Deep learning and artificial intelligence is becoming more and more prevalent in all aspects of daily life, with technology becoming ever more intelligent with constant improvements in computational power and it shows no sign of slowing and will undoubtedly begin revolutionizing aspects of life previously unfathomable. In simple terms, Deep Learning is a concept where technology attempts to mimic the human brain, and being able to analyze large amounts of data quickly, with the ability to make predictions and decisions in the same way a human would but at much greater paces and without the worry of human error. It is no surprise that this technology is making its way into healthcare, and the plethora of potential benefits to patients and healthcare teams alike is as of yet not fully quantifiable.
A primary emerging use for this technology is the image screening of potential cancer cases for diagnosis, and research has shown this novel methodology highly accurate in identifying tumors. With continuous revisions to algorithms and improvements in computational powers the full benefit of this adaptation of the technology is still to be determined. Some studies have already shown that deep learning algorithms have the potential to identify and diagnose tumors more effectively and efficiently than physicians who are provided the same details as the machine.
We welcome Original Research, leading-edge Reviews and Clinical Trials related but not limited to the aspects below:
- Use of AI networks in diagnosis and management of cancers
- How Deep Learning can be utilized in a clinical oncological setting
- Technological improvements in hardware and software which can improve outcomes for cancer patients
- AI based predictive and prognostic models for outcomes in oncology
- AI based models for predicting molecular biology of the tumors through imaging data
Please note: manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) are out of scope for this section and will not be accepted as part of this Research Topic.
Deep learning and artificial intelligence is becoming more and more prevalent in all aspects of daily life, with technology becoming ever more intelligent with constant improvements in computational power and it shows no sign of slowing and will undoubtedly begin revolutionizing aspects of life previously unfathomable. In simple terms, Deep Learning is a concept where technology attempts to mimic the human brain, and being able to analyze large amounts of data quickly, with the ability to make predictions and decisions in the same way a human would but at much greater paces and without the worry of human error. It is no surprise that this technology is making its way into healthcare, and the plethora of potential benefits to patients and healthcare teams alike is as of yet not fully quantifiable.
A primary emerging use for this technology is the image screening of potential cancer cases for diagnosis, and research has shown this novel methodology highly accurate in identifying tumors. With continuous revisions to algorithms and improvements in computational powers the full benefit of this adaptation of the technology is still to be determined. Some studies have already shown that deep learning algorithms have the potential to identify and diagnose tumors more effectively and efficiently than physicians who are provided the same details as the machine.
We welcome Original Research, leading-edge Reviews and Clinical Trials related but not limited to the aspects below:
- Use of AI networks in diagnosis and management of cancers
- How Deep Learning can be utilized in a clinical oncological setting
- Technological improvements in hardware and software which can improve outcomes for cancer patients
- AI based predictive and prognostic models for outcomes in oncology
- AI based models for predicting molecular biology of the tumors through imaging data
Please note: manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) are out of scope for this section and will not be accepted as part of this Research Topic.
