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The Future of Artificial Intelligence in Radiology

September 3, 2019 0 Comments

A shortage of Rural doctors also includes a shortage of radiologists. From Low Mobility and staff turnover internally for the experienced Radiologists, to limitations on the Locations of the jobs and the Working conditions.

 Artificial Intelligence (AI) is now also a factor to consider. It all started for Medical Imaging when potential opportunities were recognized that AI might bring to healthcare, particularly in radiology and pathology. AI works in assisting the imaging diagnosis when a large amounts of past diagnoses is fed into the machine learning algorithms to generate new rules for classifying scans based on previous work. Applying this technique to diagnostic scans is referred to as radiomics.

A number of innovations were recently introduced such as AI being the imaging solution to accelerate critical patient diagnoses, in which it will allow for instant notifications to review critical findings that may accelerate patient diagnosis or its advanced clinical decision support and diagnostics.

The Future of Artificial Intelligence in Radiology

AI has been widely seen as a help to radiologists tasked with making informed clinical decisions and choosing effective treatments. With AI and machine learning, the field of imaging analytics could also improve the care delivery and patient outcomes. A number of healthcare institutions in Australia have decided to incorporate artificial intelligence into its work to make a positive impact on patient care, such as the Global Diagnostics Australia.

However, some have raised a question on whether AI would replace or negatively impact some aspects of Radiologist jobs in the future.

X ray hands

AI and its implication on Radiologists job

There is potential but also challenges from incorporating AI into the healthcare industry, specifically in the medical imaging industry. Concerns have been raised about the lack of secure access to sensitive patient data to develop AI models in the first place. Another could be the public’s general lack of trust of any new methods. Computerised decision-making in healthcare dates as far back as the early 1970s, but the harder challenge is in winning the public opinion. Finally, there is also the problem of evaluating new methods based on real-world data.

In Australia, RANZCR – The Royal Australian and New Zealand College of Radiologists – expressed its concern about the decision-making transparency, data privacy and ethics in the use of AI and machine learning. They have released a first draft Ethical Principles for AI in Medicine report earlier this year, and calls for the “correct use” of AI and machine learning, specifically with regards to clinical radiology and radiation oncology. They included the following guiding principles including Safety, Avoidance of bias, Transparency and explain-ability, Privacy and protection of data, Decision making on diagnosis and treatment, Liability for decisions made, Application of human values and Governance.

RANZCR believes that these guiding principles are necessary. Because the way radiology adapts to AI may have a direct effect for patients and other healthcare professionals. They see AI more as assisting radiologists and other professions to work in a more time-efficient and effective manner.

RANZCR President Dr Lance Lawler mentioned that the hype about radiologists being replaced by machines is merely an assumption. The hype indicates that some roles will change with AI infiltrating the industry, in terms of machines replacing some of the work done by radiologists.

However, they went on to say that it is far more likely that Radiologists and other impacted professions can do with a reapplying of people’s skills into different areas. Some things a machine could do better than a human, but definitely not all things. The perfect balance between the 2 will benefit everyone the most.

RANZCR hopes to get to a point where there is an accepted use of certain AI tools for some clinical circumstances. For radiology, that may be for breast or lung screening, or comparing responses to treatments. High volume, repetitive cases that machines can do easily.

There are still a number of implementation challenges though before AI is fully developed and accepted as a safe medical tool.

Doctor holding x ray scan results

Harvard Business Review on the matter

Several published articles including this Harvard Business Review agree with RANZCR in this instance.

According to the Harvard article, radiologists do more than just read and interpret images. They also consult with other physicians on diagnosis and treatment of diseases, they perform image-guided medical interventions, they define the technical parameters of imaging examinations to be performed tailored to a patient’s condition and they relate findings from images to other medical records. They then also discuss procedures and results with patients, and other additional activities.

The clinical processes for adopting AI-based image work are a long way from being ready to use frequently. To create a comprehensive collection of cases will take many years, which also further expands the role for radiologists in the AI world.

Deep learning algorithms for images must be trained on ‘labelled data’ which is mostly owned by vendors, hospitals, imaging facilities, or patients themselves and therefore, collecting them to accumulate a critical mass for AI training will be challenging and time-consuming. Changes will also be required in medical regulation and health insurance for automated image analysis, like what RANZCR is trying to implement.

Rather than refusing to work with AI, Radiologists are encouraged to learn and work with AI.

X ray scan of human

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