How a Portable AI Device is Helping Women in Rural India Detect Breast Cancer Early

In a primary health centre in Goa, a frontline health worker holds a small, battery-powered device against a patient’s chest. It only takes a few minutes for the device to complete the scan. There is no radiation, no bulky machine humming in the background, no specialist radiologist required in the room. 

The procedure is painless, radiation-free, and non-invasive. The device is perfect for procedures like mammography, says Mihir Shah, Co-founder and CEO of medtech company UE LifeSciences, in conversation with AIM.

The result appears instantly. For thousands of women who may otherwise never have entered a formal screening pathway, this simple and brief procedure could be the first step toward early cancer detection.

For India, where breast cancer continues to exact a heavy toll, that difference matters. According to GLOBOCAN 2022 estimates, India recorded approximately 1.9 lakh new breast cancer cases in 2022, with 98,337 deaths that year. Some estimates suggest one woman dies of breast cancer every eight minutes in the country. 

Survival data underscores the urgency: five-year survival rates in India hover around 60–70%, compared to 85–90% in many high-income countries. While multiple factors influence survival, including treatment access, late detection remains central to the gap.

Companies like US-based UE LifeSciences and Goa-based Molbio Diagnostics are attempting to address through AI-enabled, sensor-driven tools such as iBreastExam.

Adapting Preventing Care in India

Shah notes that in India, demand for clinical tests dramatically outstrips clinician supply. The situation is worsened by uneven access to primary and secondary care and a rising burden of non-communicable diseases requiring tests.

Traditional screening infrastructure has struggled to keep pace. Mammography remains an important diagnostic tool, but scaling it equitably across India’s vast population poses structural hurdles: high costs of imaging equipment, radiation exposure, a shortage of trained radiologists, and the urban concentration of facilities. 

Also, deploying mammography vans across rural and peri-urban India is capital-intensive and operationally complex.

iBreastExam positions itself differently. Rather than imaging, the handheld device uses dynamic co-planar capacitive sensors to detect differences in tissue elasticity—based on the biological principle that tumours tend to be stiffer than surrounding healthy tissue. 

Shah describes it “like a doctor’s manual palpation but digitised and much more sensitive.” It is radiation-free, non-invasive, and designed to operate on low power, making it portable enough for use in primary health centres.

The technology’s evolution, Shah notes, was iterative. Early explorations included thermography and piezoelectric approaches similar to ultrasound, but limitations in scalability and infrastructure requirements pushed the team toward a capacitive sensor approach—analogous in principle to smartphone touchscreen technology. The goal was to build a triage tool capable of screening large populations quickly and comfortably.

That triage logic is central. iBreastExam is not positioned as a replacement for mammography, but as a pre-screening mechanism—identifying women who require follow-up imaging and specialist care. In public health terms, it aims to widen the top of the funnel.

Chandrasekhar Nair, Chief Technical Officer at Molbio Diagnostics, describes AI in this context as a “force multiplier.” India’s demographic profile, he argues, presents both opportunity and warning. Molbio is in partnership with UE LifeSciences. While the country remains younger than many high-income nations, the steady rise of cancers and chronic diseases is straining healthcare capacity. Specialist availability cannot grow proportionally with demand. AI-driven tools, if validated and responsibly implemented, can support early triage, screening coverage, and programme-level monitoring.

“Trustworthiness is not a marketing attribute—it is foundational to adoption,” Nair says, particularly in tier-2 and tier-3 cities and rural regions, where reputational damage from poorly validated technologies can spread quickly. In healthcare, even small errors carry outsized consequences.

Quality Management

This emphasis on validation and governance surfaces repeatedly. 

Shah notes that the company was cautious about adopting AI due to the high stakes involved in handling patient identity and medical data. The approach avoids centralised AI architectures, aligning with privacy standards such as HIPAA (in US) and GDPR (in EU). Before deployment, AI-enabled features undergo extensive testing and validation.

Strategically, AI within the platform is used more for programme-level quality management and pattern recognition—such as monitoring scan quality at scale and generating aggregated heatmaps—rather than making fully automated, one-off diagnostic judgements on individual exams. This design choice, Shah suggests, reduces the risk of AI-driven errors directly affecting single-patient decisions while still improving operational oversight.

Impact of Early Detection

Real-world deployments of the iBreast Exam provide a measure of scale. 

In Goa, the Swasth Mahila Swasth Goa initiative—a state-wide breast cancer screening programme implemented across 20 primary health centres in partnership with the state health ministry and philanthropic partners—screened more than 1,60,000 women between October 2021 and January 2024. The programme identified over 3,300 lesions, facilitated more than 2,000 follow-up diagnostic imaging procedures, and confirmed 73 cancers. 

In Durg, Chhattisgarh, a 45-day rollout under the National Programme for Prevention and Control of Non-Communicable Diseases screened 12,686 women, surpassing its target. The effort identified 573 lesions, enabled 96 diagnostic imaging follow-ups, and resulted in 92 women receiving treatment and four undergoing surgery. Even in a compressed deployment window, the programme moved beyond detection into documented downstream care.

Community-led models have also emerged. In Mumbai, a Rotary-supported initiative has conducted over 11,000 scans, with hundreds of positive screenings flagged for further evaluation. The emphasis, again, is on measurable follow-through rather than isolated screening events.

The broader implications extend beyond breast cancer alone. If preventive, AI-enabled screening technologies are responsibly integrated into public health systems, Nair argues, the benefits ripple outward: earlier detection, reduced treatment intensity, lower financial strain on families, and fewer late-stage presentations overwhelming tertiary centres. Health administrators gain better data visibility for planning and resource allocation.

At a societal level, improved early detection can influence workforce participation and long-term economic resilience. Preventive screening becomes not merely a clinical intervention but a structural investment.

Yet scaling such technologies across India’s vast and uneven healthcare landscape demands alignment with government programmes and infrastructure realities. Tools designed for rural deployment must operate reliably amid intermittent connectivity, constrained power supply, and varying skill levels among frontline workers. Deployment alone does not equal scale; measurable outcomes and digital traceability are equally critical.