Artificial intelligence has long promised to transform healthcare, yet widespread adoption has often been hindered by challenges around clinical trust, workflow integration, and demonstrating measurable value. Brainomix has emerged as one of the notable success stories in translating AI from innovation to routine clinical practice, particularly in stroke care and advanced medical imaging. At BIO International Convention 2026 in San Diego, BioSpectrum Asia spoke with Michalis Papadakis, CEO & Co-Founder of Brainomix, about the factors driving real-world adoption of healthcare AI, the growing role of imaging analytics in pulmonary fibrosis, and why the future of medical AI will be judged not by algorithm performance alone, but by its ability to improve patient outcomes, expand access to treatment, and deliver sustainable value across healthcare systems.
AI in healthcare often faces adoption barriers. What has enabled Brainomix to achieve real-world implementation?
One of the biggest challenges in healthcare AI is bridging the gap between promising technology and routine clinical use. At Brainomix, we focused from the outset on solving real clinical problems and ensuring our solutions fit seamlessly into existing workflows, and I think this foundational discipline is what has ultimately driven our adoption.
Several factors have come together to enable successful real-world implementation. We’ve built strong clinical evidence, with numerous peer-reviewed studies demonstrating the impact of our technology on patient outcomes and treatment decisions. Our platform easily integrates into hospital infrastructure, allowing clinicians to access AI-driven insights without disrupting established processes. We’ve worked closely with healthcare providers, stroke networks, and pharmaceutical partners to ensure the technology addresses genuine unmet needs and delivers measurable value.
Crucially, adoption has been driven by clinicians themselves. As physicians recognize the technology’s clinical value and gain confidence in its outputs, they become champions for its use within their institutions and networks. Because Brainomix is designed to augment rather than replace clinical expertise, clinicians trust it as a decision-support tool that provides actionable information to inform diagnosis and treatment decisions.
This combination of clinical trust, strong evidence, seamless integration, regulatory approvals, reimbursement pathways, and dedicated implementation support has enabled Brainomix to move beyond pilot projects and achieve widespread adoption in routine clinical practice.
How is AI changing stroke care pathways globally?
AI is transforming stroke care pathways globally by helping more patients receive the right treatment and improving outcomes. The greatest contribution of AI in stroke is not simply making workflows more efficient, but expanding access to treatment, which increases the number of patients who receive effective therapies and improves outcomes at scale.
One of the most important advances has been in acute stroke imaging, where AI can identify large vessel occlusions, quantify infarct core and salvageable tissue, and provide objective decision support. This enables clinicians to identify patients who may benefit from reperfusion therapies, including thrombectomy and thrombolysis, and has contributed to increased treatment rates and better functional outcomes.
AI is also helping to standardize and democratize stroke care in ways that have been previously difficult to achieve. In regions with limited access to neuroradiologists or stroke specialists, AI-powered tools provide consistent image interpretation and support treatment decisions, enabling more patients to be appropriately referred and treated. By reducing variability in assessment, AI helps ensure that patients have access to evidence-based care regardless of geography or local expertise.
The impact extends beyond individual hospitals. Across stroke networks, AI is enabling more patients to be identified for life-changing interventions and supporting broader adoption of advanced stroke therapies. As a result, more patients are surviving stroke with less disability and a greater chance of returning to independent living.
What opportunities exist for AI imaging in pulmonary fibrosis?
Pulmonary fibrosis represents one of the areas where AI imaging has the potential to make a profound and immediate difference. Patients with Interstitial Lung Diseases (ILD) may progress to progressive pulmonary fibrosis (PPF), a condition marked by irreversible lung damage and increased risk of early mortality. Without treatment, patients may have a lifespan as short as five years, yet many still endure long delays - often years - before receiving a diagnosis. Early diagnosis and intervention can play a major role in a patient’s prognosis, but determining which patients are eligible for treatment based on imaging remains challenging, even for experienced specialists.
AI imaging now offers several promising opportunities across diagnosis, disease monitoring, and clinical research in this disease space. AI algorithms can enhance early detection by identifying subtle interstitial lung abnormalities on high-resolution CT (HRCT) scans that may be missed or underestimated by visual assessment alone. Automated pattern recognition can help differentiate fibrosis, quantify the extent of disease, and support more accurate classification of fibrosing lung diseases.
AI-driven quantitative imaging provides objective biomarkers, which can help physicians better understand disease burden and progression. Longitudinal analysis of CT scans can measure changes in fibrosis, enabling more sensitive monitoring than traditional visual scoring or pulmonary function tests. These imaging biomarkers may aid physicians to improve prognostication and help identify patients at risk of fast progression.
Finally, AI imaging has the potential to accelerate drug development and clinical trials by providing reproducible, quantitative endpoints, reducing inter-observer variability, and enabling more efficient patient stratification. As these technologies mature and undergo validation, AI-based imaging tools could become valuable adjuncts to radiologists and pulmonologists, improving both patient care and research in pulmonary fibrosis.
What evidence do healthcare systems now require before deploying AI platforms at scale?
Healthcare systems are no longer willing to deploy AI platforms at scale based solely on promising pilot results. Increasingly, they require robust evidence that demonstrates both clinical and operational value in real-world settings.
This evidence typically includes:
As healthcare organizations move from experimentation to enterprise-wide adoption, the standard of evidence is increasingly resembling that required for other clinical technologies. Healthcare authorities want proof not only that AI works, but that it delivers sustainable value, can be trusted, and improves outcomes in routine practice.
How do you define success for medical AI beyond algorithm performance?
I define success for medical AI by its impact on patient outcomes and expanding access to life-saving treatments, not just algorithm benchmark performance. Strong accuracy is necessary, but only a foundation.
The real measure of success is whether AI enables more patients to receive the right treatment at the right time. In stroke, this means identifying more eligible patients for thrombectomy or thrombolysis and having access to specialist-level decision support regardless of geography or resources. Ultimately, success is measured by lives saved, fewer disabilities, and patients returning to independent living.
Beyond algorithm performance, I look at increases in treatment rates, shifts in clinical decision-making, improved outcomes, health equity, and adoption by clinicians who trust the technology. AI must integrate into workflows and perform reliably across various populations.
In healthcare, AI is considered successful not solely for its predictive accuracy but also for its ability to facilitate access to proven therapies and deliver measurable improvements in care on a broad scale. The creation of real value occurs when AI helps bridge the gap between patients who could benefit from treatment and those who actually receive it.
What is the single biggest takeaway from BIO 2026 that will influence your company's strategy over the next 12 months?
As we consider the conversations likely to shape BIO this year, I expect two themes to be particularly important to our strategy in the year ahead. The first is the growing convergence of AI, diagnostics, and therapeutics, creating new opportunities to support precision medicine. The second is the growing demand for technologies that can demonstrate measurable clinical and economic impact in real-world settings. Both reinforce our focus on developing AI-powered imaging solutions that help clinicians and researchers make better decisions and deliver better patient outcomes.
