HealthTech
Healthcare technology is not simply another software category. Clinical systems operate in environments where reliability, privacy, interoperability and usability directly affect healthcare delivery.
Diagnostic imaging is one of the most data-intensive areas of healthcare. I work with the technologies that allow imaging equipment and clinical systems to communicate.
AI has significant potential in healthcare, but high model accuracy alone is not enough. Clinical AI also requires dataset quality, population representation, explainability, workflow integration and human oversight.
AI development depends on data infrastructure — collection, de-identification, annotation, governance, provenance and secure access.
Healthcare increasingly connects software with physical systems. My broader Biomedical Engineering interests include sensors, embedded systems and edge computing.
Responsibility
Healthcare systems should be engineered with privacy, security, patient safety, interoperability and applicable regulatory requirements considered from the beginning.
I do not believe technologies should be described as clinically validated or regulatory compliant simply because they work technically. Engineering is one stage. Clinical evaluation, evidence generation, risk management and regulatory review are separate and equally important parts of responsible healthcare technology development.
African Healthcare AI
Artificial intelligence models learn from the data they are trained on. A system developed using one population, healthcare system or imaging environment may not perform identically when transferred elsewhere.
Possible sources of dataset shift include ethnicity and population characteristics, disease prevalence, scanner manufacturers, detector technology, imaging protocols, clinical practices, image quality, infrastructure and patient demographics.
Building trustworthy healthcare AI for Africa therefore requires more than importing models. It requires local research, local data, clinical participation and rigorous validation — one of the reasons behind the work being explored through Zyemed Labs.