<p><em><strong>By Mr Sanjeev Malhotra</strong></em></p>
<p dir=”ltr”>While <span class=”il”>India</span> has made remarkable progress in <span class=”il”>healthcare</span> over the past few decades, with <span class=”il”>Urban</span> centres boasting world-class hospitals, cutting-edge diagnostic infrastructure, and a growing ecosystem of health-tech innovation, the state of <span class=”il”>healthcare</span> in <span class=”il”>rural</span> <span class=”il”>India,</span> with 65% of the population,n continues to remain dismal. While <span class=”il”>urban</span> centres are turning into hotspots for <span class=”il”>healthcare</span> tourism, attracting international patients, millions of people in villages and small towns continue to face challenges in getting timely access to quality medical care, affordable and accessible <span class=”il”>healthcare</span>.</p>
<p dir=”ltr”>The key reasons include a shortage of doctors, especially specialists like radiologists and oncologists, logistical and cultural barriers especially for <span class=”il”>healthcare</span> services for women. While <span class=”il”>urban</span> areas are close to the WHO recommended 1:1000 ratio of doctors to patients, in <span class=”il”>rural</span> areas this can go as high as 1:110000 or even worse. These challenges can’t be addressed in a linear manner by increasing the <span class=”il”>healthcare</span> service staff, and technology has to play a critical role in addressing the gap. </p>
<p dir=”ltr”>Artificial intelligence (<span class=”il”>AI</span>) is especially emerging as a key technology in the transformation of <span class=”il”>healthcare</span> systems. <span class=”il”>AI</span> has the potential to augment human capability by automating routine tasks and identifying insights from vast amounts of data that is otherwise very difficult to process manually. In the context of <span class=”il”>India</span>’s <span class=”il”>urban</span>-<span class=”il”>rural</span> <span class=”il”>healthcare</span> <span class=”il”>divide</span>, <span class=”il”>AI</span> offers uniquely scalable and adaptive solutions that can reach even the remotest parts of the country.</p>
<p dir=”ltr”><em><strong>Some of the areas where <span class=”il”>AI</span> can play role are:</strong></em></p>
<h3 dir=”ltr”><span style=”color: #ba372a;”><strong>Predictive Analytics</strong></span></h3>
<p dir=”ltr”><span class=”il”>AI</span> has the capability to analyse historical health data to predict disease outbreaks. For cases like TB, Malaria & Dengue, Water-borne infectious diseases (cholera, typhoid, etc), <span class=”il”>AI</span> models can identify at-risk populations and support early intervention strategies—especially crucial in <span class=”il”>rural</span> areas where preventive care is often lacking.</p>
<p dir=”ltr”>For <span class=”il”>urban</span> areas, <span class=”il”>AI</span> models can predict problems like diabetes, cardiovascular diseases, cancer, chronic kidney diseases, and mental health problems by analysing the historical data and recommend timely intervention. As public data states that <span class=”il”>India</span> is about the become the diabetes capital of the world, AI-based predictions are likely to play an increasingly critical role in prevention.</p>
<h3 dir=”ltr”><span style=”color: #ba372a;”><strong>Medical Imaging and Diagnostics</strong></span></h3>
<p dir=”ltr”><span class=”il”>AI</span> algorithms are especially suited for analysing X-rays, MRIs, CT scans, and pathology slides with accuracy that rivals or even surpasses human specialists. This opens the door for remote diagnostic capabilities, particularly valuable where radiologists and pathologists are scarce. Even when radiologists and pathologists are available, AI-based diagnosis can reduce their workload significantly, thereby allowing them to provide timely treatment to a larger number of patients.</p>
<h3 dir=”ltr”><span style=”color: #ba372a;”><strong>Telemedicine and Remote Patient Monitoring </strong></span></h3>
<p dir=”ltr”>The NLP-based telemedicine capabilities of <span class=”il”>AI</span> can take symptoms in local language, create a preliminary diagnosis and risk analysis and route to the relevant doctor, thereby freeing doctors from the load of first-level diagnosis and addressing the serious gap in availability of doctors in vernacular languages.</p>
<p dir=”ltr”><span class=”il”>AI</span> can also process the data from wearable devices or mobile health apps to monitor chronic conditions, flag anomalies, and provide decision support for frontline health workers in real time. Timely alerts can be sent to the <span class=”il”>healthcare</span> service provider, thereby preventing complications and emergencies.</p>
<p dir=”ltr”>The success of <span class=”il”>AI</span> intervention depends upon localization, human in the loop (humans making the decision) and scalability. <span class=”il”>AI</span> doesn’t replace humans – it partners with humans to make their tasks easier.</p>
<p dir=”ltr”>While <span class=”il”>AI</span> offers transformative capabilities for addressing the <span class=”il”>urban</span>-<span class=”il”>rural</span> <span class=”il”>healthcare</span> gaps, its adoption in <span class=”il”>rural</span> <span class=”il”>India</span> faces several systemic, infrastructural, and ethical challenges. Some of these challenges are listed here:</p>
<h3 dir=”ltr”><span style=”color: #ba372a;”><strong>Data Availability and Quality</strong></span></h3>
<p dir=”ltr”>Fragmented and unstructured health data in <span class=”il”>rural</span> areas make it difficult to train accurate <span class=”il”>AI</span> models. In addition, the lack of infrastructure in terms of HER capabilities inhibits large-scale deployment.</p>
<h3 dir=”ltr”><span style=”color: #ba372a;”><strong>Connectivity limitations</strong></span></h3>
<p dir=”ltr”>The majority of <span class=”il”>rural</span> areas lack reliable internet, electricity, or digital equipment, which are basic prerequisites for most <span class=”il”>AI</span> tools. While there is the constant attempt to develop solutions which can operate independently, without internet </p>
<h3 dir=”ltr”><span style=”color: #ba372a;”><strong>Limited digital literacy and Trust issues</strong></span></h3>
<p dir=”ltr”>Patients may be hesitant to engage with chatbots or automated decision-makers, especially in sensitive areas like mental health or reproductive care. Issues around privacy also tend to hinder the open discussion when data is being stored remotely. In such cases, the role of local players like ASHA workers becomes critical in enabling the access. They need to explain to the patients that the information is secure and the recommendation is coming from the doctor, not a machine. </p>
<p dir=”ltr”><span class=”il”>AI</span> can be a transformation agent in <span class=”il”>bridging</span> the <span class=”il”>urban</span>-<span class=”il”>rural</span> <span class=”il”>healthcare</span> <span class=”il”>divide</span>, but the success depends upon addressing the last-mile human and system challenges. Ultimately the solutions must be inclusive, accountable, culturally sensitive, and designed keeping in mind the very communities they aim to serve.</p>
<p><em>(The author is the CEO, Nasscom Center of Excellence)</em></p>
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