Engineers at Stanford University have developed an affordable, battery- and solar-powered microscope with built-in artificial intelligence that can automatically diagnose malaria in blood samples—transforming a process once reliant on slow, manual examination by technicians. The device, named Octopi, is designed for use in remote, off-grid areas where malaria is most prevalent and healthcare infrastructure is limited. Currently, diagnosing malaria involves a technician manually scanning slides under a microscope for hours, analyzing one sample every 30 minutes. This limits a worker to about 25 diagnoses per day. Octopi, in contrast, can scan 1 million blood cells per minute—100 times faster than traditional methods—and detect as few as 12 infected cells per microliter of blood with near 100% accuracy. It also provides quantitative data on infection severity, which is critical for determining treatment and saving lives. Malaria kills around 600,000 people annually, mostly children in sub-Saharan Africa. Millions more carry the parasite without symptoms, unknowingly spreading it through mosquitoes. Early and accurate diagnosis is essential to prevent progression to severe, life-threatening forms of the disease. Octopi’s design prioritizes affordability and accessibility. It uses low-cost optics instead of expensive glass lenses, reducing the cost to about $1,000 per unit—far below the $100,000 or more for current robotic microscopes. The device operates without electricity or internet, powered by rechargeable batteries or solar panels. The key innovation lies in detecting a subtle spectral shift when infected blood cells are illuminated with ultraviolet light. This optical signature is easily captured with inexpensive lenses and processed by AI to identify and count infected cells. The concept draws from astronomy, where similar techniques are used to analyze distant stars. A standout feature of Octopi is its open software architecture, which allows users worldwide to train the device to detect different diseases without changing hardware. The team has already adapted it to identify all four types of sickle cell anemia in Nepal and tuberculosis in other regions. The vision is to create a universal diagnostic platform—akin to an app store—where health workers can develop and share models for hundreds of diseases, including sexually transmitted infections, leishmaniasis, and schistosomiasis. To support this, the team has also created Inkwell, a low-cost, 3D-printable device that automates the preparation of blood smears. Inkwell uses capillary action to produce a perfect, consistent thin layer of blood on a slide—critical for accurate imaging—without electricity and for less than $5 in materials. It has already been tested in over 15 countries. The researchers are now raising funds to launch ODION, the Open Diagnostic Imaging Observatory Network, a global initiative that empowers healthcare workers in the Global South to collect data, train AI models, and build diagnostic tools tailored to local needs. With 260 million malaria cases reported each year—and the potential to reach half a billion without improved diagnostics—Octopi could be a game-changer. Prakash estimates that 1,000 units could process 50 million slides annually. With 10,000 units deployed worldwide, the impact could be transformative, bringing the world closer to eradicating malaria.