AI Clinics on Mobile (AICOM): Universal AI Doctors for the Underserved and Hard-to-Reach

This paper introduces Artificial Intelligence Clinics on Mobile (AICOM), an open-source project devoted to answering the United Nations Sustainable Development Goal 3 (SDG3) on health, which represents a universal recognition that health is fundamental to human capital and social and economic development. The core motivation for the AICOM project is the fact that over 80% of the people in the least developed countries (LDCs) own a mobile phone, even though less than 40% of these people have internet access. Hence, through enabling AI-based disease diagnostics and screening capability on affordable mobile phones without connectivity will be a critical first step to addressing healthcare access problems. The technologies developed in the AICOM project achieve exactly this goal, and we have demonstrated the effectiveness of AICOM on monkeypox screening tasks. We plan to continue expanding and open-sourcing the AICOM platform, aiming for it to evolve into an universal AI doctor for the Underserved and Hard-to-Reach.

Markov Random Fields and Mass Spectra Discrimination

For mass spectra acquired from cancer patients by MALDI or SELDI techniques, automated discrimination between cancer types or stages has often been implemented by machine learnings. These techniques typically generate "black-box" classifiers, which are difficult to interpret biologically. We develop new and efficient signature discovery algorithms leading to interpretable signatures combining the discriminating power of explicitly selected small groups of biomarkers, identified by their m/z ratios. Our approach is based on rigorous stochastic modeling of "homogeneous" datasets of mass spectra by a versatile class of parameterized Markov Random Fields. We present detailed algorithms validated by precise theoretical results. We also outline the successful tests of our approach to generate efficient explicit signatures for six benchmark discrimination tasks, based on mass spectra acquired from colorectal cancer patients, as well as from ovarian cancer patients.