AI in Healthcare: Use Cases, Strategies, and Best Practices
by Dr. Phil Winder , CEO
The healthcare sector generates a staggering 13% of the world’s data, yet only 3% of it is actively used, creating a tremendous gap between potential and reality. By harnessing this underutilised data, AI enables healthcare organisations to drive actionable insights and automated decision-making. AI reshapes everything from diagnostics to operational efficiency, addressing urgent challenges such as rising costs, staff shortages and complex patient care. It also paves the way for further innovation.
In the sections that follow, we’ll explore the key use cases and benefits of AI in healthcare, along with practical steps for successful implementation. Through these insights, you’ll discover how to fully leverage AI’s potential and transform data into impactful solutions for both patients and providers.
Transform Your Business with AI
Artificial Intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. Within business, AI is applied to the automation of decisions and strategies through the medium of data, like text in natural language or signals generated by natural phenomena.
The healthcare industry is reported to be one of the largest producers of data in the world. With a single mammogram generating approximately 0.5 GB of data, it is unsurprising that 10 zettabytes (ZB) of healthcare data is estimated to be generated in 2025. In 2024, the whole world generated approximately 150 zettabytes (ZB) of data. This means that in 2024, a whopping 13% of the world’s data was generated by the healthcare industry.
The sad news is that approximately 97% of this data is not used. The scale of this opportunity loss is enormous and affects everyone involved, from patients to healthcare providers. But thanks to advances in AI technology, this data can be used to assist analysis and decision-making across a wide variety of use cases.
Uses of AI In Healthcare
The adoption of AI by healthcare organizations is transforming how patients are treated and receive care. Surveys of the US healthcare industry say that 46% of healthcare organisations are in initial production implementation of GenAI, 75% of leading health care companies are experimenting with (or planning to scale) Generative AI across the enterprise, and 72% of healthcare organisations have at least a proof of concept project underway. These statistics suggest that despite lagging behind industries like finance, AI is becoming more widely appreciated across healthcare. How this adoption is distributed across different healthcare functions is driven by use cases.
The history of AI in healthcare dates back several decades, beginning with the introduction of rule-based systems in the 1970s and 1980s. One notable example is MYCIN, an early expert system designed to assist in diagnosing bacterial infections and recommending treatments. These early systems highlighted the potential of AI to enhance clinical decision-making and set the stage for future developments. Around the same time, clinical decision support systems (CDSS) emerged in the fields of radiology and pathology, helping clinicians analyse complex datasets to make more informed decisions.
These early efforts encountered significant challenges, however, including limited computational power and insufficient access to medical data. This resulted in a limited scalability beyond narrow use cases. Despite these hurdles, the late 20th century saw a shift as data became more readily available and advances in AI made it possible to learn models directly from data, paving the way for broader adoption and more sophisticated applications.
Today, AI-powered technologies are transforming healthcare by harnessing advancements in machine learning, and through the availability of vast datasets. In diagnostics, AI is enhancing the accuracy of imaging tools, assisting in the detection of fractures, tumours and other anomalies with remarkable precision. Advanced imaging technology combined with AI is successfully being used to expand the treatment window for ischemic stroke.
In patient care, predictive and prescriptive analytics of health data are enabling more personalised treatment plans and improving the management of chronic diseases. Operational efficiencies are also improving, as AI automates routine tasks such as scheduling and resource allocation, allowing healthcare professionals to dedicate more time to patient care. Emerging innovations, including robotic-assisted surgeries, virtual health assistants and AI-driven drug discovery, are also revolutionising the industry, paving the way for more efficient healthcare solutions.
The following examples highlight a few more of the many impactful applications of AI in the healthcare industry:
Remote Patient Monitoring
AI-powered systems enable continuous monitoring of patients' health metrics. This allows early detection of anomalies and timely interventions, thereby improving patient outcomes and reducing hospital readmissions.
Source →Personalised Treatment Plans
Machine learning algorithms analyse patient data to tailor treatment plans to individual needs, enhancing the effectiveness of therapies and patient satisfaction.
Source →Medical Imaging Analysis
AI techniques assist with interpreting medical images such as X-rays, MRIs and CT scans, improving diagnostic accuracy and aiding the early detection of diseases.
Source →Predictive Analytics for Disease Outbreaks
AI models predict potential disease outbreaks by analysing large datasets. This enables healthcare providers to prepare for, and respond proactively to, public health threats.
Source →Virtual Health Assistants
AI-driven virtual assistants provide patients with health information, appointment scheduling and medication reminders, enhancing patient engagement and adherence to treatment plans.
Source →Clinical Decision Support
AI systems assist healthcare providers by analysing patient data to recommend personalised treatment plans, improving patient outcomes.
Source →AI-Enhanced Surgical Procedures
AI is integrated with robotic systems to assist surgeons during complex procedures. This improves precision, reducing recovery times and enhancing surgical outcomes.
Source →Electronic Health Record (EHR) Management
AI streamlines the management of electronic health records, improving data accuracy, reducing administrative burdens and enhancing patient care coordination.
Source →Mental Health Support through AI
Applications developed by AI provide mental health support, including mood tracking, cognitive behavioural therapy exercises and crisis intervention.
Source →Remote Rehabilitation Programs
AI-driven remote rehabilitation programs provide personalised exercises and monitor patient progress, facilitating recovery for patients who are unable to attend in-person sessions.
Source →Benefits of AI In Healthcare
The vast array of potential applications raises the question: what tangible benefits can AI bring to healthcare? Diagnostic applications provide an ideal starting point, as their performance can be directly compared to human expertise. For instance, in detecting prostate cancer, AI demonstrated an 84% accuracy rate, significantly outperforming pathologists who achieved just 67% accuracy. This breakthrough not only enhanced diagnostic precision but also paved the way for innovative treatments, such as ultrasound therapy for prostate cancer. Similarly, AI has revolutionised the detection of diabetic retinopathy, a leading cause of blindness among adults. By analysing retinal images with high precision, AI algorithms have consistently outperformed human ophthalmologists, enabling earlier and more effective interventions.
AI also offers vast opportunities to enhance operational efficiency. For example, an Australian hospital implemented predictive analytics to reduce patient stays by 6.5%, resulting in $10 million in annual savings. In another case, a UK pharmaceutical company is halving drug discovery costs by leveraging AI tools to accelerate research and development processes.
AI-powered solutions can analyse vast amounts of medical data rapidly and with unparalleled consistency, allowing healthcare providers to improve patient outcomes and unlock opportunities for innovation.
The Greatest Challenge for AI in Healthcare: Harnessing Human Expertise
Integrating AI into healthcare presents a significant challenge. While the technology is advancing rapidly, extracting its full value remains technically and operationally complex. Successful integration requires not only advanced technical skills but also a deep understanding of statistical operational principles. Since its inception in 2013, Winder.AI has evolved from a focus on data science to innovative applications, including Generative AI.
AI offers unparalleled opportunities, but without expert guidance and skilled AI practitioners, organisations risk falling short of their goals. The intricate web of risks, regulatory requirements and ethical considerations, which are especially pronounced in healthcare scenarios, can further complicate adoption. At Winder.AI, we bridge this gap by ensuring that AI is seamlessly implemented with precision, efficiency and a deep commitment to ethical responsibility.
Case Studies of AI in Healthcare by Winder.AI
Winder.AI has delivered a wide variety of AI projects for more than a decade. Here are a few case studies that we’re proud of; you can see more in our portfolio.
Guide to Success: Unlocking the Potential of AI in Healthcare
Harnessing AI in healthcare presents a transformative opportunity to streamline operations, strengthen compliance and achieve measurable ROI. To ensure success, business leaders must strategically address the key drivers of impact while proactively mitigating potential risks.
Key Success Factors
Successful AI projects require three essential components:
- Clean, Reliable Data
According to the 2024 Healthcare Information and Management Systems Society (HIMSS) survey, healthcare professionals’ primary concerns revolve around data privacy and security. The highly sensitive nature of patient information makes the need for security more critical in healthcare than in other industries. Additionally, ensuring data quality and representativeness is essential for the effectiveness of any AI application. Unified, secure and accurate data therefore forms the cornerstone of successful AI implementation in the healthcare sector.
- Clear, Measurable Objectives
The top four use cases in healthcare today, according to the 2024 HIMSS survey, are all transcription-based. These use cases are straightforward because they have clear, measurable objectives. However, this approach may overlook AI’s strength in decision-making. The next four use cases include analysing X-rays, CT scans and health records, and scheduling follow-up tasks. These use cases are more complex due to less obvious objectives. For example, should you optimise to solve the current problem, or for long-term patient health? A good goal is clear, measurable and valuable. You should therefore define precise goals tied to specific outcomes, such as ‘reduce patient waiting times by 20%’.
- Expert Guidance and Cross-Functional Teams
After good data management and well defined problems, the final key challenge is access to talent. We recommend that you engage professionals who bridge AI expertise with a deep understanding of the healthcare domain, ensuring alignment with both technical and business needs.
Investment Planning
To achieve success, allocate resources strategically. For a mid-sized AI project in healthcare, anticipate the following costs:
- Team: A team of two to three dedicated engineers (£100-200k annually each), corresponding to roles that match the work. You’ll also need a domain expert and someone to act as the product owner. Depending on how many projects you’re running, you might also consider dedicated project management staff.
- Technology: Initial data infrastructure investment of £50–100k annually, plus £25–50k for tools and training annually.
- Timeline: ROI realisation typically occurs within 6 to12 months, depending on project scope and readiness.
You can start small with a pilot project to validate the ROI, then expand based on demonstrated results. This helps to minimise wasted spend.
Project Lifecycle
A successful healthcare AI project typically follows these key phases:
POC & Discovery (4 to 6 weeks):
- Audit data sources and assess quality. This is the first step in any AI project and helps to establish the scope of the project.
- Define compliance boundaries and project objectives. A clear understanding of the problem is crucial to the success of the project.
- A small, iterative cycle of development and testing, with a focus on de-risking future development.
Core Development (3 to 4 months):
- Build and test AI models using historical data. This is the core development phase, where models are built and tested. It is typically the most time-consuming phase but can be controlled through iterative development.
- Collaborate closely with business stakeholders to refine outcomes. Avoid the temptation to over-engineer the solution at this stage. Instead focus on delivering value quickly.
Deployment & Refinement (1 to 2 months):
- Roll out models gradually and focus on low-risk use cases initially to gain confidence. Buy in from stakeholders and leverage this to roll out to other areas of the business.
- Incorporate feedback to optimise performance. Concentrate on problem areas that are most impacting the value. It is more often the user experience, not the model performance, that creates a bottleneck and requires a solution.
Maintenance (ongoing):
- Monitor performance and update models as regulations, data or business needs evolve.
- Invest in model logging and monitoring to ensure the solution is performing as expected.
- Automate insights by integrating outputs with analytics and reporting tools.
Final Thoughts
AI in healthcare holds immense potential to transform operations, reduce costs and drive innovation. However, success hinges on clear objectives, aligned teams and thoughtful execution. By addressing common pitfalls and focusing on business outcomes, you can turn AI from a buzzword into a strategic advantage.
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