The gap in data diversity has long been one of the most serious weaknesses in modern medicine, particularly in oncology, where clinical decisions often depend on patterns found in large datasets. PAICON, a young healthtech company rooted in artificial intelligence and cancer research, has stepped directly into this problem with the goal of building cancer diagnostics that work for the entire world rather than only for the populations that existing models tend to reflect. According to Dr Manasi A Ratnaparkhe, CEO and co founder of PAICON, most cancer AI systems presently rely on datasets drawn primarily from Western countries, which cover only a small portion of the global population. She explains that the majority of people worldwide remain unrepresented, and that this lack of diversity contributes to missed diagnoses and avoidable harm. She describes PAICON’s mission as an effort to correct this imbalance and bring more fairness and accuracy to cancer diagnostics.
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PAICON introduced its work at the first WHX Tech event in Dubai, where the company also joined the Xcelerate startup competition that brought together fifty young companies developing new solutions for healthcare. At the core of PAICON’s approach is the PaiX Cancer Data Lake, which the company describes as a globally curated and standardised repository containing data from more than one hundred and thirty thousand cancer cases across more than sixty countries. Dr Ratnaparkhe calls it the first cancer data lake designed at a truly global scale. She explains that the wider the range of genetic backgrounds, tissue features and regional variations included in the dataset, the more reliable and accurate the resulting AI models become. She points to examples where patients suffered complications because the genetic variants that shaped their response to treatment were common in their own populations but completely absent from reference datasets built almost entirely around Caucasian patients. She views such cases as powerful reminders of why inclusive data is critical.
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The company’s AI tools fit directly into the clinical workflow. One of its leading models, SatSight DX, can identify microsatellite instability in colorectal cancer from a digital slide. This task usually requires molecular testing that takes weeks and tends to be costly. PAICON’s technology performs the analysis in about an hour at a fraction of the cost. Dr Ratnaparkhe explains that this brings precision oncology within reach for more hospitals and patients. Another tool, PaiNet, is designed to connect oncologists around the world to exchange opinions supported by AI assisted slide interpretation. It blends automated analysis with the judgement of experienced clinicians, providing a balance between computational accuracy and human insight.
PAICON has built its products with regulatory expectations in mind. All development takes place within an ISO 13485 certified quality system that traces each stage from concept to deployment. The company partners with hospitals across different regions to complete multi site clinical validation. It follows international privacy and security laws, including GDPR and HIPAA, and adapts its architecture where governments require data to remain within national borders. To achieve this, PAICON uses local or federated systems so that sensitive patient information never has to move. Dr Ratnaparkhe explains that these choices reflect a commitment to responsible AI use and align with global guidelines issued by organisations such as the World Health Organization.
Interest in PAICON’s work has grown quickly. Clinicians in Europe, India and the Middle East report that the tools give them faster access to molecular level insights and greater confidence in complex cases. Researchers in the pharmaceutical industry and government health organisations have also taken notice. Many see value in the diverse data within the PaiX Cancer Data Lake, since it can help identify new biomarkers, broaden clinical research and reduce long standing issues with trial diversity. Dr Ratnaparkhe stresses that the company’s progress depends on cooperation across the healthcare ecosystem. She explains that hospitals contribute clinical experience, pharmaceutical firms supply research capacity and public institutions guide responsible stewardship of health data.
The company’s approach also has an economic dimension. Accelerating and simplifying diagnostic processes reduces both cost and clinical burden. Dr Ratnaparkhe points out that systems benefit when patients spend less time waiting for results, when unnecessary tests are avoided and when treatments begin earlier. She notes that these gains are especially meaningful for regions with limited laboratory infrastructure and restricted budgets. In such places, the ability to deliver precision oncology without expensive sequencing could reshape the economics of cancer care.
A strong theme throughout PAICON’s work is the idea of responsibility in AI. Dr Ratnaparkhe says that bias begins when communities are not represented. The company uses diverse datasets, extensive human review and traceable model development to reduce the risk of uneven performance. Tools such as PaiNet support clinician oversight, allowing specialists to interpret AI findings with clarity. She explains that PAICON’s definition of responsible AI centres on trust, reliability and transparency for everyone involved, including doctors, patients and regulators.
Looking ahead to the next five years, PAICON aims to set a new global standard for cancer AI, one that recognises differences across populations rather than ignoring them. Dr Ratnaparkhe envisions a future where a patient in cities as distant as Santiago, Dubai, Nairobi, Mumbai, Tokyo or Munich can rely on the same level of diagnostic accuracy from the technology. She believes the PaiX Data Lake will support not only PAICON’s models but also new tools created with partners, and she hopes that regulatory systems will evolve so that diversity becomes a requirement rather than an afterthought.
The company’s message comes at a significant moment. Investment in health AI continues to grow at record speed. Digital health funding worldwide reached more than twenty billion dollars in the first three quarters of 2025, according to data from Galen Growth. Despite this momentum, concerns remain about fairness and generalisation across different populations. As healthcare moves into a future where diagnostics rely as much on datasets as on physical samples, PAICON’s work highlights a simple truth. If data does not reflect the world, the AI model built from it will also fall short. By building systems that recognise the full range of human diversity, the company hopes to improve care and offer more people access to the benefits of modern cancer technology.
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