AI for imaging and diagnosis: Research directions

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FUTURE RESEARCH DIRECTIONS ON THE APPLICATIONS OF
AI IN IMAGING AND DIAGNOSIS
An Academic presentation by Dr. Nancy Agnes,
Head, Technical Operations, Tutors India Group
www.tutorsindia.com Email info_at_tutorsindia.com
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Artificial Intelligence in the field of Medicine
and Healthcare

• 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)
  in medicine and healthcare constitutes a
  continuing revolution and has yet to be widely
  understood. This integration has the prospect of
  significant breakthroughs but 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).

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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 could detect 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)

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• 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).

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Future Research Directions

• A study on the synergistic effects of algorithms
  for diagnosis

• 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.

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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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