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The Future of AI in Healthcare Diagnostics

Artificial intelligence (AI) is no longer the stuff of science fiction. It plays an integral role in everybody’s daily lives, whether they know it or not, and we’re only really scratching the surface of what it’s truly capable of

In the world of healthcare, we’re already starting to appreciate the substantial and tangible impact that AI can have. We’ve seen it help healthcare professionals to support their patients through the detection of conditions and diseases, reducing the potential for misdiagnosed cancer. It’s also becoming an integrated element in helping big health data to support clinical decision-making.

But what does the future of AI look like in healthcare diagnostics? We’ll be taking a closer look at that question in the following post. Be sure to read below to find out more…

How Does AI Currently Affect Healthcare Diagnostics?


AI solutions are currently in place in order to assist with automating images for analysis and diagnosis. This can help to highlight areas of interest in scans to a radiologist, as well as help to drive efficiency and reduce the potential for human error.

This can help to highlight areas of interest in scans to a radiologist, as well as help to drive efficiency and reduce the potential for human error through various tools and software like medical information sharing.

Developments have taken place which indicate that fully automated solutions could take place which read and interpret scans without any human oversight is necessary.

These steps forward have, for example, helped to improve tumour detections on MRIs and CTs, which demonstrates the opportunities available for cancer prevention.

Patient Risk Identification

AI is also being used to analyse vast amounts of historic patient data to provide real-time support for clinicians in identifying at-risk patients.  A current issue that is being focused on in this area centres around readmission risks, highlighting patients who have an increased chance of returning to the hospital within 30 days of being discharged.

Multiple companies and healthcare providers are looking at developing solutions that are based on data in patients’ electronic health records. Another revelation that has been driven by AI is the ability of practitioners to predict the risk of cardiovascular disease based purely on a still image of a patient’s retina.

What Does the Future of AI and Health Care Diagnostics Look Like?

Now that we’ve taken a look at how AI is currently being used in healthcare diagnostics, what could the future look like? Let’s explore this further…

Machine Learning (ML)

Machine learning (ML) is a specific branch of AI. In basic terms, ML is the practice of using algorithms to parse data, learn from that data, and then provide a solution or prediction. The ‘learning’ aspect enables systems to act without actually being programmed to do so – a step forward from the current patient risk identification programmes being carried out.

ML enables a faster and more accurate analysis of massive quantities of data. It’s a fast-growing trend in the healthcare industry, but there are still further developments on the horizon that could shape healthcare diagnostics.

One suggestion for the future of ML is to help find a way of compiling real-time personal health data (such as data taken from smartwatches) into a single central hub. This could then be used to effectively prevent illness and provide better treatment for individuals.

As well as diagnosis, ML has the potential to be used for alternative healthcare goals, such as drug discovery and clinical trials.

Natural Language Processing (NLP)

Natural Language Processing (NLP) is another strand of AI that converts human language into a structured format. This can then be used by computers to perform various computational analyses and various other tasks.

In the healthcare industry, for example, there is still a significant amount of clinical information that is documented via a number of different unstructured methods, such as dictation, typing, and writing. Until this information has been codified and structured, it cannot be used by a computer.

Trials are ongoing which are looking at how NLP can be applied in a clinical setting to convert information from transcribed history into data that represents a patient’s problem list and past medical history. This can then be stored in a codified fashion in an electronic medical record.

Computer-Aided Detection (CAD) Systems

AI is already being used in Computer-Aided Detection (CAD) systems, but there are still future advances to be made. It’s hoped that the accuracy of CAD systems for imaging studies will exceed that of radiologists, with CAD systems also being the primary interpreters of said studies.

CAD could also be used in the future to help diagnose skin legions, using dermatology systems to learn about the various appearances of legions and prospectively identify ones that pose a high risk.

What Challenges Face AI in Healthcare Diagnostics?

Privacy and Data

As with any discussion related to AI, there’s an issue related to privacy. For any AI solution to be deemed successful, it inevitably requires a vast amount of patient data for its algorithms to be optimised.

This raises the issue of patient privacy and the ethics of data ownership, which is highly controversial. This is especially true if the data ever falls into the hands of an unauthorised third party.

Quality of Data

How do we know that the data we collect for AI systems is always going to be fully reliable? In other industries that work with AI, the data gathered is generally objective and accurately measured – that’s not always possible in the healthcare industry.

In healthcare, data can be subjective and often inaccurate, with issues such as incorrect clinician notes and inaccurate reports from patients. This will, therefore, undermine the work that AI can carry out.

How Do You Think the Future of AI and Health Care Diagnostics Will Look?

In this post, we’ve taken an in-depth look at how AI is currently shaping and will continue to shape the healthcare industry. We’ve taken a particular look at what it means for diagnostics, and it’s certainly exciting!

If you’re a health care provider, how do you view AI? What changes do you think are the most feasible moving forwards? Feel free to leave your thoughts in the comments down below!

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