Benefits of AI in Cancer Diagnostics

It is an established fact that diagnosing cancer at its earlier stages can remarkably boost possibilities for a cure. The fact also suggests that an appropriate as well as early diagnosis are evenly or even twice as much important as establishing novel treatment procedures. Viewed in this way, the venting potential of AI in healthcare and medicine, and especially in the field of oncology, has drastically enhanced diagnosis and treatment conclusions by virtue of rendering more personalized, efficient, and economical treatments.

Siddhartha Mukherjee, M.D., labeled cancer the “Emperor of All Maladies.”, in his Pulitzer-Prize winning book. The term is quite accurate as the deadly disease rules the focus of all who come in reach with it, whether they be medical practitioners, patients, or researchers, alleging the lives of more than 8 million globally and unsettling the lives of millions more.

Although there are still high hopes that various advances in artificial intelligence, as well as machine learning, will make symbolic progress in the field of oncology. The researchers are specifically focusing on the proclaimed benefits of machine learning and artificial intelligence (AI) methods when compared to prevailing conventional methods for the diagnosis and treatment of cancer as it may prove more efficient to diagnose cancer competently and with ease than conventional methods as they cut off the need for invasive procedures and interpreting the outcomes of imaging procedures.

Artificial intelligence (AI) is proficient at identifying sequences in extremely huge volumes of data, deriving relationships between perplexing features in the data, and recognizing attributes in data inclusive of images that cannot be understood by the human brain.

Alliance of AI technology with the field of oncology has the capability to upgrade the veracity and quickness of diagnosis, support clinical decision-making, and ultimately result in exceptional health outcomes.

Noticeably, the AI-steered medical care has the capacity to play an essential role in lowering health discrepancies, specifically in low-resource settings. Although, none of the researchers across the world foresees AI substituting medical professionals, radiologists, physicians, or pathologists.

However, the constantly rising geriatric population worldwide, elevated possibilities of diagnostic tests as well as developing prominence on precision medicine, AI technology, and especially machine learning and deep learning carries the potential to assist them to do their jobs by recognizing the high-risk circumstances they should aim attention on and supporting them to make decisions about the unsure diagnosis.

Moreover, as per the estimates of the World Health Organization, as the worldwide population grows feeble, the count of cancer patients is shooting up. And what is more is that the new cancer diagnoses are projected to elevate by 70% in the upcoming 2 decades, from 14 million to approximately 22 million, which signifies that the burden of cancer is a global phenomenon. In the nick of time, artificial intelligence (AI) and machine learning have significantly influenced numerous facets of the healthcare industry, and this progression in technology has unquestionably paved the way for the evaluation of huge datasets in a time and cost-effective manner. And thereby, clinical oncology and research are procuring the benefits of AI technology.

In many instances, the diagnosis of cancer is complemented with the visual analysis of infected tissue under the microscope by a pathologist. Notably, this method has been factually one of the crucial and essential tools employed in the diagnosis of solid tumors such as prostate, breast, and colon cancers.

Per contra, the assessment of the diseased tissue by a human expert can be a bit intuitive or semi-quantitative resulting in variations in diagnostic conclusions, thus disturbing clinical decision-making. On account of that, AI-based approaches are gaining huge traction in the global market as it excels at dealing with the problems in pathological tissue analysis.

AI has the capability of solving traditional tasks, such as segmentation of certain tissue entities, cell counting, and categorization of histological types.

Owing to the numerous benefits of AI technology in the healthcare sector, and particularly in the field of oncology, various corporate giants, as well as many startups, are also trying to bring machine learning algorithms to the fight against cancer.

For instance, IBM and Google are already concentrating on making breakthroughs in oncology, using advanced AI algorithms for early diagnosis and personalized treatment of cancer.

Also, Google DeepMind declared a research partnership with the University College London Hospitals’ radiotherapy department, as the DeepMind will test the use of machine learning to decrease the time it takes to plan radiotherapy treatment for hard-to-treat cancers of the head and neck.

Furthermore, BERG Health, a startup that employs an AI platform for drug discovery, initiated a phase II clinical trial, in recent past years, for a drug compound that carries the potential to treat pancreatic cancer.

Although, on the other hand, Andreas von Deimling, a neuropathologist at the German Cancer Research Centre says that he doesn’t imagine a computer will provide all the answers in medicine. According to him the AI technology is never going to replace pathologists entirely, it is just an extremely tremendous and powerful tool that should be in the hands of the pathologist.

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