**8. Artificial intelligence and classroom**

AI may be able teachers to identify students that may need some additional help or individuals with special needs that may struggle in a typical classroom. AI algorithms have been designated to increase the production of learning and the efficiency and effectiveness in learning. There are some paramount roles of AI in education. They include automation of grading with an approach tailored explicitly to short answered question other than multiple choice questions, teacher's support using chatbot able to communicate directly with students, student's aid with future students having an AI lifelong learning companion starting from high-school to university and postgraduate education adopting a new model of AI-controlled continuing professional development. Moreover, AI may be able to identify each student's strengths and weaknesses in a way that may be more standard than conventional teaching, which may be linked to the current motivation of the teacher. There is a personalized learning curriculum with an AI machine able to help students with special needs by adapting teaching material to lead them to success without being downscaled for mental r physical barriers. AI will allow teachers to Act as learning motivators and help to mentor undergraduate and postgraduate students to the best suitable path for them. As AI takes on more of an education role by providing students with the necessary information, this procedure will change the position of teachers in the classroom. Educators will move into the role of classroom supervisor, facilitator or learning motivator and adopt a previously unimaginable relationship with the students. Some examples of classroom-based AI include Thinkster Math, brain, and Content Technologies Inc. Thinkster Math (http://get.hellothinkster.com/why-tabtor-is-now-thinkster/), which is a math tutor able to identify the level of each student allowing each student to improve the logic process by providing video assistance for stuck students and immediate, personalized feedback. Brainly (https://brainly.com/) is the social media site for classroom questions allowing users to ask questions and receive verified answers from fellow students. Content Technologies, Inc. (http://contenttechnologiesinc. com/) is an AI company using Deep Learning to create customized textbooks. Teachers carefully import curricula (syllabi) into a CTI engine. The CTI equipment then masters the content and uses specific algorithms to create tailored books and coursework based on core concepts. Mika (https://www.carnegielearning.com/ products/software-platform/mika-learning-software/) is another AI based on math, and like Thinkster Math, Carnegie Learning's Mika harbors AI-based tutoring tools for learners, who may be too busy for after-school tutors. This solution has also been promoted for students who require personalized attention. Finally, Netex Learning (http://www.netexlearning.com/en/) teachers design curriculum across a variety of digital platforms and devices (iPad, android or surface devices). The use of Netex allows teachers to create customized materials to be published on any digital platform while providing tools for video sessions, adapted assignments, and learning analytics (https://www.thetechedvocate.org/5-examples-artificialintelligence-classroom/). There will be plenty of apps in the future able to target pathology residents in their curriculum preparation and the proposed limitation of the pathology education to core-competencies only is a tragic evolution. The identification and implementation of these technologies should form the basis for venture companies able to shape the transforming platform of work of pathologists. An application to improve pathology teaching is the use of eye-tracking technology [73, 74]. During the teaching of histopathology skills to medical students and postgraduates, the use of eye tracking allowed a better performance at the final score in learners that took advantage of this technique compared to learners that did not utilize it.

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*Digital Pathology: The Time Is Now to Bridge the Gap between Medicine and Technological…*

DP is far from the niche described a few years ago. DP is a stable platform in many universities and colleges. Radiology images are chiefly acquired as digital data and saved in robust picture archiving systems [75]. Hartman et al. [75] describe the challenges using digital pathology for second opinion intraoperative consultations for over 10 years implementing an incremental rollout for digitalization in pathology on subspecialty benches. They began with cases that contained small amounts of tissue (biopsy specimens). The authors successfully scanned over 40,000 slides through their digital pathology system and emphasized that a successful conversion to digitalization in pathology requires pre-imaging adjustments, integrated software, and post-imaging evaluations. The limitations in the implementation of digital pathology include: (1) Infrastructure and resources support, although the cost of acquisition and maintenance of DP equipment, networking equipment, and staffs expenses are cheaper than a few years ago. (2) Integration into an existing laboratory information system (LIS) or Provincial Health Network (PHN) portals such as the upcoming Epic software implementation in several regions (e.g., Alberta, Canada) [76–79] rather than a stand-alone DP education system may attract investments from the government or the private sector or creating public-private partnerships. (3) Acceptance of digital pathology images in the diagnosis (4) Engagement of all

Artificial neural networks (ANN) are increasingly a desirable technique for solving machine learning and AI issues. The variety of neural network type and their use of diagnosis and therapy in medicine requires skilled knowledge to choose the most appropriate approach. ANN may be considered as simplified models of the human brain neuronal networks. In both natural and artificial, the essential requirement for a system is that it should attempt to capture the necessary information for further processing. The simplest ANN that may be listed here is the threshold logic unit or TLU. A processing unit for numbers with n inputs x1, x2,…, xn and one output y constitute the TLU. In the TLU, there is a threshold θ, and each input xi is associated with a weight wi. A TLU computes the function and then output a "1" if this sum exceeds a threshold, and a "0" otherwise. TLUs mimic the thresholding comportment of biological neurons *in vivo*. This simple logical unit may become more complicated and apply to various areas of medicine, such as diagnostic systems, biochemical analysis, image analysis, and drug development. ANNs are very useful in medicine and applications to have been described in the literature dealing with problems in cardiology and oncology. ANNs are an AI technique that uses a set of nonlinear equations to mimic the neuronal connections of biological systems. ANNs are useful for pattern recognition and outcome prediction applications and have the potential to bring AI techniques to the personal computers of practicing pathologist, assisting them with a variety of diagnostic procedure, such as hepatocellular carcinoma [80–83]. The benefits to utilize ANNs is that they are not affected by external factors such as fatigue, working conditions and emotional or mood state. ANNs may represent a useful AI companion in the routine diagnostic pathology as it has been used in several other fields in medicine, such as to analyze blood and urine samples, track glucose levels in diabetics, and determine ion levels in body fluids. There are numerous applications including tumor detection in ultra-sonograms, classification of chest X-rays, blood vessel classification in MRI, determination of skeletal age from X-ray images, and determination of brain maturation, among others. ANNs are also useful in the

*DOI: http://dx.doi.org/10.5772/intechopen.84329*

pathologists in practice or training.

**10. Artificial neural networks in medicine**

**9. Challenges of digital pathology education**

*Digital Pathology: The Time Is Now to Bridge the Gap between Medicine and Technological… DOI: http://dx.doi.org/10.5772/intechopen.84329*
