Access to quantitative, robust, and affordable diagnostic tools is essential to address the global burden of infectious diseases. While manual microscopy remains a cornerstone of diagnostic workflows due to its broad adaptability, it is labor-intensive and prone to human error. Recent advances in artificial intelligence (AI) and robotics offer opportunities to automate and enhance microscopy, enabling high-throughput, multi-disease diagnostics with minimal reliance on complex supply chains. However, current automated microscopy platforms are often costly and inflexible - barriers that are especially limiting in low-resource settings. Here we present Octopi 2.0, an open, highly configurable, general-purpose automated microscopy platform for a broad range of diagnostic applications, including sickle cell anemia and antibiotic resistance that we have reported recently. Applying Octopi to imaging malaria parasites with 4',6-diamidino-2-phenylindole (DAPI) staining, we discovered a spectral shift in fluorescence emission that allows rapid screening of blood smears at low magnification with throughput on the order of 1 million blood cells per minute. We further developed image processing and deep learning-based segmentation and classification pipelines to enable real-time processing for malaria diagnosis. For real-world performance validation, we collected a data set of 213 clinical samples from Uganda and the United States with a total of 905 million red blood cells and around 1.4 million malaria parasites. Using a ResNet-18 model and only one round of retraining, the model is able to achieve on average less than 5 false positive parasites/μL and a per-parasite level false negative rate of less than 8% in our test dataset. This per-cell performance implies a limit of detection (LoD) around 12 parasites/μL, and we measured patient-level performance of >97% specificity and sensitivity in our independent test data set of clinical samples from 73 patients/donors. As more data is collected in larger validation studies, we expect the robustness and performance of the model to continue to improve according to what we observe in our proof-of-concept experiments carried out in this study. With significant cost reduction in hardware compared to current automated microscopes and an open and versatile approach for tackling multiple diseases with standard glass slide-based sample preparation, we envision Octopi 2.0 to help enable the "app store" for equitable data-driven, AI-powered diagnostics of many diseases and conditions.

H.L and M.P are co-founders of Cephla Inc., and P.S. is a current employee of Cephla.

H.L. was supported by a Bio-X Stanford Interdisciplinary Graduate Fellowship. L.F.V. was supported by a Stanford Electrical Engineering department fellowship. This research was supported by HHMI-Gates Faculty Fellows Grant (M.P.), NIH New Innovator Award (M.P.), NSF Career Award (M.P.), NSF CCC (grant DBI-1548297), Schmidt Fellow (M.P.) and Moore Foundation.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Ethics committee/IRB of Uganda National Council of Science and Technology (HS 2700), the Makerere University School of Medicine Research and Ethics Committee (2019-134), the University of California, San Francisco Committee on Human Research (19-28606), and the Institutional Review Boards at Stanford University (52977) gave ethical approval for this work.

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I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

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The data for this study will be made publicly available before publication of the final version of the manuscript. Code, software and models are publicly available. The software and firmware for Octopi is available in https://github.com/hongquanli/octopi-research and the code for the graphical user interface (GUI) with real-time processing and identification of malaria parasites is available in https://github.com/octopi-project/octopi-malaria-gui/.