locater16 shares a report from IEEE Spectrum: One morning in 2019, Adebayo Alonge was in a Cape Town hotel room, preparing to demonstrate his startup's AI answer to a serious problem in African health care: counterfeit medication, which kills thousands of people across the continent every year. The RxScanner is a handheld spectrometer that scans a pill with infrared light, then sends the item's molecular profile to an AI model equipped with a pharmaceutical database. In seconds, the AI identifies the medication from its molecular profile -- or reports that it's phony.

Pharmacies were using the system in more than a dozen countries, including Ghana, Kenya, Myanmar, and Alonge's native Nigeria. But that morning in South Africa, it didn't work. "I was shocked," Alonge says... So Alonge immediately asked his engineers to shrink the AI model down to a smaller, low-power, unconnected version that could run entirely on his Android phone. They produced it 2 hours later, and that saved the demo. More importantly, the work birthed a new version of his device, which can authenticate a pill in places without broadband, computers, or even reliable electricity. It also turned Alonge into an advocate for this kind of "small AI." "The article goes on to detail other immediately useful 'small' AI applications without any subscription or billion dollar data centers needed," writes locator16. For example, Bala Murugan and colleagues at Vellore Institute of Technology in India developed a drone-based system that photographs cashew plants and identifies disease-indicating splotches on the plants. The key advantage is that all processing happens on the drone itself, so farmers do not need a computer, broadband connection, or cloud server access.

In a Uruguayan vineyard, researchers developed small-AI systems to identify ant infestations. The article doesn't go deep into the deployment details, but it presents this as another example of a narrow, localized model trained to recognize a specific agricultural threat. Small AI has also been used to detect the presence of malaria-carrying mosquitoes in multiple countries. This is especially useful in regions where public-health teams may lack reliable network access or expensive lab infrastructure, but still need fast, local detection.

In parts of Brazil without access to more complex medical equipment, researchers have used small AI to run electrocardiograms from an Arduino device. The article also describes Marcelo Jose Rovai's work on a TinyML model that generates electrocardiograms in a patient simulator lab. Rovai also describes a newer experiment using an Arduino UNO Q with a Qualcomm chipset. The device runs a language model locally, collects sensor data, and analyzes it to detect tiny pools of water where mosquitoes might breed -- while using only about 3 watts of power.