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The Impact of AI on Jobs

The many promises of AI included automated image segmentation, detection of cancerous lesions, and comparison of images. AI systems will not get tired while performing the same tasks that doctors may get tired and lose accuracy while completing. AI can also help physicians by completing tedious tasks and managing large amounts of data. Depending on the requirements of a job, a worker can either be helped by technology or in competition with it. For example, improvement in robotics can decrease opportunities for employment for workers in manufacturing but in the field of artificial intelligence and cognitive technology, it would increase the demand for workers. Recent data expresses that when considering its image and predictive analysis, AI might soon prove to be more efficient than radiologists. However, it is likely that AI will not replace the role of general physicians, but rather augment them. This is due to the fact that AI is unable to engage in interactions with patients that
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Doctor-Patient Feedback and Interpretation

Of the different challenges that AI algorithms in medical training pose, feedback and interpretability are two of the most prominent issues. Interpretability of AI algorithms is the ability of a human to understand the way it made a connection between features extracted and its predictions. A study done at Mount Sinai Hospital created a type of AI algorithm known as deep learning using data from 700,000 patients. Their algorithm was highly accurate and was able to diagnose conditions that even experts struggle to diagnose, such as  schizophrenia. However, there was no way for humans to know how the system reached a diagnosis (Paranjape, Schinkel, Nannan Panday, Car, & Nanayakkara, 2019) . This is an issue because it becomes very hard for patients to trust a system that cannot provide an explanation and if a calculation were to be made incorrectly that puts a patient in danger, then it is not known whether the doctor, the hospital, or the company that developed the AI algorithm is l
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Segmentation and Cervical Cancer Classification

Cervical cancer does not occur as often as brain and lung cancer, however it still possesses an incredible threat to women every year. While it can be cured if found in an earlier stage, many women die every year due to the fact that their cancer was not detected at an earlier time and symptoms did not appear until the cancer was too far advanced to treat. Cells in the cervix can either by squamous cells, which when infected cause squamous cell carcinoma, or glandular cells which cause adenocarcinoma. Squamous cell carcinomas are flat cells that vary in morphology and cover the surface of the cervix. Adenocarcinoma cells develop in the glandular cells which produce music and grow along the inside of the endocervix, the passage that runs from the cervix to the womb. While both squamous cells carcinomas and adenocarcinomas are both associated with HPV infection, adenocarcinomas are much less common (Conceição, Braga, Rosado, & Vasconcelos, 2019, p. 2) . Because cervical cancer is dif
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Biomarkers and their Role in Screening for Lung Cancer

Two of the principal components of treating lung cancer are screening and detection.  In this blog post, I will be discussing how AI can be used to identify tumor progression, and the role that biomarkers play in lung cancer diagnosis. Histopathological images can provide scientists with information about the environment surrounding a tumor, also known as the microenvironment, which can have an impact on the tumor itself. The microenvironment of lung cancer consists of the tumor cells, lymphocytes, stromal cells, macrophages, and blood vessels. These components are all important factors for tumor progression and have differing effects on the outcome of the lung cancer patient. Researchers of one study created a type of deep learning model, a convolutional neural network, that had the ability to identify the differences between these components of the microenvironment of lung cancer using pathological images. This study was completed by removing small images of the cell nuclei which wer
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Application of AI for Predictions Related to Brain Cancer

A s discussed in my previous blog posts, a tool known as the Virtual Operative Assistant has been created that will allow medical students to practice surgery in a similar setting to the operating room, before they complete surgeries on actual patients (Mirchi et al., 2020). In addition to surgical training, making predictions related to a patient's prognosis, such as chance of survival, is one of the most important things for choosing the proper course of treatment. This blog post will cover another important prediction related to neuro-oncology- if a patient's tumor will metastasize to the brain. A metastasis, or the formation of a second tumor away from the original tumor, is one of the most deadly complications that can occur from cancer. A recent study was performed in which an AI system was developed to outline characteristics of cancer cells in tissue grafts from patients that came from both the primary tumor and brain metastases (Oliver et al., 2019). A cell imaging alg
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Weaknesses of AI Surgical Training

The original use for AI in surgical education was to provide individualized feedback to students, and very little attention was paid to actual assessments of students learning. Since then many improvements have been made to AI including the development of a teaching assistant that can teach material as well as supervise students and provide feedback. However as mentioned in my previous blog post, although surgical simulations used as training for surgeons can be extremely beneficial, the virtual operating assistant along with other surgical training tools that use AI algorithms, do possess certain weaknesses which are explained in a research study performed by Kai Siang Chan and Nabil Zary. The inability to provide sufficient feedback, the possibility of “cheating the system”, the presence of board parameters, and the lack of surgeon expertise and involvement are the few of the obstacles that stand in the way of AI surgical training tools from being implemented in the real world. One m
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The Virtual Operative Assistant

When looking at modern cancer treatment, one of the most important aspects is educating the doctors of the future. Artificial Intelligence can prove to be a very useful tool when large amounts of data are being used. A recent study on The Virtual Operative Assistant completed by Nykan Mirchi, Vincent Bissonnette, Recai Yilmaz, Nicole Ledwos, Alexander Winkler-Schwartz, and Ronaldo F. Del Maestro, shows the benefits of using AI to conduct training that tests cognitive skills and determines the level of psychomotor expertise that an operator possesses through the use of a surgical simulation. The study aimed to create a model that used AI and could be utilized to train surgeons and authenticate the model using the Virtual Operative Assistant. Artificial Intelligence technologies have often been labeled “black boxes” because of how difficult it is to understand how their algorithm makes a certain decision, however this newly designed model shows potential in multitudes of fields, includin
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