Artificial intelligence (AI) is increasingly being applied across healthcare, from supporting diagnosis to improving operational efficiency and enabling more proactive models of care. The potential implications are significant, particularly as health systems face growing demand, workforce pressure and rising costs across the globe.
At the same time, the reality is more complex. Healthcare is not an environment where new technology can simply be layered in and expected to work. Decisions carry real consequences, and clinicians are working in fast-moving, high-pressure settings. For AI to be useful, it has to fit into that reality. That means working with the data that is available, integrating with existing systems, and producing outputs that are clear enough to act on in the moment.
AI is already starting to show up in everyday healthcare settings, particularly in areas like diagnostics, remote monitoring and operational planning. The success tends to come down to how well these tools are introduced into real clinical environments, which of course, can vary based on a number of factors that are rarely simple to control.
In a healthcare context, AI refers to systems that analyse data, recognise patterns and support decisions or actions with limited human intervention. This includes tools that interpret clinical records, identify risk and automate parts of workflows, which we’ll touch on below.
In practice, AI is less about replacing clinicians and more about helping them work with more information, more quickly. Its value comes from supporting judgement, not removing it.
However, what’s key is that AI does not operate in isolation. It depends on data being available, connected and usable across systems. In healthcare, that is often a challenge. Information is spread across devices, platforms and care settings, and not always easy to access in real time.
This is where healthcare digital transformation, IoT and connectivity become critical. Connected devices and monitoring tools generate the data AI relies on, while secure, reliable networks, including private networks, allow that data to move between systems and teams. Without that foundation, AI struggles to deliver consistent value in practice.
AI is already being used across different parts of healthcare, from direct clinical support to operational planning. While the use cases vary, they tend to fall into a few clear categories. In all cases, AI is not replacing decision-making, but helping clinicians and organisations work with more information, more quickly.
One of the clearest uses of AI in healthcare is as a support layer for clinical judgement. Rather than replacing clinicians, these tools help surface relevant information more quickly by analysing patient records, symptoms, test results and treatment histories.
That can mean suggesting possible diagnoses, flagging drug interactions or highlighting conflicts between different treatment guidelines. The underlying aim is not to remove judgement from care, but to make complex decisions easier to navigate, especially where clinicians are working across multiple conditions, competing priorities or large volumes of patient information.
This is already happening in practice. The ROAD2H project, for example, has explored how AI can help clinicians manage patients with several chronic conditions by identifying where guidelines may conflict and drawing attention to points that need human review. The point is not that the system decides, it is that it helps clinicians see what could otherwise be missed.
Imaging remains one of the most established areas for AI in healthcare. This is where the technology has moved furthest from theory into practical clinical use, particularly in radiology, ophthalmology and cancer care.
AI tools are being used to analyse scans such as X-rays, CT images and MRIs, often to detect abnormalities, prioritise urgent cases or support interpretation. Moorfields Eye Hospital, for instance, has trialled AI to help identify retinal diseases from optical coherence tomography scans, using the system to flag cases that may need urgent attention. The BMA also notes that diagnostic tools, especially in radiology and cardiology, made up the largest category of AI products in use in the NHS in England in a 2021 survey.
With imaging departments are under pressure, AI can help bring urgent cases to the front of the queue and act as a second set of eyes, but it still depends on clinician oversight and on systems being accurate in real-world settings, not just in controlled trials.
Another active area is predictive analytics. Here, AI is used to analyse historical and live patient data to identify patterns linked to deterioration, readmission or future risk.
Hospitals and care teams are interested in these tools because they can support earlier intervention. A patient at risk of deterioration may not look critical yet, but a model trained on previous cases may detect warning signs sooner than a manual review alone. In theory, that means more proactive care and fewer avoidable escalations.
Similarly, predictive maintenance models can help flag when medical equipment is in need of repair or replacement. When complete with IoT technology, healthcare equipment becomes part of a smart, connected ecosystem. Equipment becomes connected to analytic capabilities, providing huge implications for the potential of AI models.
The real test is whether those predictions are useful at the point of care. A model that flags too many patients, or flags them too late, quickly loses value. So while predictive analytics is one of the more promising uses of AI and IoT in healthcare, it is also a good example of why implementation and clinical workflow matter as much as model performance.
This is one of the areas where AI and connectivity come together most clearly. As more care happens outside hospitals and clinics, remote monitoring tools are being used to track patients over time rather than relying only on periodic appointments.
Wearables, home sensors and connected medical devices can capture data such as movement, heart rate, oxygen saturation or glucose levels. AI can then help interpret that information, spotting patterns or changes that might need attention. That creates more scope for earlier intervention and more personalised long-term care, particularly for chronic conditions.
There are already concrete examples of this approach. Researchers at Phillips found that AI analysis helped reduce the clinical burden of false positives amongst AFib diagnoses. Dr. Suneet Mittal, BA, MD Director of the Electrophysiology Laboratory and Associate Chief of Cardiology at the Valley Hospital in Ridgewood, NJ, says:
”The high false positive rate of AFib detected by some Implantable Loop Recorders (ILRs) has created a clinical burden. Since ILRs transmit data daily, these false positives are one of the Achilles heels of remote cardiac monitoring. This study demonstrates that Cardiologs’ advanced AI can filter up to 2/3 of false positive AF episodes, which should improve clinical efficiency.”
A significant proportion of current AI use in healthcare sits outside direct clinical care. That reflects where many health systems are feeling the most immediate pressure, not just in diagnosis and treatment, but in capacity, scheduling, documentation and administrative workload.
As a result, many early deployments focus on areas such as appointment management, triage support, transcription and workflow optimisation. Across different health systems, studies have consistently shown that a substantial share of clinician time is spent on administrative tasks, creating a clear opportunity for automation and decision support tools to reduce that burden.
This is also where some of the more practical gains are being realised. Healthcare providers in multiple regions are using AI to predict missed appointments, prioritise patient queues and automate routine interactions, helping to improve capacity without increasing resource. These applications are often less visible than clinical use cases, but they address some of the most persistent operational challenges in healthcare systems.
AI is also being used earlier in the healthcare lifecycle, particularly in drug discovery and pharmaceutical research. In this context, its value lies in speed and scale. Models can analyse large biological and chemical datasets far more quickly than traditional approaches, helping researchers identify potential drug targets, predict compound behaviour and narrow down candidates before laboratory testing.
Research in this area suggests that AI can improve the efficiency and accuracy of early-stage drug discovery, particularly in predicting the efficacy and toxicity of potential compounds. However, its impact still depends heavily on the quality of underlying data and the integration of AI methods with established experimental processes, rather than replacing them entirely.
This, again, reflects the broader pattern; that AI can help focus research and reduce time spent on trial-and-error methods, but it remains part of a wider scientific workflow that still relies on validation, regulation and human expertise.
At a public health level, AI is being used to make sense of large datasets that would be difficult to analyse manually. That includes identifying trends, spotting emerging risks and helping health systems anticipate demand.
As research in the Lancet Public Health Journal notes, AI can support public health surveillance, epidemiological research, communication, resource allocation and other forms of decision-making, particularly where large and complex datasets need to be processed quickly. The same paper highlights examples such as the use of AI to track disease spread, analyse behavioural and environmental data, and support planning during vaccination campaigns.
This is valuable, but it of course depends heavily on coverage and representation. If data is incomplete or skewed, the output will be too. In public health, this can become an equity issue, particularly if already underserved groups are less visible in the data used to train or run these systems.
AI is also beginning to shape parts of surgical care, although this remains more specialised than many of the use cases above. In practice, this typically involves systems that support precision, imaging or intraoperative decision-making, rather than anything approaching fully autonomous surgery.
A useful example comes from Italy, where researchers at the Candiolo Cancer Institute in Turin, working with the University of Turin and San Luigi Gonzaga Hospital, developed the BLAIR system for robot-assisted radical prostatectomy. The tool uses AI to predict the risk of intraoperative bleeding and provide warnings during the procedure, illustrating how machine learning is being applied to support surgeons in real time rather than replace them.
In surgery, decisions have immediate consequences, so there is far less room for error. That puts more weight on how these systems are tested, how their outputs are explained, and who is ultimately responsible for acting on them. As a result, the conversation is not just about what the technology can do, but how it fits safely into clinical practice.
AI’s value in healthcare is often framed in terms of potential, but in practice its impact tends to show up in more specific, incremental improvements across clinical and operational workflows. We outline some of the main benefits of AI in healthcare below.
One of the clearest areas of benefit is in pattern recognition and prediction. AI systems are being used to identify early signs of conditions such as sepsis, heart failure and cancer by analysing large volumes of patient data in real time.
A recent narrative review in the medical literature highlights how these models are already improving diagnostic accuracy and supporting earlier intervention, particularly in data-rich environments where subtle signals might otherwise be missed.
Some of the clearest near-term benefits of AI in healthcare are administrative rather than clinical. Automating routine tasks such as documentation, scheduling, coding and records management can improve the efficiency and accuracy of day-to-day operations, while reducing the time clinicians spend on non-clinical work.
In practice, that can mean fewer bottlenecks, better use of capacity and more time directed towards patient care. Over time, these tools may also become more responsive and personalised in how they support communication with patients, although that remains a more developing area.
AI is also beginning to shift parts of care closer to the patient, particularly in the management of long-term conditions. Wearables and connected devices can now feed continuous data into AI systems, allowing changes in health status to be identified earlier and acted on more quickly.
In practice, this supports more consistent monitoring outside clinical settings and can help patients manage conditions more effectively between appointments. That can mean fewer avoidable admissions and a more proactive approach to care rather than reacting once issues escalate.
Alongside its potential, AI introduces a set of risks that are particularly significant in healthcare, where decisions can directly affect patient outcomes.
AI systems can produce false positives, false negatives or recommendations that appear credible but are not clinically appropriate. Without proper validation and oversight, this can lead to poor decision support rather than improved care.
AI models are shaped by the data they are trained on. If that data is incomplete or unrepresentative, the outputs can reinforce existing inequalities. This is a well-documented concern, particularly in areas such as diagnosis and risk prediction.
There is also a more subtle risk around overreliance. If clinicians are expected to defer to automated systems, it can affect decision-making, professional confidence and the clinician–patient relationship. In practice, maintaining a clear role for human judgement remains essential.
These challenges are not necessarily barriers to adoption, but they do shape how and where AI can be used safely.
Many challenges with AI tools in healthcare relate to how well it fits into day-to-day clinical work.
In practice, clinicians will not use systems that slow them down, interrupt workflows or produce outputs that are difficult to interpret. Studies on AI adoption consistently highlight usability, training and integration with existing systems as key barriers, even when the underlying technology performs well.
There is also a skills and training dimension. Healthcare professionals are not expected to become data scientists, but they do need enough understanding to interpret outputs, question results and use systems safely. Without that, adoption tends to stall or remain limited to small pilots.
At an organisational level, this is why AI is often tied to broader healthcare digital transformation efforts. Systems need to work with existing records, devices and processes, not sit alongside them. This is where infrastructure becomes relevant. IoT device management and reliable connectivity, including private networks, support the consistent flow of data these tools depend on. Without that, outputs are delayed, incomplete or unreliable.
Finally, there is the question of governance. Healthcare organisations are accountable for how AI is used in decision-making. That includes data quality, safety, transparency and regulatory compliance. These are not theoretical concerns. They directly affect whether systems can move beyond trials into routine use.
AI in healthcare is already delivering value across a range of use cases, from diagnostics and monitoring to operations and research. What is becoming clearer, however, is that its impact is less about breakthrough moments and more about steady improvements in how care is delivered and managed.
The organisations seeing the most progress are not necessarily those using the most advanced models, but those building the right conditions around them. That means investing in data, connectivity and infrastructure, but also in people, processes and governance.
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