion questionnaire. Results reveal a clear trade-off between clinical reliability and linguistic quality. The template-based system scored higher on fluidity, coherence, and results presentation, while GPT-4 produced more concise output. Directional differences are consistent across evaluation dimensions, though no comparison reached statistical significance after correction, reflecting the scale constraints of expert clinical evaluation. Based on evaluator feedback, we derive eight design recommendations for clinical reporting systems in remote rehabilitation settings. More broadly, this work contributes a replicable methodology combining expert elicitation, taxonomy-driven generation, and multi-dimensional human evaluation for clinical NLG in low-resource settings, and illustrates how controlled comparisons can inform the responsible adoption of generative AI in healthcare.