As artificial intelligence (AI) and machine learning (ML) dominate headlines and reshape everyday practices within healthcare, they are not just buzzwords – they’re revolutionising the way we work.
With 2024 drawing to a close, Cambridge Advance Online, the University of Cambridge’s online short course provider, taps into the expertise of AI and data science academic lead, Dr Russell Hunter, to uncover the top ML trends healthcare leaders need to know as they navigate this rapidly evolving landscape.
This follows the government’s recent commitment to a digital-first NHS, sparking widespread interest in how ML could transform the healthcare sector:
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Prime Minister, Sir Keir Starmer, recently pledged major reforms to the digitalisation of the NHS, following critical concerns raised in the Darzi report.
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Questions about how the healthcare industry applies ML are frequently searched, with common queries including “How is machine learning used in healthcare?” and “Does the NHS use machine learning?”.
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IBM’s latest global AI adoption index found that 42% of enterprise-scale companies claim to be actively deploying AI in their business – the same amount that were still exploring its use the year prior.
Dr Russell Hunter works within the Department of Engineering at the University of Cambridge and leads Cambridge Advance Online’s Leveraging Big Data for Business Intelligence course.
Explainable AI (XAI)
XAI aims to make AI decisions understandable to humans, enhancing trust and regulatory compliance.
“When you build a model to solve a particular problem, it is often more difficult to persuade stakeholders to come on board”, Dr Hunter shares. “In fact, in many cases they would prefer a less optimal model that can be visualised and understood more easily than jumping on board with some kind of mysterious model that works for unknown reasons. This is especially important when it comes to healthcare or finance.”
In healthcare, XAI provides explanations for diagnostic decisions or treatment recommendations made by AI systems. These explanations are crucial for doctors and patients to trust and act on AI-driven insights, ultimately improving patient outcomes. AI models used for predicting patient risks, such as the likelihood of developing a certain disease, need to be clear and understandable to ensure that healthcare providers can grasp the underlying factors behind the risk assessment.
Autonomous decision-making
These advanced systems are transforming healthcare by accelerating the speed and precision of decision-making, driving greater efficiency and enhancing customer experiences. By automating manual processes, ML technologies can increase businesses’ abilities to analyse vast amounts of data quickly while uncovering patterns and making informed decisions.
Dr Hunter explains how autonomous systems can be applied to the healthcare industry, “Sophisticated multimodal AI can analyse genetic data and patient histories to recommend personalised treatment plans. This leads to more effective and individualised health care. Similarly, by leveraging data from electronic health records, these systems can predict patient outcomes or complications, which allows for proactive intervention.”
Agenetic AI
“Agentic AI represents a significant advancement beyond classical reactive AI by being designed to proactively set its own goals and take autonomous actions to achieve them”, explains Dr Hunter. These proactive systems not only enhance patient care but also have the potential to alleviate the burden on healthcare professionals by automating routine monitoring and treatment adjustments.
“In the realm of personalised healthcare, agentic AI can revolutionise patient care by continuously monitoring patient health metrics and autonomously administering medication as needed. For example, an agentic AI system could monitor a diabetic patient’s blood sugar levels in real-time and administer insulin precisely when required, thus maintaining optimal glucose levels and reducing the risk of complications.
“Another application is in personalised treatment plans for chronic diseases,” Dr Hunter adds. ‘Agentic AI can analyse vast amounts of patient data to predict disease progression and suggest tailored treatment plans. For instance, in oncology, agentic AI can process data from medical records, genetic profiles and treatment responses to recommend personalised chemotherapy protocols, potentially improving outcomes and minimising side effects.”
Edge AI
Another cutting-edge development, Edge AI brings an immediate processing capability which is crucial for applications in healthcare monitoring, where time-sensitive tasks require prompt responses. According to Dr Hunter, this is achieved by processing data locally on the device, reducing latency, enabling real-time decision-making and minimising the amount of data that needs to be transmitted to central servers.
By processing sensitive information locally, this also enhances privacy and security, reducing the risk of data breaches during transmission, something specifically important with healthcare data, however, Dr Hunter does point out that “challenges such as hardware limitations, integration complexity, and the need for efficient management and maintenance of numerous edge devices curtail the full effectiveness of edge AI.”
Augmented workforces
While there are concerns that AI will replace humans in the workplace, Dr Hunter believes that the latest AI developments can augment rather than undermine human contributions. AI can assist doctors by analysing medical images and patient data, identifying patterns that might be missed by the human eye. This allows doctors to make more accurate diagnoses and develop personalised treatment plans, thereby improving patient outcomes and operational efficiency.
“This collaboration between humans and AI combines the strengths of both, allowing AI to handle repetitive, data-intensive tasks while humans focus on strategic, creative and interpersonal activities that require emotional intelligence and critical thinking”, Dr Hunter notes. “Rather than eliminating jobs, AI reshapes them, leading to the creation of new roles that require managing, programming and collaborating with AI systems.”
It is crucial to keep an eye on these developments as a healthcare leader, to ensure your organisation is fully equipped to gain an edge by leveraging AI and ML.
For an indepth look at these insights and additional trends across industries, read Dr Hunter’s machine learning trend analysis on the CAO blog.
