Dr Sudeep Rauniar
Artificial intelligence is fast becoming one of medicine’s most potent diagnostic tools. By sifting vast datasets, spotting subtle patterns and triaging cases, AI is reshaping tumour detection — a domain long defined by the judgement of radiologists and pathologists. The shift is not about replacing experts; it is about giving them sharper instruments and more time where it counts.
Support for diagnosticians
For decades, radiologists and pathologists have read images that often decide a patient’s fate. Each scan or biopsy slide demands sustained attention.
Even the best-trained eye can miss a faint sign — especially late in a shift or when patterns are unusually complex. Here, AI can act as a second reader.
Deep-learning systems learn from hundreds of thousands of examples, flagging regions of interest that merit a closer look. The clinician still decides; the machine adds speed, consistency and a safety net.
How radiology and pathology are changing
In radiology, AI tools already assist with mammography, CT and MRI. They can characterise a lesion, compare it with prior studies and even suggest likely behaviour, which improves reader consistency and reduces reporting time.
Pathology is undergoing a similar transition. On digitised slides, algorithms highlight atypical cells, count mitoses and quantify margins.
Instead of scanning every field manually, the pathologist can focus on confirming the diagnosis and advising on treatment.
Earlier detection
AI’s greatest benefit may be in catching disease earlier, not in re-identifying advanced cancers. When imaging is combined with clinical, laboratory and genetic data, risk signals emerge sooner.
The trajectory is clear: software will quietly prompt clinicians about small abnormalities that, left alone, could become tumours years later. Earlier flags mean earlier action.
Bias, oversight and privacy
Technology brings obligations. AI is only as sound as the data it learns from; biased or incomplete inputs yield skewed outputs.
Medical AI must be trained, validated and monitored to rigorous standards and always used under human supervision. Privacy is another non-negotiable. Hospitals must govern patient data responsibly and be transparent about how AI systems are developed, tested and deployed.
Looking ahead
AI will not replace human intelligence in tumour diagnosis. Machines process information at scale; clinicians bring context, ethics and empathy.
Together they can deliver faster, more reliable and more personalised care. Over the next decade, expect AI to assist at every step — prioritising lists, suggesting differentials, learning from each confirmed case — and to fade into the background as a dependable partner.
Far from being sidelined, diagnosticians may gain agency as mundane tasks recede and complex decisions come to the fore.
Having observed AI’s growing use in diagnosing histopathological slides, I believe the next few years are about partnering efficiently with AI for prompt, effective tumour diagnosis.
Dr. Sudeep Rauniar, a budding tumor diagnostician and MD resident at MMIMSR, Ambala, who has a core interest in comprehending AI’s usage in the prompt and effective diagnosis of tumors. He is also a member of the American College of Physicians and the Royal College of Physicians of Edinburgh and has attended CME Certifications from Stanford Medicine and MD Anderson Cancer Centre.
