AI applications in robotics, diagnostic image analysis and precision medicine: Current limitations, future trends, guidelines on CAD systems for medicine

Tetiana Habuza, Alramzana Nujum Navaz, Faiza Hashim, Fady Alnajjar, Nazar Zaki, Mohamed Adel Serhani, Yauhen Statsenko

Research output: Contribution to journalReview articlepeer-review

11 Citations (Scopus)

Abstract

Background: AI in medicine has been recognized by both academia and industry in revolutionizing how healthcare services will be offered by providers and perceived by all stakeholders. Objectives: We aim to review recent tendencies in building AI applications for medicine and foster its further development by outlining obstacles. Sub-objectives: (1) to highlight AI techniques that we have identified as key areas of AI-related research in healthcare; (2) to offer guidelines on building reliable AI-based CAD-systems for medicine; and (3) to reveal open research questions, challenges, and directions for future research. Methods: To address the tasks, we performed a systematic review of the references on the main branches of AI applications for medical purposes. We focused primarily on limitations of the reviewed studies. Conclusions: This study provides a summary of AI-related research in healthcare, it discusses the challenges and proposes open research questions for further research. Robotics has taken huge leaps in improving the healthcare services in a variety of medical sectors, including oncology and surgical interventions. In addition, robots are now replacing human assistants as they learn to become more sociable and reliable. However, there are challenges that must still be addressed to enable the use of medical robots in diagnostics and interventions. AI for medical imaging eliminates subjectivity in a visual diagnostic procedure and allows for the combining of medical imaging with clinical data, lifestyle risks and demographics. Disadvantages of AI solutions for radiology include both a lack of transparency and dedication to narrowed diagnostic questions. Designing an optimal automatic classifier should incorporate both expert knowledge on a disease and state-of-the-art computer vision techniques. AI in precision medicine and oncology allows for risk stratification due to genomics aberrations discovered on molecular testing. To summarize, AI cannot substitute a medical doctor. However, medicine may benefit from robotics, a CAD, and AI-based personalized approach.

Original languageEnglish
Article number100596
JournalInformatics in Medicine Unlocked
Volume24
DOIs
Publication statusPublished - Jan 2021

Keywords

  • AI in medical imaging
  • Deep learning
  • Neural networks
  • Oncology assistive technologies
  • Precision medicine
  • Robotics and health
  • Survey

ASJC Scopus subject areas

  • Health Informatics

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