# Performance Assessment of ChatGPT vs Bard in Detecting Alzheimer's Dementia

arXiv: https://arxiv.org/abs/2402.01751 · HTML: https://mlsanity.com/paper/2402.01751/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 30 Jan 2024.

## Builds on (most cited first, 5 of 5)

- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Detecting cognitive decline using speech only: The ADReSSo Challenge](https://mlsanity.com/paper/2104.09356/) (2021)
- [Tab-CoT: Zero-shot Tabular Chain of Thought](https://mlsanity.com/paper/2305.17812/) (2023)
- [Alzheimer's Dementia Recognition Using Acoustic, Lexical, Disfluency and Speech Pause Features Robust to Noisy Inputs](https://mlsanity.com/paper/2106.15684/) (2021)
- [Alzheimer's Disease Detection from Spontaneous Speech through Combining Linguistic Complexity and (Dis)Fluency Features with Pretrained Language Models](https://mlsanity.com/paper/2106.08689/) (2021)

## Built on by (most cited first, 3 of 3)

- [National Institute on Aging PREPARE Challenge: Early Detection of Cognitive Impairment Using Speech -- The SpeechCARE Solution](https://mlsanity.com/paper/2511.08132/) (2025)
- [Dementia Insights: A Context-Based MultiModal Approach](https://mlsanity.com/paper/2503.01226/) (2025)
- [Explicit Knowledge-Guided In-Context Learning for Early Detection of Alzheimer's Disease](https://mlsanity.com/paper/2511.06215/) (2025)

## Abstract (arXiv)

Large language models (LLMs) find increasing applications in many fields. Here, three LLM chatbots (ChatGPT-3.5, ChatGPT-4 and Bard) are assessed - in their current form, as publicly available - for their ability to recognize Alzheimer's Dementia (AD) and Cognitively Normal (CN) individuals using textual input derived from spontaneous speech recordings. Zero-shot learning approach is used at two levels of independent queries, with the second query (chain-of-thought prompting) eliciting more detailed than the first. Each LLM chatbot's performance is evaluated on the prediction generated in terms of accuracy, sensitivity, specificity, precision and F1 score. LLM chatbots generated three-class outcome ("AD", "CN", or "Unsure"). When positively identifying AD, Bard produced highest true-positives (89% recall) and highest F1 score (71%), but tended to misidentify CN as AD, with high confidence (low "Unsure" rates); for positively identifying CN, GPT-4 resulted in the highest true-negatives at 56% and highest F1 score (62%), adopting a diplomatic stance (moderate "Unsure" rates). Overall, three LLM chatbots identify AD vs CN surpassing chance-levels but do not currently satisfy clinical application.