Table 5 Characteristics of sampled grey literature.
Name of AI tool | Description | Country | Main user group | AI use category | AI type employed |
|---|---|---|---|---|---|
Disease outbreak intelligence platform65 | Disease outbreak prediction and real-time disease risk assessment for COVID-19 | ASEAN countries66 | Public health practitioners | Population health | Natural language processing; machine learning |
Medical robot assistants67 | Service delivery robots in hospitals to improve patient care | China | Non-physician healthcare workers | Healthcare delivery | Robotics |
Computer assisted diagnostic (CAD) radiology tool for COVID-19 and other conditions to aid physicians | China | Physicians | Clinical decision support | Computer vision using deep learning | |
Close contact catcher70 | Population surveillance identifying close contact between individuals | China | Public health practitioners | Population health | Computer vision using deep learning |
Deep learning - fractional flow reserve derived from coronary CT angiography71 | Automated non-invasive physiological functional assessment of coronary arteries using coronary CT angiograms as an alternative to invasive coronary angiography | China | Physicians | Clinical decision support | Computer vision using deep learning |
Diabetes risk prediction tool72 | Predictive tool for individual users to identify their risk of diabetes, and to promote early diagnosis | China | Individuals/patients | Population health | Machine learning |
Robotic COVID-19 case monitoring73 | Automated screening calls and follow-up calls, performed by voice robots, in order to reduce call centre workloads | China | Non-physician healthcare workers | Frontline health worker virtual assistant | Robotics |
Intelligent triage74 | Platform for patients to consult with ‘AI Doctor’ in order to facilitate access to relevant medical information and possible diagnoses, and to find a suitable doctor | China | Individuals/patients | Patient virtual assistant | Natural language processing; machine learning |
Intelligent hospitals75 | Integration of multiple AI services, including speech input medical records, CAD systems, and AI-driven follow up | China | Physicians; non-physician healthcare workers | Frontline health worker virtual assistant; clinical decision support | Natural language processing; computer vision using deep learning; machine learning; expert systems |
Drone delivery of medical supplies and samples to hospitals | China, Dominican Republic, Rwanda, Madagascar, Malawi, Senegal | Healthcare providers | Healthcare delivery | Robotics | |
Computer Assisted Diagnostic tool diagnosing diabetic retinopathy using hospital retinal imaging to ease physician workloads | China, India | Physicians; non-physician healthcare workers | Clinical decision support | Computer vision using deep learning | |
RAD-AID AI radiology81 | Capacity building and implementation of CAD radiology tools to ease workloads of radiologists in low-resource settings | Multiple Latin American, Asian and African Countries | Physicians; non-physician healthcare workers | Clinical decision support | Computer vision using deep learning |
Automated whole slide imaging and histological diagnostics82 | Automated whole slide imaging using conventional microscopes and smartphone, and AI histology diagnostic assistant, for diagnostics in low-resource settings | Mexico, Tanzania, India | Physicians; non-physician healthcare workers | Frontline health worker virtual assistant; clinical decision support | Computer vision using deep learning |
Automated malaria diagnostics83 | Web-based platform for diagnosing malaria with thick blood smear images to strengthen laboratories | Uganda | Physicians; non-physician healthcare workers | Frontline health worker virtual assistant | Computer vision using deep learning |
App and web-based chatbots automating triage and self-directed care for patients | Brazil, China, India, Tanzania | Individuals/patients | Patient virtual assistant | Natural language processing; machine learning | |
Predictive tool for healthcare providers to identify HIV patients at risk of being lost to follow up, promoting proactive intervention | South Africa | Healthcare providers | Population health | Machine learning | |
Tailored healthcare worker training90 | Identifying recurrent errors by health workers and proposing AI tailored training modules via digital platform | Burkina Faso | Non-physician healthcare workers | Personnel management | Machine learning |
Perinatal asphyxia computer aided diagnostics91 | CAD tool used by healthcare workers to detect early signs of perinatal asphyxia using recordings of newborn cry sounds | Nigeria | Non-physician healthcare workers | Frontline health worker virtual assistant | Computer audition using deep learning |
