But here, accuracy has always been a battle fought against exhaustion, distractions and overwhelming demand.<\/p>\n
A paediatric blood report arrives on Chaitanya\u2019s screen. The numbers seem ordinary: white blood cells normal; haemoglobin slightly low; platelets borderline. Typical monsoon fever, he thinks, almost ready to dismiss it. But experience and the scars of past misdiagnoses have taught him caution. Instead of signing off, he gently places the slide into an artificial intelligence (AI)-powered scanner he recently bought, against nearly everyone\u2019s advice.<\/p>\n
Amid the clamour rising from the street, the machine begins examining hundreds of cells, undistracted by the relentless noise below.<\/p>\n
Then, a red notification flashes on the screen. Blast cells: 86%.<\/p>\n
Chaitanya stares briefly. There\u2019s no ambiguity.<\/p>\n
\u201cThat\u2019s leukaemia.”<\/p>\n
Across town, in a dimly lit paediatric ward at the government hospital, Prasad Pawar, a sugarcane farmer from a village nearby, wipes sweat gently from his son\u2019s forehead. The 12-year-old boy lies listless, IV fluids dripping slowly into his arm. For days they had moved from clinic to clinic, first an orthopaedist for back pain, then a paediatrician suspecting dengue. No one said the word \u201ccancer.” No one thought it necessary to look deeper.<\/p>\n
\u201cHe had some pain in his back, so we went to the orthopaedic,” Pawar told this writer later.<\/p>\n
Earlier that afternoon, a pathologist Pawar had never met\u2014someone who had failed class 12, someone who taught himself pathology from social media site X, someone who learned the hard way not to trust tired eyes alone\u2014had seen something no one else had.<\/p>\n
Minutes after the machine flagged the anomaly, Chaitanya called Pawar\u2019s paediatrician.<\/p>\n
\u201cRefer them immediately,” he said calmly.\u201cSambhajinagar, now.”<\/p>\n
There was no debate. The boy\u2019s name didn\u2019t matter yet, nor did Chaitanya\u2019s past failures. All that mattered were the numbers the machine refused to overlook\u2014and the life they might still have a chance to save.<\/p>\n
In India\u2019s small-town labs, accurate diagnoses are often missed\u2014fatigue, human error and limited resources turn routine tests into silent gambles. But AI is changing the rules. This isn\u2019t Silicon Valley\u2019s grand vision of AI replacing doctors. Instead, it\u2019s a story about overlooked places like Parbhani, where technology helps exhausted pathologists see clearly again, catching life-threatening diseases before it\u2019s too late.<\/p>\n
In India\u2019s small-town labs, accurate diagnoses are often missed\u2014fatigue, human error, and limited resources turn routine tests into silent gambles. But AI is changing the rules.<\/p>\n
Lessons from X<\/h2>\n
In the margins of Chaitanya\u2019s r\u00e9sum\u00e9, beyond medical degrees and equipment manuals, lies a quieter credential: failure. Not the motivational kind of failure that fills TED talks and commencement speeches, but the stark, personal kind that nearly ends a journey before it begins.<\/p>\n
Chaitanya grew up in Parbhani, a small district town in Maharashtra better known for sugarcane fields than second chances. His father, an electrical engineer, died in an accident when Chaitanya was in school, leaving him to be raised by his mother. In class 12, the crucial Indian exam that determines academic futures, he failed.<\/p>\n
\u201cI failed class 12\u2026 Everyone told me to give up,” he recollected.<\/p>\n
But his mother refused to accept defeat, and slowly, so did he. Over the next three years, he regrouped, clawed through self-doubt, and finally gained admission to a medical college. His original dream\u2014neurosurgery, inspired by his father\u2019s early death\u2014was out of reach. Instead, he found himself in pathology, not by choice but circumstance: his exam scores weren\u2019t high enough for a competitive surgical residency.<\/p>\n
At first, pathology felt like a compromise. But slowly, it turned into something deeper. In 2018, while teaching pathology at Smt. Kashibai Navale Medical College and General Hospital in Pune, Chaitanya stumbled upon a Leica digital scanner\u2014unplugged and forgotten, gathering dust in a corner of the department.<\/p>\n
He asked around; no one seemed to care if he took it. He contacted the manufacturer for training and began experimenting, scanning pathology slides digitally and sharing images on X. What started as idle curiosity soon became his education. Pathologists from across the world responded to his blurry scans, corrected him, debated diagnoses, taught him what textbooks couldn\u2019t.<\/p>\n
\u2018X changed my entire scenario. Instagram reels don\u2019t teach you pathology.\u2019 \u2014 Dr Chaitanya Khillare<\/p>\n
\u201cX changed my entire scenario,” he said. \u201cInstagram reels don\u2019t teach you pathology.”<\/p>\n
Each night, after his wife and kids went to sleep, he spent hours analysing blood smears, studying subtle variations, understanding how even trained doctors could disagree over what constituted a \u201cblast” cell\u2014the marker of leukaemia. Most importantly, he reflected on his own diagnostic mistakes.<\/p>\n
\u201cI\u2019ve missed cancer myself,” he said. \u201cTwice.”<\/p>\n
He said it plainly, without drama\u2014the confession of someone who had spent countless hours hunched over a microscope, certain he had seen enough, only to realize later he hadn\u2019t. He missed diagnoses not out of carelessness, but because he was exhausted. This was why the AI scanner mattered. Not because it was infallible, but because it never grew tired, never skipped the 10th slide out of fatigue.<\/p>\n
\u201cAI isn\u2019t about speed,” Chaitanya said. \u201cIt\u2019s about not failing someone just because we\u2019re exhausted. Or because a pattern didn\u2019t jump out the first time.”<\/p>\n
The Leica scanner had given him his first glimpse of technology\u2019s potential. But the urgency crystallized only when he returned to Parbhani to open his own pathology lab above a cardiology clinic. Running a 24-hour operation, he faced a daily reality of exhaustion and relentless demand\u2014blood reports needed immediately, cardiac emergencies arriving at all hours. Fatigue was no longer theoretical; it was inescapable.<\/p>\n
When he heard about SigTuple\u2019s AI-powered device\u2014one capable of scanning blood smears, tagging cells and detecting abnormalities\u2014he didn\u2019t ask if it would be profitable. He asked if it would see what he sometimes missed.<\/p>\n
SigTuple, a Bengaluru-based AI startup specializing in medical diagnostics, builds tools designed to catch diseases before it\u2019s too late.<\/p>\n
Against everyone\u2019s advice, Chaitanya bought it\u2014a \u20b9<\/span>6.5 lakh investment in a town where most labs still used manual counting.<\/p>\n It was the SigTuple AI100 scanner that caught Prasad Pawar\u2019s son\u2019s leukaemia.<\/p>\n The device methodically analysed the smear, finding and focusing on the monolayer, where cells lie evenly spaced and clearly defined. Within minutes, it scanned hundreds of cells, measuring their size, shape and patterns, a task nearly impossible for an exhausted pathologist reviewing dozens of slides a day.<\/p>\n No one had explained algorithms or AI to Prasad. But what he understood instinctively was that something had looked closer\u2014exactly when others had looked away.<\/p>\n Within a day, the diagnosis was confirmed through flow cytometry at Sambhajinagar, a city with better medical facilities, about 200km from Parbhani. Treatment began immediately. The boy remained stable, under careful monitoring.<\/p>\n For Chaitanya, such outcomes reaffirmed his faith in the partnership he\u2019d forged with his machine\u2014one that wasn\u2019t always about spotting disease, but sometimes about ruling it out.<\/p>\n Chaitanya didn\u2019t buy the scanner because of a glossy pitch or hype. In fact, there was no pitch at all\u2014just a promise from a machine that didn\u2019t care whether the slide was stained perfectly or the day had run too long. He trusted it because it caught things he sometimes missed. And that trust was built on years of meticulous engineering by someone who understood how fragile frontline diagnosis could be.<\/p>\n The AI-powered scanner that flagged 86% blast cells in that boy\u2019s smear was developed by Tathagato Rai Dastidar, co-founder of SigTuple. Tathagato had spent years building tools meant for labs that never appeared in glossy brochures\u2014places like Parbhani, where blood samples are mis-processed, staining is uneven and technicians might never have completed formal training.<\/p>\n \u201cYou walk into a lab in India, and 8 out of 10 times, the smearing or staining is poor,” Dastidar told this writer. \u201cIf we didn\u2019t build for that, we\u2019d be building for nobody.”<\/p>\n \n\t\t\t\t\tView Full Image<\/span>\n\t\t\t\t<\/p>\n<\/p><\/div> SigTuple\u2019s scanner doesn\u2019t simply magnify slides; it replicates the thought process of a skilled pathologist at machine speed. A single drop of blood, smeared on a glass slide and stained, is loaded into the AI100. The scanner quickly finds the monolayer\u2014the ideal zone where cells are clear, distinct and undistorted. It then captures high-resolution images, identifies and counts white blood cells, and pinpoints any morphological anomalies.<\/p>\n It doesn\u2019t stop at a glance. It reviews dozens, sometimes hundreds, of cell images to ensure nothing subtle is missed. In under two minutes, it performs a level of analysis that would take a human nearly half an hour, flagging cells that appear suspicious, uploading findings to the cloud and creating a detailed digital report.<\/p>\n \u201cWe\u2019ve seen staining quality improve in labs within days of installing our scanner,” Dastidar said. \u201cBecause now, nothing gets discarded. Everything is seen.”<\/p>\n What SigTuple created is more than a faster microscope; it\u2019s a workflow that remembers. Once analysed, no smear vanishes into a forgotten folder. Each questionable cell, each subtle anomaly, can be retrieved, reconsidered or re-examined months or even years later.<\/p>\n \u201cThis isn\u2019t about man versus machine,” Dastidar explained. \u201cAI sees the sample. It doesn\u2019t know the patient. But when used correctly, it helps the doctor see better.”<\/p>\n \u2018AI sees the sample. It doesn\u2019t know the patient. But when used correctly, it helps the doctor see better.\u2019 \u2014 Tathagato Dastidar<\/p>\n This subtle yet crucial partnership bet-ween human intuition and machine vigilance is precisely what Umakant Soni, an experienced AI ecosystem builder closely familiar with SigTuple, emphasized.<\/p>\n \u201cIndia\u2019s rural and semiurban healthcare infrastructure is fragmented, stretched thin, often running under conditions of constant fatigue,” Soni said. \u201cYou see compounders playing the role of doctors. Technicians working without proper training. Historical patient data is rare or non-existent.”<\/p>\n For Soni, the critical insight behind SigTuple\u2019s technology wasn\u2019t simply its sophistication under ideal conditions\u2014but its resilience under real-world pressures. \u201cAI models here must adapt to noise, to incomplete information, to messy realities,” he said. \u201cThey must work offline, speak in vernacular languages, and fit into the chaotic rhythms of frontline care.”<\/p>\n Soni believes systems like SigTuple\u2019s can dramatically reshape rural healthcare. \u201cThere\u2019s a powerful case for using AI-driven screening at the primary health centre level,” he argued. \u201cRight now, too many preventable cases escalate into full-blown health crises that overwhelm urban hospitals like Tata Memorial. Early diagnostics could stop these tragedies before they start.”<\/p>\n Not every misdiagnosis unfolds in a rural lab or culminates dramatically in a paediatric leukaemia ward. Some happen over days and weeks, even in cities where good doctors and advanced diagnostics are easily accessible. But the experience\u2014the confusion between what you\u2019re told and what you feel\u2014is no less disorienting. That\u2019s why Anuruddh Mishra\u2019s story belongs here, as a counterpoint to the urgency that saved a child in Parbhani.<\/p>\n \n\t\t\t\t\tView Full Image<\/span>\n\t\t\t\t<\/p>\n<\/p><\/div> In early 2020, just weeks before India entered its covid-19 lockdown, Anuruddh, a tech product builder living in Bengaluru, began noticing stiffness in his fingers. Then came sharp pains in his knees. Initially, he brushed it off. But as discomfort grew, anxiety prompted what most urban Indians with access tend to do: he booked multiple tests, consulted telehealth platforms and tried to decode medical jargon on his own.<\/p>\n Three different labs returned the same troubling result: elevated anti-cyclic citrullinated peptide (anti-CCP) levels\u2014a biomarker that pointed toward early-onset rheumatoid arthritis. A specialist agreed it was worrying, though perhaps not advanced enough yet to warrant aggressive medication. Uncertainty lingered.<\/p>\n Over the months that followed, his pain intensified. One morning, as he stood cooking breakfast, Anuruddh\u2019s right knee abruptly gave out beneath him. \u201cI told myself I\u2019d walk into the airport that day without assistance,” he later recalled, still vividly remembering his determination to resist the narrative of disability creeping into his twenties.<\/p>\n Then came an unexpected twist. New tests from another lab returned clean: no anti-CCP. No rheumatoid arthritis. The source of his discomfort turned out to be something entirely different\u2014a simple dietary imbalance involving elevated uric acid and vitamin D deficiency, easily manageable with changes in diet and supplements.<\/p>\n Relief, however, was quickly replaced by frustration. \u201cI had access. I wasn\u2019t poor, wasn\u2019t rural,” Anuruddh explained, visibly animated by lingering disbelief. \u201cBut for months, I was left guessing, worrying and confused. All I had were conflicting numbers.”<\/p>\n This personal turmoil sparked the creation of August.AI, a conversational assistant designed not to replace doctors, but to offer clarity and reassurance in the chaotic silence between visits. \u201cI wasn\u2019t building an AI doctor,” Anuruddh emphasized. \u201cI was creating the companion I desperately needed\u2014one who says, \u2018I see exactly what you\u2019re seeing. Let\u2019s figure this out together.\u2019”<\/p>\n \n\t\t\t\t\tView Full Image<\/span>\n\t\t\t\t<\/p>\n<\/p><\/div> Today, August.AI serves millions of patients, primarily in towns far smaller and less equipped than Bengaluru. It has prevented dangerous drug interactions, alerted liver donors to contraindicated medications and correctly identified the cause of fainting episodes as a missed vaccine. Anuruddh\u2019s own months-long brush with confusion turned into a deeply empathetic digital companion.<\/p>\n He isn\u2019t a doctor, but his experience echoes the central question that Chaitanya had grappled with in his own lab: What if someone had just looked a little closer?<\/p>\n Most medical labs in towns like Parbhani aren\u2019t built to remember\u2014they\u2019re built to churn. Samples arrive, are processed quickly, and the paper reports vanish into the vast machinery of India\u2019s fragmented healthcare system. Patients rarely return for follow-ups, and their medical history often fades, lost or discarded.<\/p>\n But Chaitanya\u2019s lab refuses to forget.<\/p>\n \u201cPatients vanish,” he said, voice steady yet gentle. \u201cBut their cells don\u2019t have to.”<\/p>\n This isn\u2019t merely a line he repeats; it\u2019s the core philosophy of the lab. Here, each smear and scan is uploaded, meticulously archived and stored securely in the cloud within minutes, even at odd hours when most technicians are sleep-deprived and mistakes are commonplace. The lab runs 24 hours, reflecting the urgent heartbeat of the cardiology clinic below. Night or day, emergencies arrive. Slides are hurriedly prepared, fed into the AI scanner and remembered permanently.<\/p>\n \n\t\t\t\t\tView Full Image<\/span>\n\t\t\t\t<\/p>\n<\/p><\/div> Memory, Chaitanya has learned, can be transformative. One case stood out vividly when we spoke: a young girl whose fever had lingered too long. Her paediatrician suspected rheumatic fever\u2014a condition easily missed but devastating in its consequences if not diagnosed early. The initial lab results\u2014an anti-streptolysin O (ASO) titer\u2014came back normal. But Chaitanya couldn\u2019t shake his unease.<\/p>\n \u201cThe doctor kept saying it didn\u2019t feel resolved,” he recalled. \u201cI felt it too.”<\/p>\n Chaitanya insisted on another test, sending the sample nearly 300km away to a certified lab in Hyderabad. Three tense days later, the results came back positive, confirming the initial suspicion\u2014acute glomerulonephritis, a serious kidney condition.<\/p>\n \u201cThat night was restless,” Chaitanya confessed. \u201cShe was just a child. If that report had been missed, six years later, she would surely have developed heart disease.”<\/p>\n Chaitanya\u2019s refusal to erase ambiguity can feel counterintuitive in a system that values quick results and neat answers. Yet it has already proven crucial. Once, a young girl arrived with a swollen lymph node. Lymphoma was suspected, but her family, wary and perhaps overwhelmed, refused further tests. They disappeared without leaving even a phone number behind.<\/p>\n Almost a year later, the girl returned. This time, the mass was larger, visibly worse. Chaitanya, recalling their abrupt departure, didn\u2019t have to rely on fragmented memory or scribbled notes. He still had the cells\u2014from the very first visit, carefully digitized and saved.<\/p>\n \u201cI didn\u2019t need a fresh biopsy,” he explained. \u201cI had the truth, right there, from the last time she came.”<\/p>\n Diagnostics, especially in places like Parbhani, is often about speed, about navigating between fatigue and efficiency, about clearing the next report from a backlog. But for Chaitanya, persistence has become just as crucial as precision. He sees his AI scanner not only as a diagnostic companion but as a witness, capable of remembering every overlooked cell, every ambiguous smear, every patient who slips from the waiting room into uncertainty.<\/p>\n \n\t\t\t\t\tView Full Image<\/span>\n\t\t\t\t<\/p>\n<\/p><\/div> In Parbhani, where diagnostic delays often mean irreversible outcomes, there is little room for error.<\/p>\n \u201cIt\u2019s not about being faster,” Chaitanya told this writer late one evening. \u201cIt\u2019s about not failing someone because we were tired or distracted.”<\/p>\n This commitment connects him to others who refuse to accept the routine failures in healthcare.<\/p>\n Tathagato Rai Dastidar, who built SigTuple\u2019s scanner, didn\u2019t create it for venture capital pitches or tech headlines. He built it to withstand the frustrating realities of Indian labs\u2014poor staining, uneven training and weary technicians. \u201cAI doesn\u2019t blink,” Dastidar said. \u201cEvery sample gets the same attention.” In Bengaluru, Anuruddh Mishra felt first hand the pain of misdiagnosis. It drove him to build August.AI\u2014not to replace doctors, but to offer clarity when medical answers felt cold or distant. These three paths cross not because they share a vision of futuristic technology, but because they refuse to normalize neglect.<\/p>\n Not every story ends well. Chaitanya knows this. Weeks after this writer\u2019s visit, he described a case that still haunted him. Late one night, a three-year-old\u2019s blood report showed extremely high white blood cell counts. The AI quickly ruled out leukaemia and sepsis. Troubled, Chaitanya called the paediatrician and urged him to look deeper.<\/p>\n Hours later, the doctor discovered the child had stopped drinking water and was showing signs of hydrophobia\u2014a hallmark of rabies. There was no visible bite or wound history, just the silent, devastating progression of the disease. The child, born after his parents tried for 17 years, could not be saved.<\/p>\n \u201cAI didn\u2019t diagnose rabies,” Chaitanya later wrote on LinkedIn, openly grappling with the outcome. \u201cBut it helped rule out leukaemia and sepsis quickly. Sometimes integrity means doing everything right\u2014even when it won\u2019t change the outcome.”<\/p>\n For Chaitanya in Parbhani, AI isn\u2019t revolutionary. It\u2019s just one more safeguard against the simplest yet harshest failure: missing something because you were too tired, too rushed, or too human.<\/p>\n <\/div>\n","protected":false},"excerpt":{"rendered":" Inside, Dr Chaitanya K. sits in front of a microscope and a half-drunk cup of tea. On the wall, a faded sign reads in hand-painted Marathi: \u201cAccurate diagnosis, unwavering trust.” But here, accuracy has always been a battle fought against exhaustion, distractions and overwhelming demand. A paediatric blood report arrives on Chaitanya\u2019s screen. The numbers […]<\/p>\n","protected":false},"author":2,"featured_media":394522,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[8],"tags":[36708,3904,36705,284,188,183,185,186,36079,36713,36704,866,36706,36710,36702,187,184,36701,36709,4226,36715,36707,36711,189,36712,150,182,36714,190,36703],"class_list":["post-394521","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-technology","tag-ai-replacing-doctors","tag-ai-startup","tag-ai-powered-scanner","tag-artificial-intelligence","tag-blockchain-tech","tag-blockchain-technology","tag-crypto-technology","tag-cryptocurrency-technology","tag-diagnosis","tag-diagnostics","tag-dr-chaitanya-k","tag-healthcare","tag-leukaemia","tag-life-threatening-diseases","tag-maharashtra","tag-metaverse-technology","tag-nft-technology","tag-parbhani","tag-pathologists","tag-pune","tag-rheumatoid-arthritis","tag-sambhajinagar","tag-sigtuple","tag-soul-bound-token","tag-tathagato-rai-dastidar","tag-tech","tag-technology","tag-telehealth-platforms","tag-token-technology","tag-udyati-pathology-lab"],"_links":{"self":[{"href":"https:\/\/dripp.zone\/news\/wp-json\/wp\/v2\/posts\/394521","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/dripp.zone\/news\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/dripp.zone\/news\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/dripp.zone\/news\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/dripp.zone\/news\/wp-json\/wp\/v2\/comments?post=394521"}],"version-history":[{"count":1,"href":"https:\/\/dripp.zone\/news\/wp-json\/wp\/v2\/posts\/394521\/revisions"}],"predecessor-version":[{"id":394523,"href":"https:\/\/dripp.zone\/news\/wp-json\/wp\/v2\/posts\/394521\/revisions\/394523"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/dripp.zone\/news\/wp-json\/wp\/v2\/media\/394522"}],"wp:attachment":[{"href":"https:\/\/dripp.zone\/news\/wp-json\/wp\/v2\/media?parent=394521"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/dripp.zone\/news\/wp-json\/wp\/v2\/categories?post=394521"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/dripp.zone\/news\/wp-json\/wp\/v2\/tags?post=394521"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}What the machine saw<\/h2>\n
Eyes that don\u2019t tire<\/h2>\n
\n\t\t\t<\/figcaption><\/figure>\n<\/div>\nA different kind of miss<\/h2>\n
![\"A](\"https:\/\/www.livemint.com\/lm-img\/img\/2025\/06\/08\/600x338\/Anuruddh_1749365260128_1749365296259.jpeg\" "\\"A")
\n\t\t\t\t <\/source><\/picture>\n
\n\t\t\t<\/strong><\/figcaption><\/figure>\n<\/div>\n![\"A](\"https:\/\/www.livemint.com\/lm-img\/img\/2025\/06\/08\/600x338\/August_AI_1749369209223_1749369642841.jpeg\" "\\"A")
\n\t\t\t\t <\/source><\/picture>\n
\n\t\t\t<\/figcaption><\/figure>\n<\/div>\nThe lab that doesn\u2019t forget<\/h2>\n
![\"Dr](\"https:\/\/www.livemint.com\/lm-img\/img\/2025\/06\/08\/600x338\/Chaitanya_2_1749368845432_1749368919224.jpeg\" "\\"Dr")
\n\t\t\t\t <\/source><\/picture>\n
\n\t\t\t<\/figcaption><\/figure>\n<\/div>\n![\"Dr](\"https:\/\/www.livemint.com\/lm-img\/img\/2025\/06\/08\/600x338\/Chaitanya_with_colleagues_1749368971784_1749369021991.jpg\" "\\"Dr")
\n\t\t\t\t <\/source><\/picture>\n
\n\t\t\t<\/figcaption><\/figure>\n<\/div>\nNot to fail someone<\/h2>\n