Cancer Screening Simplified With Explainable AI

The use of the models of Explainable AI is gradually gaining traction in the smart healthcare system for making efficient decisions. The healthcare industry is one where it is of vital importance to ensure that the decisions that are made are based on algorithms that can be trusted and also take care of the fact that the healthcare professionals are able to figure out the decisions that are made by these AI algorithms. The XAI models hold the potential to improve interpretability as well as explainability associated with the processes of decision-making that are based on artificial intelligence. The field of healthcare is indeed very complicated and it demands the use of models that come along with that fine touch of sophistication particularly when it comes to classifying cancer images or in the process of cancer screening.  

Cancer is indeed a very dreadful disease. It is associated with a condition which claims the lives of innumerable people every year. Detection as well as diagnosis of this deadly disease are challenges that are being faced by the healthcare industry around the world. This is precisely the reason as to why advancements are being made in the realm of detection of cancer.

Explainable AI or XAI in brief

Before we move ahead to understand the use of Explainable AI in the processes of cancer screening, it is important to understand what is Explainable AI? Well in very simple words, Explainable artificial intelligence or XAI is associated with a set of processes and also methods that enable the human mind to understand and also trust the results or the output that is developed by the machine learning algorithms. 

Explainable AI is used to describe a model of AI which includes the expected impact created and also the biases if any. Explainable AI becomes instrumental in the process of characterization of the model accuracy, transparency as well as fairness and the outcomes when it comes to dealing with the decision making process powered by AI.  When it is about implementing the AI models for the purpose of production then it becomes extremely important to harness the power of Explainable AI or XAI for the purpose of building trust as well as confidence.

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The contributions of XAI in the world of medical science 

Next let us move on to see the contributions of this wonder of science that is Explainable AI in the world of cancer detection and diagnosis. 

The story of IBM Watson 

IBM Watson for Oncology leverages the power of artificial intelligence to help oncologists in preparing various plans of treatment for cancer patients. When XAI or Explainable AI is integrated, then Watson for Oncology provides accurate explanations for the recommendations that are being made for the purpose of treatment of cancer patients, for instance, how the different particular characteristics of the cancer patient and also the historical data impact the different options that have been suggested. This has proven to be of real help as it helps oncologists to comprehend the reasoning that AI makes use of and this paves the way for enhancement of the process of decision making. 

XAI in the realm of lung cancer 

Lung cancer happens to be one of the most common as well as deadly forms of cancer on a global basis. 2.09 million new cases, 1.76 million deaths every year – those are the figures associated with lung cancer cases each year. Explainable AI which is a branch of artificial intelligence aims to create models that simplifies the process of comprehending and paves the way for early detection of lung cancer as well as diagnosis in an accurate manner. XAI is particularly vital for the healthcare industry as it helps to build the confidence of healthcare professionals and also the patients in explaining the very reasons as to why the machine learning algorithms are making such predictions.  The XAI model can very efficiently assist doctors in making informed decisions and also give patients personalized suggestions and these are based on the particular risk factors. Cancer screening test price is something in this respect is something that needs to be considered while moving ahead with the XAI and ML models.

It is quite a feasible option to build models by making the best use of XAI that can very accurately and precisely predict the risk of a person acquiring lung cancer and these are based on the various risk factors like the age of the person, the smoking history, the family history associated with cancer. Also by understanding the decision making process of the AI model, healthcare professionals can gain significant knowledge about the risk factors that are closely related to lung cancer and this way various plans of treatment and the ways to prevent the occurrence of this deadly disease can be framed with precision. Cancer screening package lets an individual understand the different associated costs involved with the screening process. 

SHAP is simplifying the process of cancer detection 

An XAI framework, SHAP which stands for Shapley Additive Explanations is an open source package that becomes instrumental in determining whether  a particular machine learning model can be trusted or not. In a particular study it was seen that when the SHAP values were in the machine learning models then it helped in the identification of genes that were associated with the progression of skin cancer. These genes that were identified can actually serve as diagnostic as well as prognostic biomarkers helping in the early detection of Squamous Cell Carcinoma or SCC which is a type of skin malignancies. 

Many breast cancer detection as well as classification techniques make the best use of SHAP for the purpose of explaining the results to healthcare professionals. Many researches have been conducted where XAI techniques have been combined to gain insights about the complex mechanisms associated with the development as well as progression of breast cancer. Also it is helping to carry out the treatment procedures. 

The situations that demand the use of XAI

Explainable AI or XAI is definitely recommended when situations such these as mentioned below arise:

• When fairness is something which is given the top priority and when the end customers demand a proper explanation to make a particular informed decision.
• When the consequences of a decision made by AI can actually be far reaching that is it can prove to be fatal as in the case of a surgery. 
• When the cost of a particular mistake can be very high, for instance, misclassification of a malignant tumor that could lead to several problems like bearing unnecessary costs, health risks, and the list goes on.

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Final Note

Thus we saw in brief the boon of Explainable AI or XAI to mankind. It goes without saying that this branch of artificial intelligence is indeed helping the healthcare professionals and the patients in the most unique way. Detection and treatment of cancer shall now find a way out and definitely this brings in a new ray of hope to the world. 

Weavers Web Solutions is also playing its part and is contributing towards the progress of this field of science and technology. The great team with its extraordinary skills and outstanding knowledge is helping the world cope with the problem of cancer screening with its excellent AI solutions. So if you are interested in making a difference in the world and creating solutions that could benefit the world greatly then it is time that we collaborate together. Contact us today at:  info@weavers-web.com