It’s so big a deal that GhanaWeb could not ignore seeing a photo of a young Ghanaian man with Bill Gates recently.
After a few calls, we got in touch with Darlington Akogo via WhatsApp, and he was kind enough to share the inspiration behind that photo.
“We have been working on AI for years now, since 2016/17 at minoHealth AI Labs and then KaraAgro AI. We’ve built AI system for radiology (https://www.minohealth.ai), microscopy, and several other fields.
“MinoHealth AI Labs is one of the Grand Challenges’ ‘Catalyzing Equitable Artificial Intelligence (AI) Use’ grants announced by the Bill & Melinda Gates Foundation. The funding will go towards our development of minoChat: a generative AI (multimodal large language model (LLM)) for Radiology. This is an AI system we have been working on for some few months now. We are building a foundation model for all of radiology! With the funding from the Gates Foundation, we will be fast tracking the development and releasing this AI system in just few months.
“The first features of our AI system will include it being able to take different medical imaging modalities (x-rays, mammograms, CT scans etc) and interpret it by screening and diagnosing it. It can then generate medical reports for patients based on these scans. It will also allow clinicians to have continuous dialogue (question and answers) about the medical image and patient. This is meant to eventually be a true well rounded AI super-radiologist that can support all clinicians around the world,” he said via text.
Darlington further explained how and why he got to take that photo with the global icon, Bill Gates.
“The Gates Foundation are excited about our work so they reached out and asked if I’m available to attend a private dinner with Bill Gates and the President of Global Health at the Bill & Melinda Gates Foundation, at Bill Gates residence. I agreed and attended,” he added.
About Darlington and MinoHealth
Darlington Akogo of MinoHealth AI Labs in Ghana will leverage a multimodal Large Language Model (LLM) to generate accurate and comprehensive medical reports based on the analysis of medical images to reduce the need for manual reports and enhance diagnostic capabilities for radiologists and clinicians.
African healthcare systems have excessively high patient-to-doctor ratios and prevalent diseases and severely inadequate numbers of radiologists.
They will fine-tune a multimodal LLM applied to radiology and medical imaging using a supervised approach with a labeled dataset of medical images and corresponding reports collected from facilities across Ghana and Africa.
The platform will enable interactive conversations with clinicians seeking answers to specific queries or clarifications regarding medical images.
They will use metrics and humans to evaluate the model and assess its ability to generate accurate and comprehensive medical reports.
They will also conduct field testing with clinicians and individuals from diverse demographics.
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