Staying abreast of developments in artificial intelligence and machine learning is becoming increasingly important for the delivery of timely, efficient and cutting-edge healthcare, but that can be a challenge. Data science academic Dr Russell Hunter PhD looks at the top trends that healthcare professionals and their organisations need to know about as they navigate this rapidly evolving landscape.
There has long been a widespread interest in how artificial intelligence (AI) and machine learning (ML) could transform the healthcare sector. For example, common searches on Google include questions such as ‘How is machine learning used in healthcare?’ and ‘Does the NHS use machine learning?’.
The interest was taken up a notch recently when the Government committed to a digital-first NHS following critical concerns raised in the Darzi report. Yet, although AI and ML are reshaping everyday practices within healthcare, questions – and perhaps scepticism – remain. And it can be hard for healthcare leaders to address concerns when they are not experts and AI is evolving so fast.
So, what do leaders need to know in terms of emerging trends in AI and ML, and how can those who are suspicious of AI be convinced that it can be a help rather than something to be worried about?
Explainable AI
Explainable AI, also known as XAI, aims to make AI decisions understandable to humans, enhancing trust and regulatory compliance.
When a model is built to solve a particular problem, persuading stakeholders to come on board can often be difficult. In fact, many would prefer a model that is more easily understood, even if it is less optimal. Something that can be visualised is preferable to jumping on board with a 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
Autonomous systems are transforming healthcare by accelerating the speed and precision of decision-making, driving greater efficiency and enhancing customer experiences. In the business world, ML technologies can increase companies’ ability to quickly analyse vast amounts of data while uncovering patterns and making informed decisions.
Just as automating manual processes can help make sense of business data, advanced systems can be applied to healthcare. Sophisticated multimodal AI can analyse genetic data and patient histories to recommend personalised treatment plans. This leads to more effective and individualised healthcare.
Similarly, by leveraging data from electronic health records, these systems can predict patient outcomes or complications, which allows for proactive intervention.
Agenetic AI
Agenetic AI is a new class of AI designed to act with autonomy. It proactively sets its own goals and takes autonomous steps to achieve them, making decisions and taking action without direct human intervention. This makes it a significant advancement beyond classical reactive AI.
These proactive systems can enhance patient care and 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 the blood sugar levels of a patient with diabetes in real-time and administer insulin precisely when required, thus maintaining optimal glucose levels and reducing the risk of complications.
Agentic AI can also help with personalised treatment plans for chronic diseases by analysing 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 is Edge AI, which brings an immediate processing capability crucial for applications in healthcare monitoring where time-sensitive tasks require prompt responses. 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.
Processing sensitive information locally also enhances privacy and security, reducing the risk of data breaches during transmission, which is particularly important with healthcare data.
However, there are challenges. There are hardware limitations and integration complexity, and there is a need for efficient management and maintenance of numerous edge devices. These could curtail the full effectiveness of edge AI.
Augmented workforces
While there are concerns that AI will replace humans in the workplace, the latest AI developments can augment rather than undermine human contributions. For example, AI can assist doctors by analysing medical images and patient data to identify patterns that the human eye might miss. This allows doctors to make more accurate diagnoses and develop personalised treatment plans, thereby improving patient outcomes and operational efficiency.
The collaboration between humans and AI combines the strengths of both, allowing AI to handle repetitive, data-intensive tasks while people focus on strategic, creative and interpersonal activities that require emotional intelligence and critical thinking. This applies to healthcare as much as any other sector.
Rather than eliminating jobs, AI reshapes them. As technology advances, new roles will be created where the job is managing, programming and collaborating with AI systems. It is crucial to keep an eye on developments to ensure healthcare organisations are fully equipped to gain an edge by leveraging AI and ML.
Dr Russell Hunter has a PhD in Computational Neuroscience and works at the University of Cambridge. He leads the course Leveraging Big Data for Business Intelligence at Cambridge Advance Online.
A version of this article was originally published by our sister publication Healthcare Leader.
