The AI-driven solution is analysing vast amounts of medical data swiftly and precisely, leading to earlier and more accurate diagnoses, and improved patient outcomes. AI-driven healthcare is more cost-effective and is being deployed widely in remote and underserved areas, ensuring access to quality care. The integration of AI, machine learning, and Internet of Things (IoT), terms previously confined to other sectors, are now acting as a force for good in healthcare. Access to Universal Health Care enabled by science and tech is a reality.
by Madhav Joshi & Prashant Warier
Nestled somewhere remote in the western Indian state of Maharashtra is the district of Gadchiroli. Today home to 10,72,942 residents, the district faces many daunting healthcare-related challenges – scarce resources, high patient loads, overwhelmed systems, and prolonged waits, aggravated by the absence of local radiologists or X-ray machines.
Cut to 2047, a Viksit Bharat, where after facing two weeks of consistent cough, a large language model-trained app directs a patient directly to their nearest primary healthcare facility using guidance in Marathi. A mobile van awaits them with a semi-portable X-ray, guided by an AI-based app that automatically analyses their X-Ray. Within two minutes, at no cost to the patient, a healthcare worker with limited technical training diagnoses TB with unmatched accuracy and seamlessly logs the case on the Nikshay TB platform. The patient is immediately linked to effective treatment, is handed a digital pillbox that will support them with adherence to treatment over the next several weeks. Another software offers them personalized care including nutritional guidance, addressing their unique need and medical profile.
In another faraway village hidden in Himalayas, ASHA workers employ an app for door-to-door TB screening using a simple mobile phone and a software that can detect the disease from cough sounds. The AI-driven solution is analysing vast amounts of medical data swiftly and precisely, leading to earlier and more accurate diagnoses, and improved patient outcomes. AI-driven healthcare is more cost-effective and is being deployed widely in remote and underserved areas, ensuring access to quality care.
Right at this time, in another tribal district deep in the forests of Orissa, an AI and machine learning (ML)-based algorithm automatically analyses blood smear slides testing the presence of malaria parasite, identifying subtle patterns and anomalies in medical imaging and patient data. The patient from an underserved community, once diagnosed, is being linked right away to an expert using telepathology.
The integration of AI, machine learning, and Internet of Things (IoT), terms previously confined to other sectors, are now acting as a force for good in healthcare. Access to Universal Health Care enabled by science and tech is a reality.
While the above are hypothetical glimpses from 2047, they illustrate a reality that is increasingly feasible and not too far from being true. In a multi-layered and complex healthcare sector like India, ripe for disruption from emerging technologies, AI-driven solutions present the most intuitive and obvious use case for intervention.
The increasing activity from both large corporates and start-ups in developing AI-focused healthcare solutions serves as evidence of the sector’s readiness for transformation. The adoption of AI for healthcare applications is expected to witness an exponential increase in the next few years, with the global healthcare market driven by AI projected to register an explosive compound annual growth rate (CAGR) of 40% through 2021 to 2029.
The hurdles on the way
- Limited access to data: While the technology development is seeing exponential growth, AI algorithms require large and diverse datasets for training and validation before they can be used in clinical practice. A lack of comprehensive data reflecting all socio-economic categories can jeopardize the reliability of interpretations for underrepresented groups. Ensuring availability of high-quality annotated data will be instrumental to integration of AI in healthcare.
- Unavailable strong and quality evidence: AI-driven healthcare solutions need robust scientific validation and evidence demonstrating their effectiveness, safety, and reliability to gain acceptance and trust.
- Policy and regulatory considerations and Privacy and data protection: Integrating AI into healthcare raises important ethical and legal concerns. The lack of clear regulatory frameworks and policies can hinder successful integration. Policymakers need to establish clear guidelines for ethical use of AI, data security, and patient privacy. Sensitive health information must be protected and ensure compliance with existing regulations will need to be ensured.
- Limited funding availability: Adequate funding is crucial for the development, deployment, and scaling of AI-driven healthcare solutions. Investment is needed not only in research and development but also for infrastructure and training.
- Interoperability with existing health systems: Achieving seamless integration of AI solutions with existing healthcare systems and electronic health records requires standardized protocols and interfaces. Using AI tools in conjunction with other existing tools especially holds potential where there is inadequacy of healthcare workers.
- Healthcare workforce training: Successful integration of AI in healthcare will necessitate effective collaboration between AI systems and healthcare professionals.
- Market access and adoption in public and private sectors: Once developed the solutions will need to penetrate deeply into the Indian market, which is currently fragmented and mostly a state subject. Alignment between policy makers to integrate the validated technologies into the national and state level health programs along with suitable financial instruments to ensure adoption is key to scale up these technologies.
While these barriers are significant, the potential of AI in revolutionising healthcare cannot be overstated, particularly at the primary care level, where the need is the highest.
What will it take for these technologies to become the healthcare norm in the remotest underserved corners of the country?
1. Building a Foundation for Tech-Readiness
To fully leverage AI in healthcare, a robust foundation of tech readiness, digital infrastructure and interoperability standards, is crucial. A strong digital infrastructure facilitates seamless data exchange, but privacy, security, and ethics must be prioritized. Early regulations are necessary for secure data storage and patient privacy. Standardized national policies are essential to align stakeholders and transform India’s healthcare with AI. Alignment with initiatives like Ayushman Bharat Digital Mission (ABDM) and Government of India policies is key for reaching remote areas in need of this technology. Providing access to data early on will also be a crucial pillar.
2. Bringing the healthcare professionals, patients and providers on board
Effective AI integration in healthcare relies on continuous training for professionals. Incorporating AI education into medical and nursing curricula will ensure future practitioners have a strong foundation. Highlighting successful training initiatives will serve as inspiring examples for further implementation. Emphasizing that AI empowers, rather than displaces, healthcare professionals will build trust and foster a collaborative relationship for improved patient outcomes.
3. Thinking “Market access” early on
The success of AI-based tools will hinge on how widely they get adopted. Thinking of sustainability mechanisms early on from the lens of affordability, accessibility will be crucial. Guidelines from the policy makers to ensure integration of the up-and-coming technologies into the national/ state health programs and financial outlays to promote adoption of promising technologies in healthcare can ensure more innovation and growth of technologies in health sector.
4. Increased investment
AI innovation in healthcare will require substantial funding from diverse sources, including government funding, grants, and venture capital. Collaborations between funding organizations and AI healthcare startups, exemplified by successful partnerships, will drive groundbreaking solutions. For instance, Qure.ai’s impactful contributions to infectious disease diagnosis have been supported by strategic philanthropic funding initiatives such as the India Health Fund. However, there needs to be more focused problem statements and patient capital, especially in the mid-stage of the innovation journey, to address existing gaps and propel transformative advancements. Newer financing mechanisms and increased public-private partnerships will also play a key role.
4. Collaboration and governance
Successful AI integration in healthcare hinges on collaboration among funders, government agencies, healthcare providers, researchers, and technology experts. Public-private partnerships, especially, have demonstrated impact by leveraging collective resources for seamless AI integration. By combining strengths, these collaborations will drive the development and implementation of cutting-edge AI solutions, improving healthcare delivery and patient outcomes. Through collaborative governance and shared vision, stakeholders will be able to navigate regulatory challenges, ensure ethical AI practices, and address pressing healthcare issues, creating a sustainable and transformative impact in medicine.
AI brings transformative capabilities to healthcare, offering unmatched accuracy, speed, efficiency, affordability, and accessibility. As we look towards the future, AI, combined with robotics and the Internet of Medical Things, hold the potential to become the new nervous system for healthcare. These advances, heightened innovation, stronger investments, and collective action will all be the ingredients that will speed India’s AI-driven healthcare transformation – ensuring a future that is healthier and equitable for all.
Madhav Joshi, CEO of India Health Fund (IHF) & Prashant Warier, CEO of Qure.ai
(DISCLAIMER: The views expressed are solely of the author and ETHealthworld does not necessarily subscribe to it. ETHealthworld.com shall not be responsible for any damage caused to any person / organisation directly or indirectly.)
", -->
Source Link
