The race to bring artificial intelligence into clinical medicine has drawn billions in venture capital and spurred a wave of research from both tech giants and startups, but the path from laboratory promise to everyday hospital use remains uneven. In 2021 alone, $4.5 billion poured into AI healthcare startups, funding clinical trials and regulatory submissions across a range of diagnostic applications. Google Health, a division of Alphabet, has published studies covering three disease areas where AI has shown particular promise: diabetic retinopathy, breast cancer screening, and lung cancer detection.
Meanwhile, older efforts such as IBM Watson Health — launched in 2015 to use AI for oncology decision support — faced challenges and was sold in 2022 to Francisco Partners, illustrating the commercial fragility that can accompany technically impressive systems.
Clinical Promise and Regulatory Realities
Google Health’s published studies focus on conditions where missed diagnoses carry serious consequences. Diabetic retinopathy, a leading cause of blindness, can be detected from retinal photographs — a task well suited to pattern recognition algorithms. Breast cancer screening with mammography is a high-volume task where AI could serve as a second reader, and lung cancer detection from CT scans is another area where subtle nodules are easily missed by human eyes.
In 2018, the company’s AI system for diabetic retinopathy underwent clinical testing in India and Thailand, among the first attempts to deploy AI screening in resource-limited settings. These tests represented a step toward expanding access to screening in regions with shortages of specialists.
Yet regulatory clearance has been uneven across the field. Viz.ai, a startup founded in 2016 that uses AI to detect strokes from CT scans, received FDA clearance in 2018, making it one of the earlier AI tools to reach U.S. hospitals. That milestone highlighted a path to clinical use, but many other systems remain in research phases.
The $4.5 billion invested in 2021 has funded a wave of clinical trials and regulatory submissions, leading to the question of whether these tools will achieve widespread adoption in healthcare systems, or whether they will follow the path of IBM Watson Health — technically impressive but commercially fragile. Google Health, PathAI, Viz.ai, and Tempus all continue to develop their platforms.
A Legacy of Health Tech Ambitions
Google’s involvement in health technology dates back to the late 2000s, with the original Google Health — a personal health record service that allowed users to store and manage their medical information online. That service, discontinued in 2012, was an early attempt to centralize health data in digital form.
Around the same time, Google also developed Google Flu Trends, which used search queries to estimate influenza activity, and Google Body, a three-dimensional anatomical browser. These projects reflected a broader interest in applying data and software to health challenges, though many were later discontinued as priorities shifted. Google Health as a division emerged later, focusing on artificial intelligence and machine learning for medical imaging and diagnostics.
Unlike earlier efforts that centered on personal data storage, this iteration emphasized clinical applications such as detecting diabetic retinopathy from retinal scans and identifying cancer in mammograms or CT images. The division’s work built on decades of research into computer-aided diagnosis, which gained momentum as computational power increased and large datasets became available.
Early systems in the 1980s and 1990s laid the groundwork for pattern recognition in medical images, though adoption was limited by technology constraints. By the 2010s, advances in deep learning enabled more sophisticated analysis, drawing investment from both tech giants and startups.
Startups Charting Their Own Paths
Alongside Google Health, a cluster of startups has taken different approaches to applying AI in medicine. PathAI, founded in 2016, develops AI for pathology and raised $60 million in Series B funding in 2018. Viz.ai, also founded in 2016, uses AI to detect strokes from CT scans.
Tempus, founded in 2015, uses AI to analyze clinical and molecular data for personalized cancer treatment. These companies represent a diverse set of strategies, from imaging to molecular analysis, each seeking to address specific gaps in clinical workflows.
The coming years will show whether the clinical evidence matches the investment hype, and whether regulators and insurers will clear the way for broader use. If validated, such systems could expand access to screening in regions with shortages of specialists, reduce delays in diagnosis, and assist overburdened healthcare systems. Yet the path from research to routine use remains uneven, with regulatory, ethical, and practical hurdles still to be resolved.
Google Health’s current work sits within this broader effort to determine whether AI can reliably complement — or, in some cases, augment — human expertise in patient care.
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