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Govt To Expand AI And E-Book Access For Medical Students, Phase 1 Covers 57 Colleges

Govt plans AI and e-book access for medical students, starting with 57 government colleges in rural and small towns.

New Delhi: The government is looking to provide access to e-books and AI resources to medical students to help them hone their skills better, with the first phase of the initiative covering around 57 medical colleges in the smaller towns and rural areas, according to a senior Health Ministry official.

Speaking at the AI Impact Summit here, Deputy Director General (Medical Education) B Srinivas stated that students from medical colleges in remote areas find it challenging to access e-books and good technical materials, including this AI material.

"So the government is thinking of using the leverage of AI to reach out to these students ... in the National Medical Library we have started the process of securing the e-books and the digital clinical material, and we are doing it right now in around 57 government medical colleges across the country," Srinivas said.

The government is looking to scale up the initiative in a gradual manner, he added.

"We are in the pipeline to also include the private medical colleges later on. But since the budget is coming from the Government, we are right now concentrating only on the government institutions," he said.

Building campuses and infrastructure is very easy, but building up the knowledge material that takes time, he added.

The panelists in the session focussed on how responsible AI can advance health equity by improving access to trusted medical knowledge, clinical decision support, and workforce capacity.

The speakers also deliberated on bringing together policymakers, healthcare leaders, clinicians, and industry experts with a focus on trust, transparency, and governance in health AI.

The panel also explored how evidence-based, explainable AI systems can be deployed safely and at scale to strengthen health systems and improve outcomes, particularly in emerging and resource-constrained settings.

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