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 Table of Contents  
EDITORIAL
Year : 2022  |  Volume : 11  |  Issue : 2  |  Page : 65-67

Will artificial intelligence assume a role in anatomy education?


1 Assistant Professor, Department of Anatomy, JSS Medical College, JSSAHER, Mysore, Karnataka, India
2 Assistant Professor, Department of Anatomy, All India Institute of Medical Sciences, Bathinda, Punjab, India
3 Professor (Additional), Department of Anatomy, All India Institute of Medical Sciences, Rishikesh, Uttarakhand, India

Date of Submission25-Apr-2022
Date of Decision28-Apr-2022
Date of Acceptance27-Apr-2022
Date of Web Publication26-May-2022

Correspondence Address:
Kumar Satish Ravi
Department of Anatomy, All India Institute of Medical Sciences, Rishikesh, Uttarakhand
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/NJCA.NJCA_85_22

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How to cite this article:
Pushpa N B, Patra A, Ravi KS. Will artificial intelligence assume a role in anatomy education?. Natl J Clin Anat 2022;11:65-7

How to cite this URL:
Pushpa N B, Patra A, Ravi KS. Will artificial intelligence assume a role in anatomy education?. Natl J Clin Anat [serial online] 2022 [cited 2022 Jul 4];11:65-7. Available from: http://www.njca.info/text.asp?2022/11/2/65/346078




  Artificial Intelligence Top


Intelligence is the ability to contemplate, analyze, learn from previous experience and adapt to a new environment.[1] The worldly life of present-day human beings could be significantly attributed to the significant role played by “intelligence.” Intelligence ensures man's cognitive capacities to understand, analyze, reason, recognize, invent, plan, overcome the crisis, and use language for communication.[2] When exhibited by machines, such intelligence is referred to as artificial intelligence (AI).[3]

Although the concept of AI and its application dates back to ancient Greece, it was John McCarthy who introduced this term at the conference held at Dartmouth College in 1956. AI is the ability of a computer or computer-controlled system to perform tasks that a human being can do.[4] It involves the development of computer systems that can reason, understand, generalize, and even learn from the experience. The broader application of AI in various fields owes to the development of advanced algorithms, efficient, low-cost graphic processors, and massive annotated databases. Main elements of AI are shown in [Figure 1].
Figure 1: Main elements of AI

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Machine learning (ML) is a part of AI often used synonymously with AI. ML enables machines to learn from data sets without being programmed exclusively for the designated task. ML can function in three ways – supervised, unsupervised, and recruitment. The supervised system functions with data set provided by the human to get the desired outcome, while unsupervised ML uses confidential data within the system. The combination of the above two forms the basis of reinforcement type, where at most precision and accuracy are expected.[5],[6]

Deep learning (DL) is a highly evolved form of AI and a subset of ML.[7] DL works with unorganized data and complex problems using complex neuronal networks. In DL, the designated system is trained with an extensive data set to reach human-like accuracy in the performance. Applications of AI are well appreciated in self-automated decision-making system, advanced web search engines, AI robots, language processing tools, and disease mapping.[8]

The working model of AI is inspired by how the human brain functions. The goal of AI is to enable a computer system which can mimic human intelligence to solve complex issues using large data set and neural networks. Due to its efficiency, AI has significant potential to influence the field of medicine, both in diagnostics and therapeutics.[9] The application of AI in medical education is evolving and needs to be customized based on regional requirements.


  Possible Application of Artificial Intelligence in Anatomical Sciences Education Top


Teaching and learning

The essential goals of AI are reasoning and problem-solving, knowledge presentation, planning, learning, and social intelligence, which can be explored to benefit the student community. AI has a crucial role in medical education in the form of an intelligent tutoring system.[10],[11] AI helps bridge the gap between the traditional teaching mode and the present digital generation of students through a smart tutoring system. This includes identifying and responding to the knowledge gap that exists among the students. With proper coding, virtual facilitators can cater constructive learning approaches, quick and customized feedback to students, and one-to-one or personalized teaching. It can also perform the activities such as attendance tracking and assignment grading, which are often done manually by most medical educators.[12] Although there are various benefits with advanced technologies such as AI, care must be taken while structuring computer system-based learning and algorithms related to AI concerning ethical and moral challenges.[13],[14] Hence, the outcome of such an application should be transparent, credible, auditable, and reliable.


  Assessment Top


In medical education, the assessment forms an essential process in measuring the students' progress. Most studies about anatomy assessment methodologies spin around assessment usefulness indices such as objectivity, validity, reliability, and educational impact.[15] Uniform assessment for all the students without any individual distinction is universally accepted. The student community will appreciate such an impartial, objective assessment method. Objectivity becomes an essential concern when different examiners are satisfied with the students' different degrees of knowledge and skills. The uniformity can achieve such objective, impartial assessment in the questions/tasks, bringing about more standardization in the expected response, skills, and behavior from the students.[16] With human beings, it is not feasible to have an objective impartial assessment that is entirely devoid of innate biases about the subject matter and cultural biases such as gender and region. Furthermore, much diversity exists in evaluating anatomical knowledge, attitude toward the subject, and skills. This mandates concurrence regarding best strategies and methodologies to be implemented to ensure reliability, consistency, validity, and standardization.[17] Such goals can be achieved by involving applications of AI in assessment, which can automatically alter the difficulty level and evaluate the students without any bias.


  Other Aspects Top


Spatial ability is a key factor in learning anatomy, which is well acquired by cadaveric dissection. In recent years, three dimensional images obtained by computerized tomography, magnetic resonance imaging, and ultrasound are also widely used to orient the medical students to internal organ structure and their relations. Quite often, such radiological images from the primary representation of internal anatomy, providing better structural details than textbook diagrams.[18],[19] The development of image biobank and its application in developing algorithms and training computer system[20] can be effectively used to teach young doctors.

Cadaveric dissection is an integral part of teaching anatomy. Optimal preservation techniques are vital in continuing the tradition of cadaveric dissection.[21] Formaldehyde is a universally used chemical for the preservation of corpses/specimen. On the other hand, studies have shown the carcinogenic effect of formaldehyde.[22] The benefits of formaldehyde usage have outweighed the occupational hazards caused in the dissection hall.[23] Hence, like the use of robots in surgical procedures, machines can be trained to dissect and prepare specimens for teaching purposes.

The applications of AI, particularly in medicine, education, finance, industries, and military fields, are working more promisingly. To ascertain the potential of AI in medical education, one should consider the goal of medical education, i.e., competent and responsive doctors. Just like any swap in medical education should be guided by this goal. There appears a need to interrogate, given the potential AI should be an essential tool in teaching and learning anatomy rather than replacing the teachers, which can be a massive threat to the profession. Hence, there needs to be a beneficial and practical balance between human resources and AI usage. At the same time, it has to be considered that excess usage of automation should not pave the way for loss of critical thinking and deterioration of interpersonal relationships and communicational skills.

Acknowledgment

We sincerely thank Mrs. Prajwala Nagavalli Basavanna B.E., MTech (Computer Science), Software Engineer, Wipro Pvt. Limited Bangalore, Karnataka, India, for her valuable technical inputs in preparing this manuscript.



 
  References Top

1.
Tirri K, Nokelainen P. Measuring Multiple Intelligences and Moral Sensitivities in Education. Moral Development and Citizenship Education. Rotterdam, Netherlands: Sense Publishers; 2011.  Back to cited text no. 1
    
2.
Colom R, Karama S, Jung RE, Haier RJ. Human intelligence and brain networks. Dialogues Clin Neurosci 2010;12:489-501.  Back to cited text no. 2
    
3.
Collins C, Dennehy D, Conboy K, Mikalef P. Artificial intelligence in information systems research: A systematic literature review and research agenda. Int J Inf Manag 2021;60:102383.  Back to cited text no. 3
    
4.
Brighton H, Selina H. Introducing Artificial Intelligence. 2nd ed. Cambridge, UK: Icon Books; 2007.  Back to cited text no. 4
    
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Raymond JL, Medina JF. Computational principles of supervised learning in the cerebellum. Annu Rev Neurosci 2018;41:233-53.  Back to cited text no. 5
    
6.
Kulkarni P, Mahadevappa M, Chilakamarri S. The emergence of artificial intelligence in cardiology: Current and future applications. Curr Cardiol Rev 2021:10.2174/1573403X17666211119102220.  Back to cited text no. 6
    
7.
Hu J, Niu H, Carrasco J, Lennox B, Arvin F. Voronoi-based multi-robot autonomous exploration in unknown environments via deep reinforcement learning. IEEE Trans Veh Technol 2020;69:14413-23.  Back to cited text no. 7
    
8.
Bengio Y, Courville A, Vincent P. Representation learning: A review and new perspectives. IEEE Trans Pattern Anal Mach Intell 2013;35:1798-828.  Back to cited text no. 8
    
9.
Hamlet P, Tremblay J. Artificial intelligence in medicine. Metabolism 2017;69S: S3640.  Back to cited text no. 9
    
10.
Friedman CP, Donaldson KM, Vantsevich AV. Educating medical students in the era of ubiquitous information. Med Teach 2016;38:504-9.  Back to cited text no. 10
    
11.
Masters K. Artificial intelligence in medical education. Med Teach 2019;41:976-80.  Back to cited text no. 11
    
12.
Pfeifer CM. A progressive three-phase innovation to medical education in the United States. Med Educ Online 2018;23:1427988.  Back to cited text no. 12
    
13.
Char DS, Shah NH, Magnus D. Implementing machine learning in health care - Addressing ethical challenges. N Engl J Med 2018;378:981-3.  Back to cited text no. 13
    
14.
Goldhahn J, Rampton V, Spinas GA. Could artificial intelligence make doctors obsolete? BMJ 2018;363:k4563.  Back to cited text no. 14
    
15.
Samarasekera DD, Gopalakirshnakone P, Gwee MC. Assessing anatomy as a basic medical science. In: Chan LK, Pawlina W, editors. Teaching Anatomy: A Practical Guide. 1st ed. New York, NY: Springer International Publishing; 2015. p. 279-89.  Back to cited text no. 15
    
16.
Brenner E. Human body preservation - old and new techniques. J Anat 2014;224:316-44.  Back to cited text no. 16
    
17.
Sullivan GM. A primer on the validity of assessment instruments. J Grad Med Educ 2011;3:119-20.  Back to cited text no. 17
    
18.
Schnotz W. Towards an integrated view of learning from text and visual displays. Educ Psychol Rev 2002;14:101-20.  Back to cited text no. 18
    
19.
Sagoo MG, Vorstenbosch MA, Bazira PJ, Ellis H, Kambouri M, Owen C. Online assessment of applied anatomy knowledge: The effect of images on medical students' performance. Anat Sci Educ 2021;14:342-51.  Back to cited text no. 19
    
20.
European Society of Radiology (ESR). What the radiologist should know about artificial intelligence - An ESR white paper. Insights Imaging 2019;10:44.  Back to cited text no. 20
    
21.
Brenner E. Human body preservation: old and new techniques. J Anat 2014;224:316-44.  Back to cited text no. 21
    
22.
Swenberg JA, Moeller BC, Lu K, Rager JE, Fry RC, Starr TB. Formaldehyde carcinogenicity research: 30 years and counting for mode of action, epidemiology, and cancer risk assessment. Toxicol Pathol 2013;41:181-9.  Back to cited text no. 22
    
23.
Coleman R. Reducing the levels of formaldehyde exposure in gross anatomy laboratories. Anat Rec 1995;243:531-3.  Back to cited text no. 23
    


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