Future Research directions on the applications of AI in imaging and diagnosis

1. FUTURE RESEARCH
DIRECTIONS ON
THE APPLICATIONS
OF AI IN IMAGING
AND DIAGNOSIS
An Academic presentation by
Dr. Nancy Agnes, Head, Technical Operations, Tutors India
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2. Artificial Intelligence (AI) represents an emerging field in
science and technology. Its influence spans across various
aspects of human life, impacting individuals, social circles,
businesses, and even countries
The use of artificial intelligence (AI) inmedicine and healthcare
constitutes a continuing revolutionand hasyet to bewidely
understood. This integration has the prospect of significant
breakthroughsbut also introduces uncertainties as well as
serious problems. In addition, AI has already paved the path for
completely revolutionary techniques in this discipline.
AI and its related technologies within Medicine and Healthcare
have undergone remarkable development, transitioning from
mere computer programs aiding in medical image analysis to
their integration across nearly every clinical and administrative
domain (Gómez-González, 2020).
Artificial Intelligence in the field of
Medicine and Healthcare

3. Artificial intelligence in imaging and diagnosis
Research has shown that AI has displayed remarkable precision and sensitivity in spotting abnormalities in
imaging, offering the potential to improve the detection and understanding of tissue-related issues.
AI coulddetect changes in image patterns that humans might not notice. For example, using machine
learning to assess brain MRI data has the potential to detect tissue changes associated with early ischaemic
stroke quickly after symptoms develop, and this technology may give more sensitivity than a human examiner
within a restricted timeframe (Oren, 2020).
AI-based medical devices (AMIDs) have gained popularity in recent years, thanks to technological
advancements in AI and Machine learning. AMIDs assist in the diagnosis, management and pharmaceutical
development for various diseases. AIMDs have shown noteworthy results in the diagnosis and prognosis of
common diseases (CDs)

4. Complex Neural Networks (CNNs), a subtype of Artificial Intelligence, can also be used for imaging and
diagnosis. CNNS have been successfully implemented to diagnose breast cancer, lung cancer and brain
cancer with high performance.
AI can help in the classification and detection of pre-malignant lesions and cancers. A classic example where
automation can be used is indeterminate pulmonary nodules, which are usually benign are found frequently,
of which a small proportion represent early stage cancers.
Another way AI is helping oncologists is by improving outcome prediction and detecting recurrences earlier
after therapy.
This means that instances deemed high-risk may receive more aggressive initial treatment, such as
increased radiation doses, whilst those deemed low-risk may receive less intense treatment to minimise
adverse effects (Hunter, 2022).

6. A study on the synergistic effects of algorithms for diagnosis
Future Research Directions
Mirababie et al. (2021) studied how algorithms can be used in diagnosing diseases, and it was discovered
that some algorithms have researched more than others, leading to a research gap.
Future research directions can include how combining various algorithms compare with using a single
algorithm for diagnosis.
A study on clinicians’ perspectives on the limitations of Artificial Intelligence in diagnosis
A study by Kumar et al. (2022) delved into the applications and limitations of Artificial Intelligence in
imaging and diagnosis.
It was revealed that many healthcare professionals found artificial intelligence beneficial, although there
were limitations regarding the data size and generalisability. Future research can deal with obtaining
feedback from healthcare professionals and clinicians about the limitations in AI-driven diagnosis.

7. CONCLUSION
The application of Artificial Intelligence (AI) in Medicine and Healthcare is a developing sector with
enormous potential.
Despite uncertainties and limitations, artificial intelligence (AI) continues to transform medical imaging
and diagnosis, exhibiting outstanding precision in detecting discrepancies and helping in disease
management.
AI-based medical device improvements have shown potential in identifying many ailments, whereas
Complex Neural Networks exhibit high performance in diagnosing cancers.
Future research directions include investigating algorithm synergy for diagnosis and addressing
clinicians’ viewpoints to improve the accuracy of AI-driven diagnosis.

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