Biomedical Independence: From AI to Nanotechnology

Biomedical independence is not a project of a single ministry, one university, one hospital, or one generation. It is a civilisational project.

Jakarta (ANTARA) - At the age of 81 years of Indonesian independence, the question of sovereignty no longer stops at who controls territory.

In the 21st century, sovereignty is also determined by who controls scientific knowledge, health data, algorithms, diagnostic technology, drug manufacturing, genomics, and the various technologies that determine the quality and safety of human life.

A country can be politically independent, yet remain dependent on other nations to read the diseases of its people, produce therapies, process biological data, and determine the health technologies used. Therefore, Indonesia needs to fight for the next agenda: biomedical independence.

Biomedical independence does not mean having to make everything itself. Modern science grows through collaboration. What is needed is the ability to choose, develop, test, produce, use, and regulate health technology from a position of equality: sovereign in data, excellent in science, resilient in industry, open in collaboration, and fair in sharing benefits.

The strategic question is simple: within the next generation, will Indonesia only become a market for the world’s health technology, or will it also become a creator, owner, tester, producer, and direction-setter of it?

The answer will affect the economy, diplomacy, health resilience, and the future of the next generation.

AI, genomics and stem cells

Artificial intelligence has entered almost the entire biomedical chain. AI can help read radiology and pathology images, find patterns in laboratory data, accelerate the search for drug candidates, screen scientific literature, and support clinical decisions. In research, AI is able to bring together various types of data and find patterns that were previously difficult for humans to recognise.

However, health AI is not only about algorithms. There are three more fundamental questions: whose data is used to train the system? Is the system proven safe and accurate in the population where it is used? Who is responsible when its recommendations are wrong?

The problem is not solely about which country the technology comes from, but whether its data and models sufficiently represent the diversity of patients and the context of Indonesian health services.

Without adequate local validation, we risk bringing bias from unrepresentative data into the clinical space.