Five AI and machine trends for healthcare providers
Looking ahead to 2025, Cambridge Advance Online has listed the key artificial intelligence trends that it says will change healthcare provision
Cambridge Advance Online – the online short course provider of the UK’s University of Cambridge – has outlined a list of five artificial intelligence (AI) and machine learning trends that will influence healthcare in the coming year.
The course provider noted that the technologies are becoming increasingly important across many sectors, citing a recent IBM global AI Adoption Index, which found that 42% of enterprise-level companies claim to be actively deploying AI in their business.
The five trends that will drive technological development in the healthcare sector are:
Explainable AI (XAI)
XAI is a field that aims to make AI decisions as transparent and understandable as possible. Cambridge Advance Online noted that this is “crucial” for building trust, particularly in diagnostic contexts, as healthcare providers need to be able to “grasp the underlying factors” behind patient assessments.
Additionally, XAI is beneficial in supporting regulatory compliance.
Autonomous decision-making
By automating manual processes, machine learning technologies can accelerate decision-making while also improving accuracy.
In a healthcare context, such technologies can analyse genetic data and patient histories – leveraging electronic health records – to recommend personalised treatment plans to patients. They can also predict patient outcomes or complications, supporting more proactive interventions.
Agentic AI
As AI tools develop, they are increasingly capable of proactively setting their own goals and taking autonomous actions – this technology is known as agentic AI.
In a healthcare setting, this could allow AI to “continuously monitor health metrics and adjust treatments, such as real-time insulin administration for diabetics, or personalised chemotherapy plans for oncology,” Cambridge Advance Online explained.
Beyond this, agentic AI also supports the processing of medical data, potentially improving outcomes and minimising side effects.
Edge AI
This development refers to how AI interacts with so-called “edge devices”, i.e. machines that allow it to interact with the physical world, such as sensors or smart devices.
In a healthcare context, this would enhance the effectiveness of healthcare monitoring, allowing data to be processed locally on the device, reducing latency, enabling real-time decision-making, and reducing the amount of data that needs to be stored centrally.
Processing data locally would also enhance privacy and security, reducing the risk of data breaches.
Augmented workforces
While some AI technologies may replace humans in the workplace, they can also be used to enhance and augment human contributions.
In a medical context, AI can assist doctors by analysing medical images and patient data for easily missed details, improving the accuracy of diagnosis.
This collaboration between humans and AI can also involve automating repetitive, data-intensive tasks, while the humans focus on strategic, interpersonal, or creative activities.
The London Medical Laboratory reviewed the potential opportunities and risks of AI in healthcare last year.