Needs, Challenges and Applications of Artificial Intelligence in Medical Education Curriculum (Preprint)

2021 ◽  
Author(s):  
Joel Grunhut ◽  
Oge Marques ◽  
Adam TM Wyatt
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Medical Education ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Medical Schools ◽  
Ethical Implications ◽  
Rigorous Curriculum ◽  
Surgical Suite ◽  
Medical Education Curriculum

UNSTRUCTURED Artificial intelligence (AI) is on course to become a mainstay in the patient's room, physicians office and the surgical suite. Current advancements in healthcare technology put future physicians in an insufficiently equipped position and even possible inferiority to machines. Physicians will be regularly tasked with clinical decision making with the assistance of AI driven predictions. Present-day physicians are not trained to incorporate the suggestions of statistical predictions on a regular basis nor are they knowledgeable in an ethical approach to incorporating AI in their distribution of care. Medical schools do not currently incorporate AI in the curriculum due to the lack of faculty expertise or knowledge on the matter, the lack of evidence in students desire to learn about AI, complacency with an already rigorous curriculum or lack of guidance on AI in medical education from medical education governing bodies. Medical schools should incorporate AI in the curriculum as a longitudinal thread in current subjects. Current students should have an understanding in the breadth of AI tools, the framework of engineering and designing AI solutions to clinical issues and acquiring knowledge about data appropriate to AI innovations. Study cases in the curriculum should include an AI recommendation that may present critical decision making challenges. Finally, the ethical implications of AI in medicine must be at the forefront of any comprehensive medical education.

Conclusion

2020 ◽  
pp. 277-288
Author(s):  
Pat Croskerry
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Medical Education ◽  
Cognitive Bias ◽  
Distributed Cognition ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Thinking Skills ◽  
System Error ◽  
Lateral Thinking

In the past two decades, there has been growing interest in the process of clinical decision making (CDM). Importantly, a strong interest has flourished in the process of diagnosis, particularly its failure rate. Two major strategies have been proposed to ameliorate diagnostic failure: minimizing system error and strategies to promote optimal clinical decision making. Many health care environments are not optimal. A variety of factors have been identified that influence the safe operation of the system, and clinicians need to be cognizant of them. In this chapter, a number of strategies are reviewed to optimize the CDM that occurs within the system, including the promotion of rationality, metacognition, thinking skills, flexibility, innovation and creativity in thinking, lateral thinking, cognitive bias mitigation, the incorporation of artificial intelligence, and distributed cognition. Instead of assuming that competence in CDM will be tacitly acquired in the course of medical education, clinicians need to advocate for explicit interventions that are known to raise the caliber of CDM.

The Use of Artificial Intelligence Systems for Support of Medical Decision-Making

2011 ◽  
pp. 1017-1029
Author(s):  
William Claster ◽  
Nader Ghotbi ◽  
Subana Shanmuganathan
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Statistical Analysis ◽  
Medical Records ◽  
Clinical Decision Making ◽  
Ct Scanning ◽  
Clinical Decision ◽  
Radiology Department ◽  
Self Organizing Map ◽  
One Year

There is a treasure trove of hidden information in the textual and narrative data of medical records that can be deciphered by text-mining techniques. The information provided by these methods can provide a basis for medical artificial intelligence and help support or improve clinical decision making by medical doctors. In this paper we extend previous work in an effort to extract meaningful information from free text medical records. We discuss a methodology for the analysis of medical records using some statistical analysis and the Kohonen Self-Organizing Map (SOM). The medical data derive from about 700 pediatric patients’ radiology department records where CT (Computed Tomography) scanning was used as part of a diagnostic exploration. The patients underwent CT scanning (single and multiple) throughout a one-year period in 2004 at the Nagasaki University Medical Hospital. Our approach led to a model based on SOM clusters and statistical analysis which may suggest a strategy for limiting CT scan requests. This is important because radiation at levels ordinarily used for CT scanning may pose significant health risks especially to children.

The Role of Artificial Intelligence in Clinical Decision Support Systems and a Classification Framework

2020 ◽  
pp. 167-186
Author(s):  
Steven Walczak
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Making ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Classification Framework

Clinical decision support systems are meant to improve the quality of decision-making in healthcare. Artificial intelligence is the science of creating intelligent systems that solve complex problems at the level of or better than human experts. Combining artificial intelligence methods into clinical decision support will enable the utilization of large quantities of data to produce relevant decision-making information to practitioners. This article examines various artificial intelligence methodologies and shows how they may be incorporated into clinical decision-making systems. A framework for describing artificial intelligence applications in clinical decision support systems is presented.

scholarly journals The Role of Artificial Intelligence in Many Dental Specialties

2021 ◽  
Vol 41 ◽  
pp. 03005
Author(s):  
Choirunisa Nur Humairo ◽  
Aquarina Hapsari ◽  
Indra Bramanti
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Clinic Management ◽  
Pediatric Dentist ◽  
Dental Procedures ◽  
Evolution Of Technology ◽  
Near Future

Background: Technology has become a fundamental part of human living. The evolution of technology has been advantageous to science development, including dentistry. One of the latest technology that draw many attention is Artificial Intelligence (AI). Purpose: The aim of this review is to explain the use of AI in many disciplines of dental specialties and its benefit. Reviews: The application of Artificial Intelligence may be beneficial for all dental specialties, varying from pediatric dentist to oral surgeon. In dental clinic management, AI may assist in medical record as well as other paperwork. AI would also give a valuable contribution in important dental procedures, such as diagnosis and clinical decision making. It helps the dentist deliver the best treatment for the patients. Conclusion: The latest development of Artificial Intelligence is beneficial for dental practitioner in the near future. It is considered as a breakthrough of the 21st century to support the diagnostic procedure and decision making in clinical practice. The use of AI can be applied in most of dental specialties.

scholarly journals Introducing Artificial Intelligence Training in Medical Education

10.2196/16048 ◽  
2019 ◽  
Vol 5 (2) ◽  
pp. e16048 ◽  
Author(s):  
Ketan Paranjape ◽  
Michiel Schinkel ◽  
Rishi Nannan Panday ◽  
Josip Car ◽  
Prabath Nanayakkara
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Medical Education ◽  
Health Care Professionals ◽  
Access To Health Care ◽  
Clinical Decision Making ◽  
New Technology ◽  
Medical Knowledge ◽  
Clinical Decision ◽  
Use Of Data

Health care is evolving and with it the need to reform medical education. As the practice of medicine enters the age of artificial intelligence (AI), the use of data to improve clinical decision making will grow, pushing the need for skillful medicine-machine interaction. As the rate of medical knowledge grows, technologies such as AI are needed to enable health care professionals to effectively use this knowledge to practice medicine. Medical professionals need to be adequately trained in this new technology, its advantages to improve cost, quality, and access to health care, and its shortfalls such as transparency and liability. AI needs to be seamlessly integrated across different aspects of the curriculum. In this paper, we have addressed the state of medical education at present and have recommended a framework on how to evolve the medical education curriculum to include AI.

Artificial Intelligence in Lung Cancer: Bridging the Gap Between Computational Power and Clinical Decision-Making

2020 ◽  
pp. 084653712094143
Author(s):  
Jaryd R. Christie ◽  
Pencilla Lang ◽  
Lauren M. Zelko ◽  
David A. Palma ◽  
Mohamed Abdelrazek ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Prediction Models ◽  
Biomarker Discovery ◽  
Surgical Techniques ◽  
Clinical Decision ◽  
Clinical Implementation ◽  
Cancer Management

Lung cancer remains the most common cause of cancer death worldwide. Recent advances in lung cancer screening, radiotherapy, surgical techniques, and systemic therapy have led to increasing complexity in diagnosis, treatment decision-making, and assessment of recurrence. Artificial intelligence (AI)–based prediction models are being developed to address these issues and may have a future role in screening, diagnosis, treatment selection, and decision-making around salvage therapy. Imaging plays an essential role in all components of lung cancer management and has the potential to play a key role in AI applications. Artificial intelligence has demonstrated value in prognostic biomarker discovery in lung cancer diagnosis, treatment, and response assessment, putting it at the forefront of the next phase of personalized medicine. However, although exploratory studies demonstrate potential utility, there is a need for rigorous validation and standardization before AI can be utilized in clinical decision-making. In this review, we will provide a summary of the current literature implementing AI for outcome prediction in lung cancer. We will describe the anticipated impact of AI on the management of patients with lung cancer and discuss the challenges of clinical implementation of these techniques.

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