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Published on: Oct 28, 2020
2 minute read
By: Dr Rajan ( Medical Second Opinion Cell)

Hepatitis C Nobel Prize

Earlier this month the Nobel Prize in Physiology and Medicine was jointly awarded to Harvey J Alter, Michael Houghton and Charles M Rice for the discovery of the Hepatitis C virus. This helped push our understanding of blood-borne hepatitis, a major global health problem that is one of the leading causes of cirrhosis and liver cancer alongside Hepatitis B. It led the way in introducing new tests for chronic hepatitis as well as new treatments that have saved the lives of millions.

HEPATITIS

Hepatitis is the inflammation of your liver, the largest solid organ in your body. Though there are numerous other causes of hepatitis, including alcohol abuse, drugs and toxins (including paracetamol overdose), and autoimmune disorders (such as Wilson’s disease), viral infections are the most common worldwide causes for hepatitis. Hepatitis A was the first to be discovered, transmitted by polluted food and water, and results in a short-term transient hepatitis. Hepatitis B on the other hand is transmitted through blood and is more of an issue as it can lead to chronic cirrhosis and possible liver cancer. It can remain indolent in a person, causing disease long after the initial infection.

Blood borne hepatitis was first discovered in the 1960s, when it was found that some patients would become ill after receiving blood transfusions. Despite this discovery and new tests for Hepatitis B reducing the number of transfusion related hepatitis, a large number of cases remained.

NOT A, NOT B

Harvey Alter, working at the US National Institute of Health, was studying the occurrence of hepatitis in blood transfusion patients. His team showed that blood from these infected patients could transmit the disease to chimpanzees, resulting in hepatitis. This agent had the properties of a virus, one that was “non-A, non B” hepatitis.

Further investigation into the virus would require the input of Michael Houghton, working for Chiron pharmaceutical. His team created a collection of DNA fragments found in the blood of infected chimpanzees. Though the majority of these fragments were from the chimpanzee, enough were from the virus. They were able to utilise this information to identify antibodies against the suspected virus, and ultimately identify it as the flavivirus Hepatitis C.

The final piece of the puzzle was shown by Charles M Rice, a researcher at Washington University, St Louis – could the virus alone cause hepatitis. He studied the RNA of Hepatitis C to identify regions that may be used for replication, and areas that might hinder replication. This was key as the virus can lay dormant for years, replicating at a slow rate before causing damage to the liver. For researchers, this was an issue as it was not feasible to wait years and see if the suspected virus caused hepatitis. By genetically engineering the viral genome, he developed a variant of Hepatitis C that would only multiple rapidly and have no mechanism to inactivate itself for dormancy. This strain caused rapid changes to the livers of infected chimpanzees; the same changes seen in hepatitis patients.

. ;.,hrvxzThe impact was significant. Prior to this, receiving a blood transfusion was like Russian Roulette. You were receiving life saving therapy, but it coZuld also be a hidden death sentence. Now that hepatitis had been discovered, it became possible to screen patients prior to donation, to screen blood transfusion bags, and to start developing treatments for the virus.

The fight is not over yet. There are still over 70 million people who still live with the virus, and it still kills around 400,000 a year. Its only very recently that we have found treatments that can reduce the viral load in patients to levels that they are considered cured. This treatment remains expensive, and we are very far away from eradication itself.

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How Artificial Intelligence Is Transforming Patient Care in India

As a clinician working closely with patients across urban clinics and remote teleconsultation setups, I have seen firsthand how delayed diagnosis, fragmented follow-up, and specialist shortages affect outcomes in India. Artificial intelligence is not a futuristic concept in Indian healthcare anymore. It is actively reshaping how we diagnose diseases, monitor patients, and prevent complications.

AI, when used responsibly under clinical supervision, is becoming a critical support system for doctors and a powerful safety net for patients navigating a complex healthcare ecosystem.

Why India’s Healthcare System Needs AI

India’s healthcare challenges are deeply structural. A large population burdened by lifestyle diseases, combined with uneven access to medical expertise, creates gaps that traditional systems struggle to bridge.

In daily practice, we increasingly see patients presenting late with diabetes, hypertension, heart disease, or cancer. Many ask a simple but important question: why was this not detected earlier? The answer often lies in limited screening, overloaded clinicians, and lack of continuous monitoring.

Chronic conditions dominating Indian clinics today include:

Diabetes affecting over 100 million individuals.
Hypertension rising even among young adults.
Cardiovascular disease driven by late detection.
Increasing cancer incidence with delayed diagnosis.

AI matters here because it supports earlier identification of risk patterns, reduces diagnostic delays, and allows clinicians to focus on decision-making rather than data overload.

How AI Is Changing Medical Diagnosis

One common concern patients raise during consultations is whether AI can truly diagnose diseases accurately. In practice, AI does not replace a doctor. It acts as a high-speed analytical assistant.

AI in Imaging and Diagnostics

AI systems can rapidly analyse:

X-rays and CT scans.
MRI images.
Mammograms.
Pathology slides.
Cardiac and neurological imaging.

These tools flag abnormalities within seconds, allowing doctors to prioritise critical findings. Clinical studies published in peer-reviewed journals have shown that AI models can match specialist-level accuracy for specific imaging tasks when used correctly.

From a physician’s perspective, the real benefit is not speed alone. It is consistency. AI reduces the risk of missed findings during high-volume diagnostic workflows, especially in resource-constrained settings.

Can AI Monitor Patients Outside Hospitals

Patients managing chronic illness often ask whether technology can help them avoid repeated hospital visits. AI-enabled remote monitoring is one of the most meaningful advances in this area.

AI-Supported Remote Patient Monitoring

AI continuously evaluates trends in:

Blood pressure.
Heart rate variability.
Blood glucose patterns.
Oxygen saturation.
Physical activity and sleep quality.

Rather than reacting to a single abnormal value, AI identifies worsening trends over time. Clinically, this allows early intervention before complications escalate.

Evidence from global health system studies shows that continuous monitoring can significantly reduce avoidable hospital admissions, particularly for diabetes, heart disease, and elderly patients.

Using AI to Predict and Prevent Chronic Diseases

Preventive healthcare remains underdeveloped in India. Most patients seek care after symptoms appear. AI helps shift this model.

By analysing medical history, lifestyle habits, vitals, and environmental factors, predictive models can estimate:

Future heart attack risk.
Progression of diabetes.
Decline in kidney function.
Stroke probability.
Asthma exacerbation triggers.

Patients often ask if AI can really prevent disease. Prevention here means early warnings. When risk patterns are detected early, doctors can adjust treatment plans, recommend lifestyle changes, and prevent irreversible damage.

Personalised Treatment in a Diverse Indian Population

Indian patients differ widely in genetics, diet, stress patterns, and cultural habits. Standardised treatment protocols often fall short.

AI supports personalised care by analysing:

Medication responses.
Dietary intake.
Blood markers.
Sleep and stress trends.
Coexisting medical conditions.

For example:

In diabetes care, AI helps personalise carbohydrate distribution and medication timing.
In hypertension, it identifies sodium sensitivity and stress-related spikes.
In hormonal conditions like PCOS, it aligns nutrition and activity with cycle patterns.

From a clinical standpoint, personalised insights improve adherence and reduce relapse rates.

AI-Enabled Telemedicine and Smarter Consultations

Telemedicine has become an essential part of care delivery in India. Patients frequently ask whether online consultations are as effective as in-person visits.

AI enhances telemedicine by:

Structuring symptom inputs before consultations.
Routing patients to the appropriate specialist.
Generating concise medical summaries for doctors.
Supporting follow-up reminders and medication adherence checks.

When used correctly, AI reduces diagnostic delays and improves consultation efficiency without compromising safety.

Expanding Healthcare Access Beyond Cities

A major question in public health is whether AI can truly improve rural healthcare access. In practice, it already is.

AI enables:

Remote diagnostics supported by portable devices.
Virtual specialist consultations for rural clinics.
Smartphone-based imaging and screening tools.
AI-guided triage in underserved regions.

By reducing dependence on physical proximity to specialists, AI helps bridge longstanding geographical barriers in India’s healthcare system.

Safety, Ethics, and the Role of Doctors in AI Care

Patients rightly express concern about safety, privacy, and over-reliance on technology. These concerns are valid.

Responsible AI use in healthcare requires:

Transparent algorithms.
Explicit patient consent.
High-quality, verified medical datasets.
Strict data privacy safeguards.
Continuous clinical supervision.

In ethical practice, AI outputs never replace medical judgment. Doctors remain accountable for decisions. Human-in-the-loop verification is essential to ensure patient safety and trust.

What This Transformation Means for Indian Patients

Artificial intelligence is fundamentally changing patient care in India by making healthcare more proactive, more precise, and more accessible. From early diagnosis to personalised treatment and continuous monitoring, AI empowers both patients and clinicians with data-backed clarity.

SecondMedic’s patient-first approach integrates AI as a clinical support system, not a replacement for doctors. By combining medical expertise with digital intelligence, the goal remains simple: better outcomes, earlier intervention, and care that adapts to each patient’s real-world needs.

As clinicians, our responsibility is to ensure that technology serves patients ethically and effectively. When used with care and oversight, AI has the potential to redefine healthcare delivery across India in a way that is inclusive, preventive, and sustainable.

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