Artificial Intelligence: Can AI Work in the Healthcare Setting?

With the increased popularity of AI (Artificial Intelligence) platforms such as Chat GPT, there has been increased discussion around AI among the media, especially regarding how it can be utilised among different job roles. So, what are its capabilities in the healthcare setting?

Artificial intelligence is the simulation of human intelligence by a machine, it is developed to learn, so it can complete the tasks we can do. There are different types:


Machine Learning

This uses algorithms to sort through data, identifying patterns to make predictions. 

Machine learning has already been introduced to the healthcare system through Electronic Health Records. These ensure all the health records are in one place making it more accessible for healthcare staff. It is also capable of sorting through the data and highlighting the key points a lot faster than the human brain: this aids healthcare professionals with diagnosis and treatment at a faster rate.

Furthermore, in the future, the system could potentially view and compare all the data from previous patients, so it has the potential to learn and spot patterns from the outcomes to eventually predict the best treatment for future patients.


Deep Learning

This is when the computer can process and recognise imagery.

Through deep learning, there is the capability to read CT scans, MRI, X-rays and Ultrasound, once again at a faster rate and higher accuracy, meaning results could be processed much quicker and reduce wait times.



Relieving healthcare workers and increasing efficiency!


The biggest challenges within healthcare are staff shortages, with these ai systems speeding up certain tasks, this will relieve a lot of pressure on these workers.


The NHS is currently exploring machine learning-based chatbots that ask the patient questions and use those answers, paired with known data, to determine a diagnosis and the suitable care needed. If this is successful, this could drastically reduce the problem of unnecessary hospital admissions, further relieving healthcare workers and increasing the efficiency of services.


The development of home–based sensors that use artificial intelligence could be beneficial within social care. This monitors the person’s movement around the house, using the data to register any problems that may occur such as a fall and contacting the relevant healthcare services as needed. This reduces the need for a carer to be present all the time, easing staff shortages and giving the resident that sense of independence.


One of the biggest causes of medical errors is human error; we all make mistakes, but in the medical field, this could be fatal. AI greatly reduces this risk as its performance is consistent.


What are the drawbacks?


The more data an AI can compare, the more accurate its predictions can be but getting the data in the first place can be challenging.

To ensure accuracy, it needs data which is not always readily available. Healthcare data can be inconsistent so larger access will allow the system to spot any anomalies or mistakes in the data. Ideally, to get the best out of AI, there needs to be shared data over one large network, focusing on overall patient outcomes. There would be the challenge of data privacy and sharing between healthcare services; the data would need extremely tight security to prevent data breaches and need patient consent to use it.


There is also the issue of accessibility, there is still room for technological error, with possible failures in internet connection. The system would also need to be user-friendly to ensure workers can be successfully trained in understanding how to navigate it.


Healthcare is not black and white; at the end of the day, everyone has individual needs that may go beyond their physical condition. The algorithm will most likely focus on efficiency over what is best for the individual’s situation and what they are comfortable with.

Therefore, it is important to continue evaluating and improving the use of AI in healthcare, while also recognising its limitations and the need for human intervention in certain situations.


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