Transforming patient care: AI in healthcare

The healthcare system is awash with data, from patient treatment plans, diagnostic imaging and hospital attendance rates, to staffing levels and logistics.

Machine learning and artificial intelligence (AI) tools are already being put to good use to mine the data and use it to transform patient care. And as analytical tools develop, opportunities for improvements in patient care continue to grow.  

But with the opportunities come concerns and risks about healthcare jobs, data security, equity in access to healthcare, and patient experience. Read on to find out more.

How is artificial intelligence being used for medical diagnosis and treatment planning?

AI technology is revolutionising medical diagnosis and treatment planning by:

Enhancing diagnostics

Many conditions like cancer, arthritis and problems with the digestive system, are diagnosed when radiologists and other healthcare professionals scan affected areas using x-rays, CT scans and MRI scans. Radiology images that are generated take time to view and analyse to pinpoint problems, such as tumors. AI-powered algorithms are helping to:

• Speed up medical imaging interpretation, allowing earlier disease diagnosis, including identifying breast cancer cells in mammograms.
• Reduce the rate of false positives and negatives in diagnosis by removing the risk of human error.
• Allow earlier diagnosis by picking up small changes not obvious to the human eye
• Support better clinical decision-making by identifying particularly vulnerable patients and ‘nudging’ clinicians to take extra steps to support them, for example in end-of-life cancer care.

Personalising treatment plans

We are all unique and how we respond to treatments can vary, as scientists develop new and more effective medicines. 

AI applications can also help predict how different people will respond to different treatments, helping clinicians with decision-making, leading to more effective personalised treatment plans.

AI tools can be used to mine vast chemical and genomic data libraries to identify potential new drug targets.

However, care needs to be taken over how the new developments are designed and regulated to ensure all patients benefit equally from new innovations. Data scientists need to take measures to avoid biases and protect patient data.

How are mobile technologies supporting better healthcare delivery?

Increasingly, within the healthcare sector, mobile technologies using AI-based algorithms are enhancing patient care, streamlining processes, and improving overall outcomes.

• AI-based secure mobile apps can link to electronic health records and be used to inform and remind patients about treatment plans and automate administrative tasks.
• Efficient data collection, storage and sharing among medical professionals and healthcare providers supports better decision-making and research and development.
• Mobile solutions can mean healthcare providers can offer remote consultations, monitor patients in their own home in real-time and decide on the best treatment options, improving access to care and better patient outcomes.
• User friendly apps make ordering repeat prescriptions easier and convenient, improving patient engagement and adherence to their medication schedule.
• Mobile apps can collect patient data that enable data-driven diagnoses and personalised treatment plans that can improve clinical workflows and patient outcomes.
• Healthcare professionals can communicate more quickly to improve workflows and accelerate diagnoses and treatments.
• Wearable medical devices and remote patient monitoring, acting as virtual health assistants, support patients with chronic diseases in their own homes and are underpinned by AI algorithms and machine learning. 

How will automation and applications of AI affect the number of jobs in healthcare?

Mobile technologies and AI tools are helping medical professionals improve discharge rates from hospitals, improving diagnostics and making processes more efficient.  But what impact will this have on jobs in healthcare and how might automation affect the patient’s experience and longer-term outcomes?

A report from the EU in 2020 predicts a gap in the global healthcare workforce of almost 10 million doctors, nurses and midwives by 2030. At the same time the world’s population is ageing. 

“By 2050, one in four people in Europe and North America will be over the age of 65—this means the healthcare organisations will have to deal with more patients with complex needs.”

AI-driven technologies can help address the increasing need for healthcare by:

• Improving efficiencies and cost-effectiveness, enhancing diagnostics, forecasting disease spread, and more.
• Automating processes that free up healthcare professionals to spend more time with patients, raising staff morale and potentially improving staff retention.
• Accelerating drug discovery and drug development and progress of new and effective treatments to clinical trials and into the clinic.
• Enabling chatbots to listen and respond to patient queries through natural language processing (NLP) algorithms.
• Using robotics to automate simple surgical procedures making them more cost-effective.
• Focusing resourcing to where it’s needed most. AI tools can use clinical data metrics to help optimise clinical workflows and reduce wait times, enhancing overall healthcare management

What are the ethical implications of using artificial intelligence in healthcare services?

While AI is underpinning breakthroughs in healthcare technologies and services, it’s essential to balance its benefits with workforce needs and ethical considerations. The future will likely see human expertise and AI-driven solutions complementing one another. There are several important ethical considerations when it comes to the use of AI in healthcare. In particular:

Informed consent

If AI algorithms trained on big data sets are involved in decision-support, how can individual patients give consent? They need to know how AI might impact their care and be able to make informed decisions.

Safety and transparency

Patient safety is paramount. How can healthcare providers, regulatory bodies and other stakeholders ensure AI systems are being used safely? Transparency in AI decision-making is important, but how can it be guaranteed?

Ensuring inclusive healthcare without bias

AI algorithms are trained on large health data sets. If there are inherent biases in the data, these can be exaggerated in data analysis and how the algorithms perform. This is particularly important when it comes to real-world patient outcomes – if an AI-enabled process is underpinned by training data that is biased towards or against particular patient groups, such as those from low socio-economic groups or particular ethnic groups, the outcomes could be unfair.

Data privacy

The need for large patient data sets to develop effective AI algorithms and tools makes protecting patient medical records an important consideration.

Data driven healthcare

Personalised care, automated data management, pandemic preparedness and AI-assisted surgery are among the healthcare benefits arising from insights generated from the vast amount of data being generated in the healthcare industry. But striking the right balance between innovation and patient well-being is essential.

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