# Hi Sigma, do I have the Coronavirus?: Call for a New Artificial Intelligence Approach to Support Health Care Professionals Dealing With The COVID-19 Pandemic

arXiv: https://arxiv.org/abs/2004.06510 · HTML: https://mlsanity.com/paper/2004.06510/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2020.

## Built on by (most cited first, 7 of 7)

- [Sounds of COVID-19: exploring realistic performance of audio-based digital testing](https://mlsanity.com/paper/2106.15523/) (2021)
- [Machine learning on small size samples: A synthetic knowledge synthesis](https://mlsanity.com/paper/2103.01002/) (2021)
- [L3-Net Deep Audio Embeddings to Improve COVID-19 Detection from Smartphone Data](https://mlsanity.com/paper/2205.07682/) (2022)
- [Voice EHR: Introducing Multimodal Audio Data for Health](https://mlsanity.com/paper/2404.01620/) (2024)
- [Telehealthcare and Telepathology in Pandemic: A Noninvasive, Low-Cost Micro-Invasive and Multimodal Real-Time Online Application for Early Diagnosis of COVID-19 Infection](https://mlsanity.com/paper/2109.07846/) (2021)
- [An Artificial Intelligence Browser Architecture (AIBA) For Our Kind and Others: A Voice Name System Speech implementation with two warrants, Wake Neutrality and Value Preservation of Personally Identifiable Information](https://mlsanity.com/paper/2203.16497/) (2022)
- [Transfer Learning for the Efficient Detection of COVID-19 from Smartphone Audio Data](https://mlsanity.com/paper/2307.02975/) (2023)

## Abstract (arXiv)

Just like your phone can detect what song is playing in crowded spaces, we show that Artificial Intelligence transfer learning algorithms trained on cough phone recordings results in diagnostic tests for COVID-19. To gain adoption by the health care community, we plan to validate our results in a clinical trial and three other venues in Mexico, Spain and the USA . However, if we had data from other on-going clinical trials and volunteers, we may do much more. For example, for confirmed stay-at-home COVID-19 patients, a longitudinal audio test could be developed to determine contact-with-hospital recommendations, and for the most critical COVID-19 patients a success ratio forecast test, including patient clinical data, to prioritize ICU allocation. As a challenge to the engineering community and in the context of our clinical trial, the authors suggest distributing cough recordings daily, hoping other trials and crowdsourcing users will contribute more data. Previous approaches to complex AI tasks have either used a static dataset or were private efforts led by large corporations. All existing COVID-19 trials published also follow this paradigm. Instead, we suggest a novel open collective approach to large-scale real-time health care AI. We will be posting updates at https://opensigma.mit.edu. Our personal view is that our approach is the right one for large scale pandemics, and therefore is here to stay - will you join?