AI and Deep Learning Can be a Robust Oncological Tool
Recent advances in intelligent technology and machine algorithms have been helping many oncologists and radiologists for diagnosing cancer. With the spike of digitization in the medical sector, AI and DL artificial intelligence and deep learning have already started to play a notable role in cancer care. However, many medical clinics lack infrastructure – before understanding the full potential of such technology and find it challenging to integrate it into their diagnostic processes.
According to the latest study released by Wiley Online Library in Cancer Communications, titled, “Emerging role of deep learning‐based artificial intelligence in tumor pathology” portrays different insights around the same. The study notes that artificial intelligence is rapidly utilized in order to understand tumor pathology. However, there remains a constant tussle among the pathologists, clinicians, and patients – as there are still some questions unanswered about the technology, its usage, and costs.
In recent years, the analysis of pathology has become exceptional in comparison to human expertise and machine learning (ML). AI helps to reduce the limitations of subjective visual analysis and assessment from the pathologists. Besides, it connects different measurements for precision tumor treatment. Though deep learning is a subset of ML, it functions differently by using hard data rather than subjective components. As a result, AI-powered is outperforming than the older methods, highlighting more accuracy.
One of the vital ways of how DL is improving cancer care is tumor diagnosis. With an analytics and DL base, doctors can now use technology to figure out tumors from other lesions, as well as distinguish among malignant and benign tumors. Besides, it has the potential to identify genetic changes in tumors and biomarkers. As mentioned in the report, “In addition to biopsy and resection specimens, pathologists should perform cytology diagnosis in routine work. For cervical cytological diagnosis, DL‐based AI could classify cells as normal or abnormal in smear‐based and liquid‐based images, reaching an accuracy of 98.3% and 98.6%, respectively.”
Thus simplified DL models are in the process of development and soon be made available to aid clinicians subtype and stage cancers. And concerning the challenges, the researchers say that the algorithms supporting AI and DL technologies require validation on larger scales and should be adapted as the new data become available. The report also notes, “Building comprehensive quality control and standardization tools, data share and validation with multi‐institutional data can increase the generalizability and robustness of the AI algorithms…In addition, AI algorithms need to be continually validated and corrected by the diagnosis of expert pathologists.”
Another area on concern is the images used by the systems – they are mostly of massive file sizes. Hence, saving and sharing them is a vital issue amid the existing information technology (IT) infrastructure. In this regard, IT experts said that the newer advances would ease these problems. The medical industry thus hopes for more progress shortly with improvements in information technology (IT) and more adoption of 5G.
Undoubtedly, with each passing day, technology is manifesting itself as a must-have thing for cancer care – provided the proponents work to gain more scientific rigor, and medical personnel is comfortable with the deployment of tools.