AI IN HEALTHCARE: A Broad Overview of the International State of Play
From Surgical Robots to Data Breaches:
What the Numbers Tell Us About AI in Healthcare Right Now
Artificial Intelligence in healthcare is, arguably, the fastest-growing, most opportunity-laden - and also the most risk-ridden - field of AI application globally right now . . . and almost certainly will be for years to come.
According to a February 23, 2026, analysis by Fortune Business Insights of AI in the global healthcare sector, the 2025 market was valued at USD 39.34 billion – with growth projected to reach USD 56.01 billion in 2026, and USD 1,033.27 billion by 2034.
In 2025, the United States dominated the AI-in-healthcare market with a market share of 44.5 percent. That market is propelled by the high adoption of AI-based technologies in medical imaging, diagnostics, and patient monitoring. Its growth is also fueled by major collaborations between recognised top-tier players.
Other countries are, however, moving along as fast as they can.
Japan is making increasing investments in AI-based medical devices for the diagnosis of various diseases, along with its general focus on integrating advanced technology into its healthcare system.
China's market is expanding due to significant investments driven, as you would expect, by the push to upgrade its healthcare infrastructure to handle the needs of its large population.
Europe appears to be concentrating on increasing AI integration into hospitals and clinics for managing patient health records, administrative workflows, diagnostics, and surgery. The presence of major pharma and biotech companies adopting AI for innovative drug discovery, particularly in the UK and Germany, is a key growth factor.
The opportunities are truly futuristic-sounding albeit they’re already here: robot-assisted surgery being a prime example, with that specific application capturing the largest market share in 2024 and being driven by the rising prevalence of chronic diseases and a growing demand for minimally invasive surgical options that offer quicker patient recovery and better outcomes.
AI in Mental Health Disorder Diagnosis & Management: A Prominent Market Opportunity
The rising, global prevalence of mental health disorders, such as depression, anxiety, and stress-related conditions – attributed to fast-paced lifestyles, social disconnection, and economic uncertainties – is leading to an increased demand for advanced solutions in this sector.
Mental Health America Inc’s ‘Prevalence of Mental Illness 2024’ report estimated that 60 million Americans experience some degree or form of mental illness, with 5.8 percent of the population experiencing it to a severe degree.
The social stigma, discrimination and other personal impacts associated with mental health issues, however, and the resultant reticence of people to seek help, places a significant gap between patients and providers . . . offering both challenges and opportunities for existing providers and future developers of AI-based solutions for handling sensitive conditions.
In April 2024, NextGen Healthcare tackled the shortage of mental and behavioural health professionals with the market introduction of NextGen Ambient Assist. This AI-powered solution transcribes patient/provider conversations in real-time, with the ability to summarise encounters within 60 seconds. It’s promoted as saving providers up to two hours of documentation time daily.
Everybody’s After A Slice of the R&D Funding Pie
Implementing artificial intelligence-based solutions in healthcare often requires significant financial investment. This encompasses the costs of cutting-edge technology, hiring qualified personnel, and maintaining infrastructure. Consequently, the high expenses associated with implementation pose challenges for organisations seeking to integrate AI into their daily operations.
This highlights the need for continuous improvement through Research and Development.
On which note, increasing investments by governments in R&D, and substantial funds by private enterprises in AI technologies tailored for healthcare, is one of the prominent drivers of the market.
Global AI healthcare R&D investment is certainly accelerating. In 2025, AI-enabled healthcare start-ups captured 62 percent of all digital health venture funding in the United States, raising an average of USD 34.4 million per round – an 83 percent premium over non-AI start-ups.
Six out of 11 new AI unicorns created in Q1 2025 were healthcare companies.
Governments are also active investors: the EU's Digital Europe and Horizon programs are funding clinical AI pilots, testing facilities and cross-border data infrastructure. In the U.S., the National Institutes of Health and DARPA have ongoing AI in healthcare research programs, while the UK's MHRA launched a National Commission on the Regulation of AI in Healthcare in September 2025 specifically to shape the next generation of investment and governance.
Notwithstanding all this R&D investment, though, the gap between what is being built and what has been rigorously proven to work – safely, at scale, and in real clinical settings – remains significant and, in some cases, dangerously wide.
The Downside Is Real . . . and Under-Played
What’s found in industry and trade press out there right now is a predominantly very positive framing of AI’s application and benefits. Analyses, however, appear less thorough in its analysis of the downsides, risks, and other aspects of Artificial Intelligence that have – and remain – less than adequately mitigated.
These risks aren’t minimal. Neither are their impacts. And neither are the number of stakeholder groups those impacts hit. Data breaches are the highest profile and most en masse risk categories.
Data breaches come in many shapes and sizes, but basically involve unauthorised access, disclosure, or theft of sensitive personal and medical information. Hardly minimal in impact for those whose data has been stolen.
These breaches can occur through various channels, including cyber-attacks, insider threats, and accidental data exposure. From a sector and governmental perspective, it leads – as a minimum - to declining patient trust in general, and an emboldening of cybercrime perpetrators.
From a market perspective, where healthcare data compromises occur, it fosters mistrust between patients and providers, impeding market growth.
Data Breach Examples
The healthcare sector in a variety of countries has seen major data privacy breaches in the last year alone.
New Zealand provides two such examples:
In late December 2025, ransom hackers accessed the "My Health Documents" module of Manage My Health – the country's largest patient-facing health portal, with approximately 1.8 million registered users. Not a small number for a country with a population of only 5.3 million people.
Medical records, GP referral letters, hospital discharge summaries and clinical correspondence belonging to between 120,000 and 127,000 patients were accessed and downloaded, with the hackers threatening to release the data on the dark web unless a ransom was paid. The breach primarily affected 45 general practices in Northland and 355 referral-originating practices across multiple regions.
Health Minister Simeon Brown described it as "incredibly concerning" and commissioned an urgent Ministry of Health review. The Office of the Privacy Commissioner noted that it was one of the most serious privacy incidents in New Zealand's history.
A second healthcare provider, CanopyHealth, separately revealed in January 2026 that it had been targeted in a cyberattack in July 2025 – a delay in disclosure that drew sharp criticism from clients and privacy advocates.
On a much larger and global note, in October 2023, 23andMe experienced the data breach of genetic information, resulting in the exposure of the personal information of some 14,000 individuals.
Other notable data and privacy breaches have included the February 2024 ransomware attack on Change Healthcare – a subsidiary of UnitedHealth Group and the United States' largest healthcare payment clearinghouse – which exposed the protected health information of an estimated 192.7 million individuals, making it the largest healthcare data breach in history.
The attack cost Change Healthcare an estimated USD 2.87 billion and forced the company to provide USD 9 billion in no-interest advances to keep healthcare providers solvent after claims systems froze.
In May 2024, Ascension Health – a Catholic health system operating 142 hospitals – fell victim to a Black Basta ransomware attack that caused an electronic health record outage lasting almost four weeks.
According to the HIPAA Journal – the leading provider of HIPAA training, news, regulatory updates, and independent compliance advice – 2025 was “another bad year for healthcare data breaches . . . with almost 57 million individuals known to have been affected . . . and at least 642 data breaches affecting 500 or more individuals.
Lack of AI-Skilled Professionals in Healthcare Sector
The risks of the rapid introduction and integration of Artificial Intelligence into healthcare settings are exacerbated - greatly - by the sector's lack of AI-skilled professionals. That, in turn, is exacerbated further by the reluctance among large swathes of medical practitioners to adopt AI.
Occurrences like large-scale and high-profile medical data breaches don’t help.
Nor does the lack of skill within the existing personnel base of the sector in terms of the operation of AI-based systems. Many educational institutions are struggling to keep pace, resulting in a shortage of graduate programs specialising in AI for healthcare applications.
Additionally, many healthcare professionals are hesitant to adopt AI technologies due to fear of job displacement and/or distrust in the accuracy and reliability of AI-driven tools. It’s a completely understandable reluctance to trust Artificial Intelligence to make clinical decisions that are traditionally the domain of human practitioners.
Notwithstanding, the AI wave in the overarching sense, is unlikely to be stemmed to any great degree. The potential in areas like robotics for surgical precision, for facilitating minimally nvasive procedures versus conventional surgery techniques, and for general operational efficiency, is too strong a driver to hold back progress indefinitely.
Specifically in the surgical context, key medtech companies are collaborating with AI solution providers to incorporate AI into their instruments to aid precision.
In March 2024, Johnson & Johnson Services Inc. partnered with NVIDIA Corporation to accelerate and scale Artificial Intelligence for surgery and to form a "digital surgery ecosystem".
The partnership – formalised through a Memorandum of Understanding announced at the NVIDIA GTC conference in March 2024 – combines J&J's medtech presence in over 80 percent of the world's operating rooms with NVIDIA's IGX edge computing platform and Holoscan edge AI platform.
The stated goals are to enable real-time analysis of surgical data, to broaden the global availability of AI algorithms for surgical decision-making, and to support AI-powered applications across the connected operating room – from pre-operative planning through to post-operative documentation.
In June 2025, the collaboration was extended through the launch of the Polyphonic AI Fund for Surgery, which added Amazon Web Services as a partner and opened funding to academic institutions, developers, start-ups and researchers working on AI model development, data engineering and AI governance in surgery.
Where to From Here?
For now, though, the Stanford-Harvard report – the inaugural State of Clinical AI (2026), released in January 2026 by ARISE, the AI Research and Science Evaluation network, a Stanford-Harvard research collaboration led by clinicians and computer scientists from both institutions —points to AI investment outpacing the evaluation practices needed to justify it . . . “solution” developers’ keenness to secure and expand their slice of this expanding market would suggest that vendors and would-be vendors need to spend more time and investment in the business case stage.
For now, AI adoption is highest for platforms facilitating documentation, clinical workflow orchestration and data infrastructure – areas with measurable administrative ROI – faster than for clinical applications with evidence of patient outcome improvement. In other words, the “boom” is more consistently occurring in applications that reduce paperwork, as opposed to those that save lives.
According to an article in the company news publication of TATEEDA Global, a San Diego-based custom healthcare software development company that has built HIPAA-compliant AI systems for U.S. healthcare organisations since 2013, in 2025, the North American landscape, by way of example, was crowded with systems focusing on the likes of AI-assisted ambient note-taking in exam rooms, AI triage in radiology, smart routing in call centres, and “remote patient monitoring programs that actually change re-admission curves".
The article went on to say that “at the same time, most organisations are still wrestling with ungainly data stacks, fragmented workflows, and early attempts at governance boards and 'AI formularies'.
(For the record, an AI formulary is a curated, approved list of AI tools that a healthcare organisation has formally evaluated and sanctioned for use – modeled directly on the concept of a drug formulary, which lists medications approved for prescribing within a hospital or health system. Just as a drug formulary exists to ensure that only tested, safe and appropriate medications are used on patients, an AI formulary is an attempt to bring the same discipline to AI adoption – preventing clinicians from independently deploying untested or unvetted tools in clinical settings.)
“That tension between proven value and uneven execution,” Tateeda points out, “is exactly what healthcare AI technology trends will need to resolve in 2026: fewer one-off experiments, more shared building blocks for data, models, safety, and monitoring across entire enterprises.”
It says 2026 “is shaping up as the year where scale and discipline matter as much as raw innovation.
“The most interesting AI innovations in healthcare will likely be those that run safely across dozens of sites, wrap around everyday tools like EHRs and RPM platforms, and consistently move hard metrics such as throughput, access, and clinician well-being.”
In Part Two of our four-Part 'AI in Healthcare' coverage, we will investigate the different types of risk as each pertains to major stakeholder groups . . . and we'll look at what the different governments around the world are doing to regulate the requirement for mitigation of these.
