Introduce the concept of Artificial Intelligence (AI). How does AI help clinical diagnosis? Do you perceive any threat to privacy of the individual in the use of AI in healthcare?

Artificial Intelligence (AI) refers to the ability of computer systems to perform tasks that normally require human intelligence, such as learning, reasoning, pattern recognition, prediction and decision-making.

AI can help in Clinical Diagnosis as:

• Medical imaging: AI can analyse X-rays, CT scans, MRI, ultrasound and retinal images to detect cancers, TB, stroke, fractures and diabetic retinopathy.
• Early disease detection: Machine-learning models can detect changes in vital signs, ECGs, lab values and wearable data before symptoms become severe. E.g., early sepsis, heart disease or cancer risk.
• Clinical decision support: AI can combine symptoms, medical history, test results and imaging to suggest probable diagnoses and treatment options.
• Pathology and lab analysis: AI can assist in analysing biopsy slides, blood samples and molecular markers, improving speed and consistency.
• Genomics and precision medicine: AI can process large genomic datasets to identify disease predisposition, likely mutations, and support personalised treatment.
• Remote and rural healthcare: AI-enabled apps, telemedicine platforms and portable diagnostic tools support frontline workers and doctors in underserved areas. E.g., AI chest X-ray screening vans for Tuberculosis detection.
• Pandemic/public health use: AI can help track outbreaks, predict disease spread and prioritise high-risk patients.

Privacy Threats in AI-Based Healthcare:

• Data misuse without consent: Patient data may be collected, shared or analysed without clear informed consent.
• Identity theft and discrimination: Unauthorised access to sensitive patient data can lead to identity theft, stigma or social and workplace discrimination.
• Massive data breaches: Centralised health databases are attractive targets for cyberattacks and can expose diseases, treatments, identities and financial details.
• Algorithmic profiling: AI may predict future disease risks, which can be misused by insurers or employers to deny coverage, raise premiums or discriminate.
• Commercial exploitation: Health-tech platforms or third-party developers may monetise personal medical data without explicit permission.

India needs stronger health-data regulation, informed consent, privacy-bydesign AI, data minimisation, cybersecurity safeguards, algorithmic transparency, ethical audits and coordination among hospitals, AI developers and regulators.