Tarun Dua, Managing Director, E2E Networks talks about generative AI and highlights that key aspect of this technology is that it shows emergent behavior, a phenomenon where novel or unexpected capabilities arise from the training and operation of these AI models. Initially designed to learn patterns and generate content based on existing data, it sometimes produces outputs beyond its direct programming or training data
In a country like India, healthcare professionals are often overwhelmed by the volume of cases they have to handle. With a growing population in geographically diverse regions, speaking in a multitude of languages, and with the constant influx of new viruses and lifestyle-related illnesses, doctors and nurses could do with a lot of assistance from technology.
Frequently, they have to offer care in remote locations with limited manpower while navigating language barriers and entrenched cultural beliefs, all the while adapting their communication and interventions to the unique needs of that geography. Additionally, there is a resource shortage in healthcare in rural India, as healthcare facilities are mostly concentrated in urban areas.
In the face of these challenges, generative AI emerges as a powerful new technology for healthcare professionals in India.
Generative AI refers to a type of AI technology that can create new content, such as text, images, or code. This technology is trained on massive amounts of data and built using neural network architecture, allowing it to learn complex patterns and relationships on its own. This training enables it to inference, identify patterns in new data, respond to queries, classify documents, discover anomalies, and even draw connections between data points which it had not been previously exposed to.
These human-like capabilities that generative AI technologies demonstrate make them extremely useful as intelligent assistants, termed ‘co-pilots’, in a number of fields including healthcare.
What makes generative AI so powerful?
The magic behind generative AI’s superpowers lies in its deep learning architecture, inspired by the human brain’s intricate neural networks. Think of it as a vast network of interconnected nodes, constantly processing and refining information. Each node holds a fragment of knowledge, and their combined strength unlocks remarkable abilities.
A key aspect of this technology is that it shows emergent behavior, a phenomenon where novel or unexpected capabilities arise from the training and operation of these AI models. Initially designed to learn patterns and generate content based on existing data, it sometimes produces outputs beyond its direct programming or training data. It comes up with answers which are unexpected, or draws inferences in a way that is human-like when exposed to entirely new data points.
Potential of generative AI in healthcare
Since Generative AI is able to traverse through vast amounts of information, identify patterns, and then communicate them in a human-like language, it acts as a great assistant, or co-pilot. Furthermore, as we discussed above, it has the ability to draw inferences from disparate data points, even the ones it has never been exposed to before.
Turns out, this translates to some extremely powerful applications in the healthcare domain.
Generating personalised reports and documentation: Generative AI assistants have the ability to analyze patient data and generate reports in real-time, freeing up doctors’ time to focus on patient care.
Identifying and diagnosing diseases: Generative AI has also demonstrated the ability to analyse medical images and data to identify patterns and abnormalities that may be indicative of disease. This can help doctors diagnose diseases early, and offer more effective treatment.
Developing personalised treatment plans: Since Generative AI can analyse patient data, it can also assist healthcare professionals in creating treatment plans that are tailored to the individual patient’s needs.
Providing patient education and support: One of the key capabilities of generative AI lies in its ability to communicate in a human-like manner. Generative AI chatbots can, therefore, help provide patients with information about their condition and answer their questions in their own language. This can have a massive impact in a country like India, where there is vast linguistic diversity.
Conducting research and development: Since Generative AI technologies are able to analyse large datasets of medical data and learn from new data points, they can also identify new insights and develop new treatments. This is now actively being used for drug discovery in several labs.
Recommendations for implementing generative AI in healthcare
While generative AI can be a powerful technology for healthcare professionals, it is important to deploy it in a compliant way that ensures security and privacy of patient data. Therefore, proprietary AI platforms aren’t the best choice in healthcare. So how do you leverage the power of generative AI in healthcare in a safe and secure manner?
The best approach is to use open-source AI models, such as open-source LLMs, which can be self-hosted on hyperscale cloud GPU platforms and trained or fine-tuned privately. This enables training and building of powerful AI models without leaking sensitive information to proprietary closed platforms. One such example of this are the open-source collections of medical conversational AI models which can be fine-tuned while safeguarding patient privacy.
Open-source models are also useful when researchers look to generate synthetic patient data from real patient data, in order to train diagnostic AI tools without compromising patient privacy.
The future of AI in healthcare
From generating personalised patient reports and aiding in disease diagnosis to crafting individualised treatment plans and offering multilingual patient education, Generative AI’s scope in healthcare can be vast and impactful. Particularly in a linguistically diverse country like India, its capacity to communicate in different languages, and in multiple formats like voice and text, could bridge significant gaps in patient understanding and healthcare delivery.
However, the deployment of generative AI in healthcare must be navigated with caution, prioritising the security and privacy of patient data. Open-source AI models emerge as a viable solution, offering the flexibility to be adapted to specific needs while maintaining data confidentiality. By leveraging open-source models on hyperscale cloud GPU platforms, healthcare providers can harness the full potential of this technology, delivering quality care while safeguarding patient privacy.
As this technology continues to evolve, its role in revolutionising healthcare in India becomes increasingly evident, heralding a new era of efficient, personalised, and accessible healthcare solutions.
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