# AI4COVID-19: AI Enabled Preliminary Diagnosis for COVID-19 from Cough Samples via an App

arXiv: https://arxiv.org/abs/2004.01275 · HTML: https://mlsanity.com/paper/2004.01275/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2020.

## Built on by (most cited first, 30 of 48)

- [CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection](https://mlsanity.com/paper/2103.05094/) (2021)
- [Vocalsound: A Dataset for Improving Human Vocal Sounds Recognition](https://mlsanity.com/paper/2205.03433/) (2022)
- [Sounds of COVID-19: exploring realistic performance of audio-based digital testing](https://mlsanity.com/paper/2106.15523/) (2021)
- [Exploring Self-Supervised Representation Ensembles for COVID-19 Cough Classification](https://mlsanity.com/paper/2105.07566/) (2021)
- [A Generic Deep Learning Based Cough Analysis System from Clinically Validated Samples for Point-of-Need Covid-19 Test and Severity Levels](https://mlsanity.com/paper/2111.05895/) (2021)
- [Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers](https://mlsanity.com/paper/2212.08570/) (2022)
- [Biometrics in the Era of COVID-19: Challenges and Opportunities](https://mlsanity.com/paper/2102.09258/) (2021)
- [Statistical Perspectives on Reliability of Artificial Intelligence Systems](https://mlsanity.com/paper/2111.05391/) (2021)
- [COVID-19 Detection in Cough, Breath and Speech using Deep Transfer Learning and Bottleneck Features](https://mlsanity.com/paper/2104.02477/) (2021)
- [Virufy: A Multi-Branch Deep Learning Network for Automated Detection of COVID-19](https://mlsanity.com/paper/2103.01806/) (2021)
- [COVID-19 Modeling: A Review](https://mlsanity.com/paper/2104.12556/) (2021)
- [Project Achoo: A Practical Model and Application for COVID-19 Detection from Recordings of Breath, Voice, and Cough](https://mlsanity.com/paper/2107.10716/) (2021)
- [Uncertainty-Aware COVID-19 Detection from Imbalanced Sound Data](https://mlsanity.com/paper/2104.02005/) (2021)
- [Benchmarking Uncertainty Quantification on Biosignal Classification Tasks under Dataset Shift](https://mlsanity.com/paper/2112.09196/) (2021)
- [Evaluating the COVID-19 Identification ResNet (CIdeR) on the INTERSPEECH COVID-19 from Audio Challenges](https://mlsanity.com/paper/2107.14549/) (2021)
- [Exploring Longitudinal Cough, Breath, and Voice Data for COVID-19 Progression Prediction via Sequential Deep Learning: Model Development and Validation](https://mlsanity.com/paper/2201.01232/) (2022)
- [Towards using Cough for Respiratory Disease Diagnosis by leveraging Artificial Intelligence: A Survey](https://mlsanity.com/paper/2309.14383/) (2023)
- [Transfer Learning to Detect COVID-19 Coughs with Incremental Addition of Patient Coughs to Healthy People's Cough Detection Models](https://mlsanity.com/paper/2311.06707/) (2023)
- [COVID-19 Detection from Respiratory Sounds with Hierarchical Spectrogram Transformers](https://mlsanity.com/paper/2207.09529/) (2022)
- [Robust COVID-19 Detection from Cough Sounds using Deep Neural Decision Tree and Forest: A Comprehensive Cross-Datasets Evaluation](https://mlsanity.com/paper/2501.01117/) (2025)
- [End-2-End COVID-19 Detection from Breath & Cough Audio](https://mlsanity.com/paper/2102.08359/) (2021)
- [Deep Neural Network Based Respiratory Pathology Classification Using Cough Sounds](https://mlsanity.com/paper/2106.12174/) (2021)
- [A Summary of the ComParE COVID-19 Challenges](https://mlsanity.com/paper/2202.08981/) (2022)
- [Automatic Tuberculosis and COVID-19 cough classification using deep learning](https://mlsanity.com/paper/2205.05480/) (2022)
- [L3-Net Deep Audio Embeddings to Improve COVID-19 Detection from Smartphone Data](https://mlsanity.com/paper/2205.07682/) (2022)
- [Cough Classification using Few-Shot Learning](https://mlsanity.com/paper/2509.09515/) (2025)
- [Cough Detection Using Selected Informative Features from Audio Signals](https://mlsanity.com/paper/2108.03538/) (2021)
- [Automatic COVID-19 disease diagnosis using 1D convolutional neural network and augmentation with human respiratory sound based on parameters: cough, breath, and voice](https://mlsanity.com/paper/2112.07285/) (2021)
- [COVYT: Introducing the Coronavirus YouTube and TikTok speech dataset featuring the same speakers with and without infection](https://mlsanity.com/paper/2206.11045/) (2022)
- [Detection of Disease on Nasal Breath Sound by New Lightweight Architecture: Using COVID-19 as An Example](https://mlsanity.com/paper/2504.00730/) (2025)

## Abstract (arXiv)

Background: The inability to test at scale has become humanity's Achille's heel in the ongoing war against the COVID-19 pandemic. A scalable screening tool would be a game changer. Building on the prior work on cough-based diagnosis of respiratory diseases, we propose, develop and test an Artificial Intelligence (AI)-powered screening solution for COVID-19 infection that is deployable via a smartphone app. The app, named AI4COVID-19 records and sends three 3-second cough sounds to an AI engine running in the cloud, and returns a result within two minutes. Methods: Cough is a symptom of over thirty non-COVID-19 related medical conditions. This makes the diagnosis of a COVID-19 infection by cough alone an extremely challenging multidisciplinary problem. We address this problem by investigating the distinctness of pathomorphological alterations in the respiratory system induced by COVID-19 infection when compared to other respiratory infections. To overcome the COVID-19 cough training data shortage we exploit transfer learning. To reduce the misdiagnosis risk stemming from the complex dimensionality of the problem, we leverage a multi-pronged mediator centered risk-averse AI architecture. Results: Results show AI4COVID-19 can distinguish among COVID-19 coughs and several types of non-COVID-19 coughs. The accuracy is promising enough to encourage a large-scale collection of labeled cough data to gauge the generalization capability of AI4COVID-19. AI4COVID-19 is not a clinical grade testing tool. Instead, it offers a screening tool deployable anytime, anywhere, by anyone. It can also be a clinical decision assistance tool used to channel clinical-testing and treatment to those who need it the most, thereby saving more lives.