We engage with clinical entrepreneurs and organizations to help them convert their medical AI concepts to marketable products or for real time medical use. Please contact us for our expertise and experience in this area.
Automated Image Interpretation and Screening Application
Automated or Semi-Automated Diagnosis Applications
Symptom Checker and Virtual Coaches
Prediction Model for Ambulatory Blood Pressure Monitoring
Four Machine learning (ML) methods - support vector machine, decision tree, random forest (RF) and extreme gradient boosting (XGBoost) – were used for accurate prediction of BP patterns. Missing data were imputed using K nearest neighbours. Models were developed in a record time and high quality to aid with use in clinical delivery and research.
AI-based system to detect morphological alterations of the condyle using CBCT scans
DenseNet network architecture provided in a Keras framework to detect morphological alterations of the condyle using CBCT scans. The AI-based system provides information about the condylar alteration of large volumes of CBCT scans within a short period of time. This enables researchers and governmental databases to gain epidemiological data about condylar abnormalities within a relatively short period of time
Medical Practitioner Encounter Transcriber
Developing a web application for the creation and management of transcribing the conversation between the medical practitioner and the patients. The application will listen to the conversation and automatically transform the voice into transcripts for the medical practitioner's review and approval using state-of-art technologies that support Natural Language Processing and Machine Learning.
Extracting insights from medical literature for Corona Virus Disease-2019 (COVID-19) using a biomedical natural language processing application
The emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in late 2019 not only led to the world-wide coronavirus disease 2019 (COVID-19) pandemic but also a deluge of biomedical literature. Following the release of the COVID-19 open research dataset (CORD-19) comprising over 45,000 scholarly articles, Medi-AI joined a multi-disciplinary team of data scientists, clinicians, medical researchers and software engineers to develop an innovative natural language processing (NLP) platform combining an advanced search engine with a biomedical named entity recognition extraction package. In particular, the platform was developed to extract and analyse information relating to clinical risk factors for COVID-19.
COVID-19 Algorithm Registry
Completion of a pilot to screen literature (non-systematic collection) for COVID-19 related algorithms using a customised checklist and then detailing select studies in a spreadsheet (registry). Based on this process, we reviewed over 80 manuscripts and selected 23 studies for inclusion in the registry.