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The next frontier in healthcare: AI and large language models

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By Enbasekar D, Co-founder & CTO, MediBuddy

The next time we face a health issue, will an AI be the first point of assistance? Advancements in artificial intelligence suggest that such human-AI collaboration in healthcare is nearing reality. India has the potential to ride this wave and reap the benefits of AI in healthcare. While AI holds promise as a valuable tool to assist trained healthcare professionals, it is important to maintain realistic expectations and acknowledge the challenges involved. AI cannot directly replace caregivers or healthcare service providers, it serves as a valuable tool for trained healthcare professionals. It is helping them deliver high-quality care at scale while maintaining cost-effectiveness.

Enbasekar D

The country is moving towards a vibrant health-tech landscape focused on affordable innovations tailored for Indian demographics. However, compared to developed nations, India is slightly slower regarding tech adoption and scaling AI applications in healthcare. The COVID-19 crisis served as an unexpected catalyst by necessitating remote healthcare capabilities at scale while exposing gaps needing technology-first interventions. It brought to light promising use cases across diagnostics, treatment protocol development, and epidemic modelling – all fueled by the power of AI in healthcare.

Entrepreneurs are now passionately building cloud-based medical solutions by uniquely combining AI assistants, telemedicine platforms, and mobile apps. The results are powerful – enhanced decision- making for doctors, personalised patient treatment plans, and intelligent triaging of cases. Government initiatives like Ayushman Bharat and the National Digital Health Mission (NDHM) will also pave the way for furthering tech-driven healthcare and artificial intelligence in medical diagnosis, but challenges remain.

Challenges in digital healthcare
Providing highly personalised patient care has become increasingly challenging as the number of patients requiring medical services continues growing while the healthcare worker population remains limited. With this reality, the onus is on finding ways to manage better patient data, which has expanded with the advent of new digital systems and diagnostic capabilities. Effective data management is the key to enabling more customised care. However, navigating the complex landscape of health data presents critical challenges around privacy, security, reliability, and regulatory compliance that providers must address.

Consolidating records between various hospitals and diagnostic centres remains difficult, limiting the
large-scale usage of patient data. Ensuring that lab results and remote consultations are timely, accurate, and correctly interpreted is crucial yet adds another layer of complexity. As providers increasingly rely on digital platforms to prescribe medications and educate patients in remote settings, meticulous processes are required to ensure patient safety and proper understanding.

With evolving regulations around healthcare data and service delivery models, staying compliant can be exceptionally demanding for digital health companies. Though remote healthcare offerings provide much-needed convenience and accessibility, they inherently lack the nuanced personal touch and customised care that in-person services can deliver.

Moving forward, providers must thoughtfully balance convenience and customisation while addressing
the multifaceted data and compliance challenges arising from an increasingly digital healthcare landscape.

How can AI assist doctors and health professionals?
Data Science models particularly Large Language Models (LLMs) have the potential to revolutionise
healthcare by analysing vast amounts of medical data to identify patterns, aid in early diagnosis, and
predict potential health risks. This empowers doctors to provide personalised treatment plans tailored to
individual needs, a stark contrast to the one-size-fits-all approach often prevalent today.

Multimodal LLMs can help automate administrative tasks, freeing up doctors time for patient interaction
and handling complex cases. Remote patient monitoring and telehealth consultations can become more
efficient and accessible with the assistance of LLMs. Drug discovery and development can also be significantly accelerated by analysing massive datasets and creating more effective therapies. However, LLMs are a very new and fast-evolving technology, and therefore, a measured and cautious approach is needed in using LLMs.

Navigating the AI terrain: A shared responsibility
Harnessing the power of AI ethically requires a multi-pronged approach. Implementing robust data
security measures and adhering to data privacy regulations are paramount. Recognising the limitations
of AI tools and ensuring they complement, rather than replace, human expertise is critical. Maintaining
human oversight throughout the AI-driven healthcare process is essential for ethical considerations and
accountability. Addressing potential biases in AI algorithms and ensuring fairness in healthcare delivery
is crucial. Regularly updating and adapting AI tools based on new data and feedback is essential for
optimal performance.

Looking ahead in terms of adoption
Looking ahead in terms of adoption, the future of healthcare lies at the intersection of technology and
innovation. Digital healthcare adoption is expected to soar in the coming years, driven by technological
advancements and evolving consumer behaviour.

While AI has influenced a lot of industries very positively, the adoption of AI in healthcare is more challenging than usual and is fraught with risk if not handled carefully. Patient safety and compliance with regulatory guidelines are very important. Healthcare is also a complex ecosystem with multiple stakeholders like medical professionals, paramedical professionals, caregivers, multiple payers, and other participants Emerging innovations such as AI-powered chatbots, virtual reality-based therapy, deep learning in healthcare, and blockchain-secured medical records hold immense promise. Consumers will increasingly demand personalised, convenient, and affordable healthcare solutions.

The rollout of 5G will further accelerate innovation and improve accessibility in remote areas. Seamless collaboration between technology companies, health tech startups, and healthcare providers will be vital to unlocking the full potential of AI in healthcare.

AI systems, no matter how advanced, cannot replicate the nuanced human touch, emotional intelligence,
and holistic understanding that experienced healthcare professionals bring to patient care. While AI can
streamline certain processes, analyse vast datasets, and provide valuable insights, it should be
leveraged as a complementary tool rather than a substitute for human expertise.

Building a future-proof ecosystem
To fully realise the potential of AI in healthcare, India needs a robust ecosystem that fosters innovation
and ensures responsible development. Establishing clear and comprehensive regulatory frameworks for AI in healthcare is essential. Fostering collaboration between public and private stakeholders is crucial for accelerating innovation and ensuring widespread adoption. Creating dedicated innovation hubs can nurture talent, facilitate research, and encourage the development of cutting-edge technologies. Investing in infrastructure and talent development is crucial to build a future-proof healthcare ecosystem capable of harnessing the power of AI. Addressing affordability and accessibility issues is key to ensuring equitable access to AI-powered healthcare solutions.

Striking the right balance between technology and human-centric care will be key. AI should becimplemented judiciously, with clear guidelines and robust oversight mechanisms in place. Human medical professionals must remain central to the decision-making process, interpreting AI-generated recommendations through the lens of their training, experience, and ethical principles.

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1 Comment
  1. vinod gupta says

    AI benefits both patients and professionals by equipping healthcare organizations with algorithms that help them make better business and clinical decisions. Professionals then have the ability to make more accurate diagnoses, deliver personalized treatments, and reshape patient monitoring.

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