AI can aid faster, cost-effective diagnosis but clinical validation and data safeguards needed: experts

The panel described AI as a way to improve disease detection, expand healthcare access and reduce diagnostic costs. These benefits matter especially where specialists are scarce or patients reach hospitals late. AI can process clinical information quickly and help identify cases needing urgent attention.

Examples included AI-supported tuberculosis screening, medical-image analysis and smartphone tools for suspicious oral lesions. Hospitals could also use AI to prioritise critical scans instead of processing every scan in arrival order. This may speed care for patients who need it most.

The experts stressed that adoption cannot rest on promising technology alone. Systems need clinical validation, meaning evidence that they work safely and accurately in healthcare settings. Doctors must understand AI limitations and retain final judgement. Patient information also needs protection, including safeguards around identifiable data and whether existing consent covers training AI models.

AI-assisted medical diagnosis is the use of computer systems to examine health information and support a clinician’s diagnosis. The system does not replace the doctor. It helps find patterns that may be difficult or time-consuming to review manually, making detection and referral faster.

Medical images are especially useful because they provide standardised digital data. AI systems can learn patterns from such images and flag findings that may suggest disease, such as tuberculosis or suspicious oral lesions. Hospitals can then prioritise critical scans instead of reviewing them only in arrival order.

This matters where radiologists and other specialists are in short supply. In a study across six Tamil Nadu districts, the panel heard that AI-supported TB screening helped reach more people. Still, doctors must assess the recommendation, explain the diagnosis and consider the patient’s circumstances before treatment decisions are made.

The panel described a major cancer-screening gap: less than 1% of the population had been screened. The figure shows that screening is reaching only a very small share of people. It also highlights why detection cannot depend only on patients noticing symptoms and seeking care.

Reuben Fernandes said many patients sought medical attention only after symptoms appeared. By then, cancer could already have advanced to Stage III or IV. AI-assisted tools could help close part of this gap by identifying suspicious signs earlier and directing people to specialists for further assessment.

Falling computational costs and wider smartphone penetration may make these tools more accessible. Smartphone-based systems could identify suspicious oral lesions without requiring every person to visit a specialist first. The panel did not provide a total number of unscreened people, but the less-than-1% figure signals extremely limited screening coverage and a need for broader outreach.

Smartphone-based AI can bring an initial screening step closer to patients who live far from specialists or face limited healthcare access. It can identify suspicious signs and direct people to a specialist for further assessment. This may reduce delays caused by travel, shortages or low screening uptake.

The panel gave oral-lesion screening as an example. A phone-based tool could examine an image and flag a possible concern. If the AI model runs directly on the phone, it can reduce dependence on internet connectivity in areas with limited infrastructure. The tool supports referral; it does not make the final diagnosis.

AI can also help hospitals manage queues more intelligently. Instead of reviewing scans strictly in arrival order, staff could prioritise critical cases. That may shorten waits for urgent patients. These benefits still depend on clinical validation, careful privacy protection and doctors who can recognise when an AI recommendation should not be followed.

Medical imaging is well suited to AI because digital images provide standardised data. Systems can learn recurring patterns from those images and help identify findings that deserve clinical attention. This creates a practical way to support doctors who must review many scans or work with limited specialist support.

Tuberculosis screening illustrates the potential. Richa Pant said a study in six districts of Tamil Nadu showed that implementing AI in TB screening could reach more people. The system can examine available digital information and help identify cases for further assessment. It can therefore extend screening beyond locations with enough radiologists.

These areas are not automatically safe or accurate simply because they use images. Clinicians still need to understand the system’s limitations and verify recommendations. The panel linked wider use to clinical validation, privacy safeguards and human oversight. AI can expand the first layer of detection, while healthcare professionals remain responsible for diagnosis, communication and treatment decisions.

Clinical validation is the process of establishing that an AI system performs accurately and safely for the healthcare task it is meant to support. It should be assessed with relevant clinical information and patients before its recommendations guide care. This matters because a system can appear promising without being dependable in every setting.

For example, an AI tool might flag a scan for urgent review or identify a suspicious oral lesion. Clinical validation examines whether those recommendations are useful enough for clinicians to act on. It does not turn the software into an independent decision-maker. Doctors still interpret results alongside symptoms, history and personal circumstances.

The panel stressed that clinicians must understand AI limitations and recognise when recommendations should not be followed. Human review also protects communication and informed decisions. Doctors explain diagnoses, discuss treatment choices and account for financial or family difficulties, responsibilities an AI system cannot replace.

Protecting privacy means limiting access to information that can identify a patient and using medical data only for clearly supported purposes. The panel urged developers to avoid names, phone numbers and locations wherever possible. Unique identifiers can let companies process medical information without directly seeing patients’ identities.

The same principle applies when AI operates on records or learns from them. Hospitals and developers should separate useful clinical information from direct identifiers and control access to the remaining data. The article also raised consent: institutions must examine whether consent originally obtained for historical records actually covered training AI models.

India’s Digital Personal Data Protection Act and medical device regulations provide relevant frameworks, but the panel called for clearer safeguards specific to healthcare AI. Privacy protection therefore requires more than technical de-identification. It also needs careful governance, transparent consent decisions and human oversight of how records are collected, shared and reused.