|Year : 2020 | Volume
| Issue : 1 | Page : 53-59
Extending capabilities of artificial intelligence for decision-making and healthcare education
Mohd Javaid1, Abid Haleem1, Ibrahim Haleem Khan2, Raju Vaishya3, Abhishek Vaish3
1 Department of Mechanical Engineering, Jamia Millia Islamia, New Delhi, India
2 Department of Computer Science and Engineering, Jamia Hamdard, New Delhi, India
3 Department of Orthopaedics, Indraprastha Apollo Hospital, New Delhi, India
|Date of Submission||06-Feb-2020|
|Date of Acceptance||18-Feb-2020|
|Date of Web Publication||17-Mar-2020|
Department of Orthopaedics, Indraprastha Apollo Hospital, Sarita Vihar, Mathura Road, New Delhi - 110 076
Source of Support: None, Conflict of Interest: None
Medical profession requires extensive knowledge and accuracy from the existing data for improved decision-making. Artificial intelligence (AI) is an appropriate technology used to improve the knowledge, skill, quality of treatment, capability, confidence, and effective decision-making process. It has the capability to revolutionize the healthcare industry. It can identify high-risk patients and their associated treatments and can help train doctors for the understanding of diseases and diagnostic treatment for better patient health. We discuss various advantages and limitations of AI in the context of healthcare sector. Various significant capabilities of AI for decision-making are identified and presented, and its significant capabilities for healthcare education are consolidated herewith. AI is helpful for appropriate planning, diagnosis, and associated activities, such as education, training, research, and development of healthcare. This technology provides some excellent capabilities to notice the changes and predict the disease of the patient. AI is now being applied for developing personal health history, and industry is contemplating a great potential for its implementation. However, cost and privacy issues are yet to be taken care of.
Keywords: Artificial intelligence, capabilities, decision-making, education, healthcare
|How to cite this article:|
Javaid M, Haleem A, Khan IH, Vaishya R, Vaish A. Extending capabilities of artificial intelligence for decision-making and healthcare education. Apollo Med 2020;17:53-9
|How to cite this URL:|
Javaid M, Haleem A, Khan IH, Vaishya R, Vaish A. Extending capabilities of artificial intelligence for decision-making and healthcare education. Apollo Med [serial online] 2020 [cited 2020 Jul 4];17:53-9. Available from: http://www.apollomedicine.org/text.asp?2020/17/1/53/280907
| Introduction|| |
Artificial intelligence (AI) with proper training can perform a task like a human mind. In healthcare, AI is becoming popular due to its capability of digitalization. Doctors can store patient data digitally, which can further help them to access better treatment in the future. It increases the potential of a physician and hospital staff. This technology performed the treatment option of diseases such as tumors and genetic abnormalities. Doctors can achieve informed clinical decisions of diseases. It generates the data and predicts the outcome of the patient as it automatically analyzes a large amount of patient data. Thus, it can improve the healthcare system through a new way of research and development.,
By the appropriate information, AI prevents the readmission of the patient in the hospital. It automatically follows up the unpaid bills and other management-related information related to hospital administration. AI helps to flow health information and improve the accuracy of clinical data. It is beneficial to perform new types of therapies accurately. This easily undertakes the complex problem, which is challenging for humans.,
Now, it is applied in every field of medicine such as the development of the drug, planning of personalized treatment, and predictions of outcomes. The unstructured data are also used for better health prediction.,,
AI technology is effectively used to detect diabetes, blood pressure, and its treatment in lesser time and cost. It easily communicates the disease process with the help of available data and recommends the treatment. This technology reduces the burden of physicians by reminding them about daily tasks. Doctors can achieve better medical knowledge, practice-based learning, communication skills, patient care, and improvement. It easily overlooks the training to medical students with the help of electronic health records. Patient safety can be improved by proper implementation of this technology.,,
In healthcare, AI is applicable to create transparency of information and treatment. It monitors the heart rate and all medical, social, and test history of the patient. AI is also efficient to store and analyze the data of public health. It removes the burdens of maintaining public health data. This technology provides greater confidence for a surgeon to perform the surgery successfully.,
| What is Artificial Intelligence?|| |
AI is a branch of computer science that uses smart machines having human-like intelligence to perform the required task. This creates advancement in every sector and industry for various approaches. This technology creates smart manufacturing, assistant, personalized recommendation, monitoring of dangerous tools, better marketing, and customer service., Machines are programmed in a way that have the capability to mimic their actions. It has great potential for learning, reasoning, perception, and solving of a complex problem. In healthcare, this technology is to use complex algorithms and software for the analysis of complex medical data., It has great capability to gain information through machine learning algorithms. This technology easily analyzes the technique of treatment which can further help improve patient outcomes.
| Research Objectives|| |
AI is used for the development of innovative medical procedures by making the human brain more powerful. This paper focuses on the capabilities of this technology for decision-making and the enhancement of healthcare education. It is helpful to educate and demonstrate medical students with different options for disease diagnosis and treatments. AI is efficient to generate an accurate result with the help of proper information. It minimizes the ongoing errors in the healthcare industry by digital capturing of data. This technology is helpful for proper heartbeat monitoring; analysis of computed tomography, X-ray, and magnetic resonance imaging scan; and proper assistance of exercise. It creates revolutionary changes of various anomalies by providing better picture, which is not detected by human eyes. It helps to save confidential information which causes harm for the patient., Various frauds and mistakes in the treatment are easily detected. The main objective of this study is to discuss the significant advantages and limitations of AI. Paper briefs the capabilities of this technology for effective decision-making and healthcare education.
| Advantages of Artificial Intelligence|| |
AI has a great capability to store medical records, pharmacy notes, analyze medical information, and analyze environmental data. It is useful for augmenting radiology and minimally invasive surgery, decreasing medical cost, and reducing mortality rates. Physicians and diagnostic experts quickly and accurately handle the large medical problem., It automates the standardized workflows for complex diagnostics and precision medicine. Major advantages of AI are as follows:
- Risk capability
- Available anytime
- Digitally controlling
- Work in a hazardous environment
- Improve security
- Reduce errors and risk
- Increase productivity
- Repetitive jobs
- Make a fast and accurate decision
- Easily handle the low- and high-level task
- Create innovation
- Rapid communication
- Create a rapid process of learning.
| Limitations of Artificial Intelligence|| |
AI has thinking capability with the help of intelligent machines. However, there are also some limitations of this technology which are as follows:
- High cost of technology
- Requirement of costly manpower
- Machine does as per the training
- Perceived unemployment
- No emotion
- Lack of creative ideas
- Not capable of thinking out of box
- Not improve with experience
- Create a high dependency on machines
- Lack of handling of intelligent task.
| Capabilities of Artificial Intelligence for Decision-Making|| |
Scientists used this technology for effective decision-making and important predictions. By the applications of AI, the physician can easily manage data to make an informed decision; thus, technology helps medical devices to increase their performance. It is helpful to understand the ongoing treatment principle and apply the latest developments., This technology can detect new diseases/problems from the available information and suggest improvements. [Table 1] discusses the significant capabilities of AI for decision-making.
|Table 1: Major capabilities of artificial intelligence for decision-making|
Click here to view
This technology is used to decide mistakes/errors in the medical image/data. It appropriately manages and addresses the proper solution for the hospital management system. This technology has great potential for the treatment options for various medical problems. It is used for a complex decision from the available patient data. This identifies the risk of daily planning and treatment. It properly monitors the health changes and provides an advance approach for successful surgery. This technology is used to increase the transparency of the whole process with the help of an intelligent monitoring system.
| Capabilities of Artificial Intelligence for Healthcare Education|| |
In healthcare, AI is used to improve the clinical reliability with the help of a proper information system. It helps to reduce the mortality rate by reducing errors in complex treatment. This technology addresses the challenge of the healthcare industry to perform specific tasks., It is also used for the diagnosis of skin cancer. [Table 2] discusses the major capabilities that can be developed through AI for healthcare education.
|Table 2: Major capabilities of artificial intelligence for healthcare education|
Click here to view
AI is an innovative technology used to provide a successful learning process in healthcare. New doctors can learn the better procedure of treatment and all biological systems. This technology is used successfully to identify the symptoms of cancer and other abnormalities, such as blockage of heart valves, fractures, and trauma. It has wide applications to develop new medicine and drug for better health. AI is useful for planning and facilitates therapy. In the future, this technology will create more advancement in the teaching and learning process.
| Major Contribution of the Study|| |
AI provides quality planning, treatment, decision, and education. It can understand the human language to convey valuable information. The great capability of this technology is a clinical workflow and proper management system. The major contributions of this study are as follows:
- AI uses smart machines to perform the required task and makes an efficient decision in healthcare
- This technology uses complex algorithms to analyze complex medical data for beneficial outcomes
- The major benefits of this technology are digital controlling of healthcare, increased productivity, efficiency, security, and available anytime in a hazardous environment
- It has also some limitations such as high cost of technology, unemployment, no emotion, lack of creative ideas, create high dependency on machines, and lack to handle the intelligent task
- The major capabilities of this technology for decision-making are highly complex decisions, analysis of complex patient's data, treatment decision, surgical decision, training decision, clinical decision, and emergency decisions
- In healthcare education, AI has wide applications for the learning of personalized treatment, better learning about cancer and radiology diagnosis, development of new medicine, automation of the healthcare industry, facilitating therapy, safety of the patient, improving learning capabilities, valuable information, improving clinical workflow, and development of teaching tools
- In the future, this technology will create more advancement in healthcare and may fulfill various challenges.
| Future Scope|| |
In the future, AI will bring major changes in the procedure of surgery. It can provide robot-assisted surgery and become helpful to strengthen the medical workforce. The major role will also be played for teaching and training of new diseases with the help of available digital data. AI assists powerful surgical techniques applied to make successful surgery. It assures the patient for quality treatment, which increases the confidence during the entire process. The diagnoses of various diseases are made based on patient symptoms. It predicts future disease with the help of accessible information. This technology will create new advancements to improve human health and address various impossible challenges. It will save the time of the clinician by automatically check the sugar level, blood pressure, and blood test. AI will prepare a various important risks and their actions to better patient health. In the future, this technology will seem an efficient approach for the proper decision-making process in lesser time.
| Conclusion|| |
AI is used to provide multiple medications by the proper implementation of algorithms. The major capabilities of this technology applied for an effective clinical decision support system, planning, and education. It is used for better treatment plan and prevention of disease to provide positive outcomes. AI is an innovative technology used for blood group detection, image diagnosis, and many other operations by opens a new way of automation. The technology conveys proper information and provides proper hospital management system. It can virtually assist all health records for better and advance research. Its applications are further applied for the improvement of clinical documentation and other related support. This helps to make a chart of patient data for the clinical program. It is used for personalized pharmaceutical and drug discovery development. This handles all administrative tasks easily such as assigning of wards, proper scheduling of doctors, billing, and another relevant task. It efficiently fulfills various innovative challenges in healthcare. In the future, doctors will use this technology for proper decision-making and effective teaching and learning processes.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Dankwa-Mullan I, Rivo M, Sepulveda M, Park Y, Snowdon J, Rhee K. Transforming diabetes care through artificial intelligence: The future is here. Popul Health Manag 2019;22:229-42.
Haleem A, Vaishya R, Javaid M, Khan IH. Artificial intelligence (AI) applications in orthopaedics: An innovative technology to embrace. J Clin Orthop Trauma 2020;11:S80-1.
Buch VH, Ahmed I, Maruthappu M. Artificial intelligence in medicine: Current trends and future possibilities. Br J Gen Pract 2018;68:143-4.
Danysz K, Cicirello S, Mingle E, Assuncao B, Tetarenko N, Mockute R, et al
. Artificial intelligence and the future of the drug safety professional. Drug Saf 2019;42:491-7.
Liao H, Si G, Xu Z, Fujita H. Hesitant fuzzy linguistic preference utility set and its application in selection of fire rescue plans. Int J Environ Res Public Health 2018;15:664.
Ahuja AS. The impact of artificial intelligence in medicine on the future role of the physician. PeerJ 2019;7:e7702.
Leicht-Deobald U, Busch T, Schank C, Weibel A, Schafheitle S, Wildhaber I, et al
. The challenges of algorithm-based HR decision-making for personal integrity. J Bus Ethics 2019;160:377-92.
Patel VL, Shortliffe EH, Stefanelli M, Szolovits P, Berthold MR, Bellazzi R, et al
. The coming of age of artificial intelligence in medicine. Artif Intell Med 2009;46:5-17.
Kerr D, Klonoff DC. Digital diabetes data and artificial intelligence: A time for humility not hubris. J Diabetes Sci Technol 2019;13:123-7.
Haleem A, Javaid M, Khan IH. Current status and applications of artificial intelligence (AI) in medical field: An overview. Curr Med Res Pract 2019;9:231-7.
VoPham T, Hart JE, Laden F, Chiang YY. Emerging trends in geospatial artificial intelligence (geoAI): Potential applications for environmental epidemiology. Environ Health 2018;17:40.
Liang XW, Cai YY, Yu JS, Liao JY, Chen ZY. Update on thyroid ultrasound: A narrative review from diagnostic criteria to artificial intelligence techniques. Chin Med J (Engl) 2019;132:1974-82.
Frank DA, Chrysochou P, Mitkidis P, Ariely D. Human decision-making biases in the moral dilemmas of autonomous vehicles. Sci Rep 2019;9:1-19.
Vuong QH, Ho MT, Vuong TT, La VP, Ho MT, Nghiem KP, et al
. Artificial intelligence vs. natural stupidity: Evaluating AI readiness for the Vietnamese medical information system. J Clin Med 2019;8:168.
Zomnir MG, Lipkin L, Pacula M, Meneses ED, MacLeay A, Duraisamy S, et al
. Artificial intelligence approach for variant reporting. JCO Clin Cancer Inform 2018;2:79.
Lysaght T, Lim HY, Xafis V, et al
. AI-assisted decision-making in healthcare. Asian Bioeth Rev 2019;11:299-314.
Pesapane F, Codari M, Sardanelli F. Artificial intelligence in medical imaging: Threat or opportunity? Radiologists again at the forefront of innovation in medicine. Eur Radiol Exp 2018;2:35.
Benke K, Benke G. Artificial intelligence and big data in public health. Int J Environ Res Public Health 2018;15:2796.
Shahid N, Rappon T, Berta W. Applications of artificial neural networks in health care organizational decision-making: A scoping review. PLoS One 2019;14:e0212356.
Kong X, Ai B, Kong Y, Su L, Ning Y, Howard N, et al
. Artificial intelligence: A key to relieve China's insufficient and unequally-distributed medical resources. Am J Transl Res 2019;11:2632-40.
Shaw J, Rudzicz F, Jamieson T, Goldfarb A. Artificial intelligence and the implementation challenge. J Med Internet Res 2019;21:e13659.
Haleem A, Vaishya R, Javaid M, Khan MI. Artificial intelligence (AI) applications in dentistry. Curr Med Res Pract 2019;12:2.
Contreras I, Vehi J. Artificial intelligence for diabetes management and decision support: Literature review. J Med Internet Res 2018;20:e10775.
Schmider J, Kumar K, LaForest C, Swankoski B, Naim K, Caubel PM. Innovation in pharmacovigilance: Use of artificial intelligence in adverse event case processing. Clin Pharmacol Ther 2019;105:954-61.
Sinčak P, Ondo J, Kaposztasova D, Virčikova M, Vranayova Z, Sabol J. Artificial intelligence in public health prevention of legionelosis in drinking water systems. Int J Environ Res Public Health 2014;11:8597-611.
Homma R, Kochi S, Niiyama T, Mihana T, Mitsui Y, Kanno K, et al
. On-chip photonic decision maker using spontaneous mode switching in a ring laser. Sci Rep 2019;9:9429.
Love-Koh J, Peel A, Rejon-Parrilla JC, Ennis K, Lovett R, Manca A, et al
. The future of precision medicine: potential impacts for health technology assessment. Pharmacoeconomics 2018;36:1439-51.
Combi C, Pozzi G. Clinical information systems and artificial intelligence: Recent research trends. Yearb Med Inform 2019;28:83-94.
Radcliffe K, Lyson HC, Barr-Walker J, Sarkar U. Collective intelligence in medical decision-making: A systematic scoping review. BMC Med Inform Decis Mak 2019;19:158.
Tenório JM, Hummel AD, Cohrs FM, Sdepanian VL, Pisa IT, de Fátima Marin H. Artificial intelligence techniques applied to the development of a decision-support system for diagnosing celiac disease. Int J Med Inform 2011;80:793-802.
Hashimoto DA, Rosman G, Rus D, Meireles OR. Artificial intelligence in surgery: Promises and perils. Ann Surg 2018;268:70-6.
Hwang DK, Hsu CC, Chang KJ, Chao D, Sun CH, Jheng YC, et al
. Artificial intelligence-based decision-making for age-related macular degeneration. Theranostics 2019;9:232-45.
Beil M, Proft I, van Heerden D, Sviri S, van Heerden PV. Ethical considerations about artificial intelligence for prognostication in intensive care. Intensive Care Med Exp 2019;7:70.
Siegersma KR, Leiner T, Chew DP, Appelman Y, Hofstra L, Verjans JW. Artificial intelligence in cardiovascular imaging: State of the art and implications for the imaging cardiologist. Neth Heart J 2019;27:403-13.
Hasegawa E, Mizumoto N, Kobayashi K, Dobata S, Yoshimura J, Watanabe S, et al
. Nature of collective decision-making by simple yes/no decision units. Sci Rep 2017;7:14436.
Lynn LA. Artificial intelligence systems for complex decision-making in acute care medicine: A review. Patient Saf Surg 2019;13:6.
Elkin PL, Schlegel DR, Anderson M, Komm J, Ficheur G, Bisson L. Artificial intelligence: Bayesian versus Heuristic method for diagnostic decision support. Appl Clin Inform 2018;9:432-9.
Kwon JM, Kim KH, Jeon KH, Lee SE, Lee HY, Cho HJ, et al
. Artificial intelligence algorithm for predicting mortality of patients with acute heart failure. PLoS One 2019;14:e0219302.
Shen J, Zhang CJ, Jiang B, Chen J, Song J, Liu Z, et al.
Artificial intelligence versus clinicians in disease diagnosis: Systematic review. JMIR Med Inform 2019;7:e10010.
Fox J, Cooper RP, Glasspool DW. A canonical theory of dynamic decision-making. Front Psychol 2013;4:150.
Amador-Domínguez E, Serrano E, Manrique D, de Paz JF. Prediction and decision-making in intelligent environments supported by knowledge graphs, a systematic review. Sensors (Basel) 2019;19:1774.
Wang R, Pan W, Jin L, Li Y, Geng Y, Gao C, et al
. Artificial intelligence in reproductive medicine. Reproduction 2019;158:R139-54.
Kulikowski CA. Beginnings of Artificial Intelligence in Medicine (AIM): Computational artifice assisting scientific inquiry and clinical art-with reflections on present AIM challenges. Yearb Med Inform 2019;28:249-56.
Rodríguez-González A, Zanin M, Menasalvas-Ruiz E. Public health and epidemiology informatics: Can artificial intelligence help future global challenges? An overview of antimicrobial resistance and impact of climate change in disease epidemiology. Yearb Med Inform 2019;28:224-31.
Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al
. Artificial intelligence in healthcare: Past, present and future. Stroke Vasc Neurol 2017;2:230-43.
Simopoulou M, Sfakianoudis K, Maziotis E, Antoniou N, Rapani A, Anifandis G, et al
. Are computational applications the “crystal ball” in the IVF laboratory? The evolution from mathematics to artificial intelligence. J Assist Reprod Genet 2018;35:1545-57.
Kuziemsky C, Maeder AJ, John O, Gogia SB, Basu A, Meher S, et al
. Role of artificial intelligence within the telehealth domain. Yearb Med Inform 2019;28:35-40.
Bonderman D. Artificial intelligence in cardiology. Wien Klin Wochenschr 2017;129:866-8.
Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer 2018;18:500-10.
Tran BX, Latkin CA, Sharafeldin N, Nguyen K, Vu GT, Tam WW, et al
. Characterizing artificial intelligence applications in cancer research: A latent Dirichlet allocation analysis. JMIR Med Inform 2019;7:e14401.
Chen X, Liu Z, Wei L, Yan J, Hao T, Ding R. A comparative quantitative study of utilizing artificial intelligence on electronic health records in the USA and China during 2008-2017. BMC Med Inform Decis Mak 2018;18:117.
Rigla M, García-Sáez G, Pons B, Hernando ME. Artificial intelligence methodologies and their application to diabetes. J Diabetes Sci Technol 2018;12:303-10.
Meskó B. The real era of the art of medicine begins with artificial intelligence. J Med Internet Res 2019;21:e16295.
Park SH, Do KH, Kim S, Park JH, Lim YS. What should medical students know about artificial intelligence in medicine? J Educ Eval Health Prof 2019;16:18.
Popenici SA, Kerr S. Exploring the impact of artificial intelligence on teaching and learning in higher education. Res Pract Technol Enhanc Learn 2017;12:22.
Fellous JM, Sapiro G, Rossi A, Mayberg H, Ferrante M. Explainable artificial intelligence for neuroscience: Behavioral neurostimulation. Front Neurosci 2019;13:1346.
Hueso M, Vellido A, Montero N, Barbieri C, Ramos R, Angoso M, et al
. Artificial intelligence for the artificial kidney: Pointers to the future of a personalized hemodialysis therapy. Kidney Dis (Basel) 2018;4:1-9.
Claudino JG, Capanema DO, de Souza TV, Serrão JC, Machado Pereira AC, Nassis GP. Current approaches to the use of artificial intelligence for injury risk assessment and performance prediction in team sports: A systematic review. Sports Med Open 2019;5:28.
Paranjape K, Schinkel M, Nannan Panday R, Car J, Nanayakkara P. Introducing artificial intelligence training in medical education. JMIR Med Educ 2019;5:e16048.
Chan KS, Zary N. Applications and challenges of implementing artificial intelligence in medical education: Integrative review. JMIR Med Educ 2019;5:e13930.
van Gerven M. Computational foundations of natural intelligence. Front Comput Neurosci 2017;11:112.
Han ER, Yeo S, Kim MJ, Lee YH, Park KH, Roh H. Medical education trends for future physicians in the era of advanced technology and artificial intelligence: An integrative review. BMC Med Educ 2019;19:460.
Joshi RS, Haddad AF, Lau D, Ames CP. Artificial intelligence for adult spinal Deformity. Neurospine 2019;16:686-94.
Hu Y, Loo CK. A generalized quantum-inspired decision making model for intelligent agent. ScientificWorldJournal 2014;2014:240983.
Kordzadeh A, Esfahlani SS. The role of artificial intelligence in the prediction of functional maturation of arteriovenous fistula. Ann Vasc Dis 2019;12:44-9.
Panch T, Szolovits P, Atun R. Artificial intelligence, machine learning and health systems. J Glob Health 2018;8:020303.
Galbusera F, Casaroli G, Bassani T. Artificial intelligence and machine learning in spine research. JOR Spine 2019;2:e1044.
Yang K, Zeng Z, Peng H, Jiang Y. Attitudes of Chinese cancer patients toward the clinical use of artificial intelligence. Patient Prefer Adherence 2019;13:1867-75.
Stein N, Brooks K. A fully automated conversational artificial intelligence for weight loss: Longitudinal observational study among overweight and obese adults. JMIR Diabetes 2017;2:e28.
Ye T, Xue J, He M, Gu J, Lin H, Xu B, et al
. Psychosocial factors affecting artificial intelligence adoption in health care in China: Cross-sectional study. J Med Internet Res 2019;21:e14316.
Nebeker C, Torous J, Bartlett Ellis RJ. Building the case for actionable ethics in digital health research supported by artificial intelligence. BMC Med 2019;17:137.
[Table 1], [Table 2]