Impact of Expert Systems on Industrial Growth - Essay Example

?Expert Systems: Impact of Expert Systems on Industrial Growth By Table of Contents Table of Contents 2 References 15 Introduction At thepresent, technology is playing a significant role in every walk of life. It has offered wonderful techniques which allow organizations to carry out their daily tasks smoothly. In this scenario, this paper presents an overview of expert systems. An expert system is believed to be an information system or decision support system but actually it is different from these systems. As its name indicates, expert systems are used to perform specific tasks which involve the knowledge and skills of experts. This paper will discuss the role of expert systems in two different sectors. This paper is divided into three sections. In the first section I will present an overview of the expert system generally, in the second section I will discuss the impact of expert systems on the banking sector. In this scenario, I will discuss the advantages of expert systems with respect to the banking industry. This section will also contain some of the well known examples of applications of expert systems in the banking sector. In the third section I will discuss the impact of expert systems on the medical/healthcare sector. In this scenario, I will discuss the advantages of expert systems with respect to healthcare sector. This section will also contain some of the well known examples of applications of expert systems in the healthcare sector. Expert Systems: An Overview Although Artificial Intelligence (AI) has been around for more than fifty years, it has been just recently that a lot of organizations all over the world are beginning to utilize AI based tools and applications to help them become more competitive in the ever-increasing competitive world. In the past few years, AI has been rapidly turning into an imperative technology and there is at this time an explosion of interest centering on this field. In fact, both industries and educational institutes are assigning more resources than ever before to AI. Basically, the artificial intelligence is a wide-ranging trend and it consists of a lot of sub domains such as game-playing systems, vision systems, computer-aided instruction, natural language translation, voice synthesis and recognition, robotics, and expert systems. Expert systems are possibly the fastest progressing sub domain of AI (Schon & Helferich, 1989). Expert systems are acknowledged as a significant subject of artificial intelligence. Basically, an expert system offers a method to collect and transform the knowledge of experts. The expert system consists of computer programs that try to be like the way people think. The history of expert systems can be traced back to over twenty years ago in the labs of Stanford University where it was first used to help make a diagnosis of infectious blood diseases. In fact, since that time expert systems have been using into almost every walk of life that involves human knowledge and judgment. In addition, the expert systems are normally based on three most important elements: the clarification generator and user interface, the inference engine, and the knowledge base. Additionally, in the beginning while using expert systems at Stanford, the knowledge base encompassed medical "rules" to demonstrate IF-THEN conditions and statements with a related confidence factor. The example of this rule can be like this, IF the patient is diagnosed with symptom A AND symptom B THEN the result/disease is X, and confidence is Y%. If the patient is diagnosed with symptom C, then this rule would not even be applicable. Moreover, the decision regarding the selection of the rules is made automatically by the inference engine (MoreBusiness, 1998). In simple words, “an expert system is an information system that collects and stores the expertise of human experts and then reproduces human reasoning and decision making”. Furthermore, an expert system can be defined as a knowledge exhaustive computer program that captures the skills or expertise of human in limited fields of knowledge. An expert system can help decision making by creating related questions and describing the causes for taking definite action (Shelly et al., 2005, p.729; Laudon & Laudon, 1999, p.446). Advantages of Expert Systems As discussed above, expert systems can be used to store useful expert knowledge and make it archival. It is a significant advantage for the organizations, especially when they feel they can lose the experts would be a considerable loss to the organization. On the other hand, making use of the expert knowledge improves employee efficiency by providing the required support to make the best decision. In addition, enhancements in consistency and performance normally come into view when expert systems give out expert advice, judgment, and clarification on demand. It is an admitted fact that expert systems can carry out extremely complicated activities and tasks and a tremendously rich knowledge-database arrangement and comfortable. In this way, they are well-matched and compatible to copy human problems and activities. Additionally, expert systems can decrease production downtime and, consequently, enhance efficiency and performance. Moreover, expert systems make easy the distribution of expertise to distant locations by making use of digital communication channels (such as the Internet). Furthermore, in many cases, continuing use of an expert system would be cheaper and more reliable than the services of a human expert (ReferenceForBusiness, 2012). According to (Sagheb-Tehrani, 1993), organizations can use expert systems to get a wide variety of advantages, for instance minimizing expenditures, organizational changes, to improve/aid decision making and improving business efficiency. The first advantage is about minimizing expenses, when expert systems replace with human expertise then the need for human resource is decreased. Additionally, the implementation of an expert system usually causes organizational changes in reporting arrangement, job content, and so on. In fact, it can also require human resources learning a wide variety of techniques for carrying out their tasks. On the other hand, decreasing job numbers because of implementation of an expert system engages some degree of organizational changes. One of the most important benefits of expert system is improving organizational efficiency. It is because of the fact that expert systems allow the organizations to make effective and timely decisions which can result in higher quality and quantity in productions. In view of the fact that expert systems are giving the assurance for improved efficiency, thus organizations must keep in mind its consequences for mankind and the features of their work atmosphere. However, it can have need of active management response, which can go deeper than just the obligation for user participation. Moreover, the innovative environment of this technology requires a deeper knowledge and understanding of its design and the environment in which this technology will be implemented (Sagheb-Tehrani, 1993), Disadvantages of Expert Systems Despite a number of advantages, expert systems have some limitations as well. Some of the limitations of expert systems are outlined below: (Learners House, 2012; Liberopoulou, 2006) Solving a problem using an expert system can be more time-consuming than experts. Expert Systems are not generalized problem solvers or experts. Some of the problems cannot be resolved by an expert system. It is not for all time feasible to take out knowledge from an expert. In fact, it takes a lot of time and effort to develop an expert system. It is a very complex task to make the cost-benefit analysis of by making use of an expert system. Data and information to be collected for the expert system may not for all time be readily accessible. Different experts can have different approaches to solve a single problem. There is no technique to verify that whether the results of expert systems are logical or correct. Impact of Expert Systems on Banking The advancements of information technology have enhanced the scope and scale of financial growth and are having a deep impact on the performance and tasks of all the banking organizations. In fact, banking organizations are always looking for modern tools and techniques to make effective use of information technology in an attempt to offer an ever-increasing number of products and services to their customers. Artificial intelligence has resolved a lot of issues which banking organizations had been facing in the past. In addition, expert systems stand for one of the most important areas in the area of artificial intelligence which have established a strategic and promising position. Thus, keeping in mind the numerous advantages of expert systems for the banking sector, the majority of banking organizations have started implementing or looking into expert systems for profitable application (Canadian Banker, 1992). In this scenario, an excellent instance of application of expert systems in banking organizations is expert systems for mortgages. Actually, loan departments of banks are interested in expert systems for mortgages due to the always increasing expenses of workers which make the management and approval of comparatively small loans less gainful. Moreover, the banking organizations also observe in the adoption of expert systems an opportunity for consistent, well-organized control over mortgage loan, and value that for the approval of mortgages there are rigid regulations which do not for all time exist with other kinds of loans (Loteyro, 2008). A lot of researches have been carried out to determine the impact of experts systems on banking sector with the majority of reports of applications in the USA, Europe and Japan. In these countries, the majority of banking organizations have been using expert systems for credit evaluation, portfolio management and assessment, risk analysis and management, assessment of personal and corporate loans and treasury operations. In addition, some of the important advantages of expert systems in the banking sector have been recognized as: automation of routine decision-making tasks, which frees managers to pursue more challenging problems; disconnection of decision making expertise from human decision makers, facilitating new managers to make use of the same knowledge and judgment as familiar experts and better harmonization and authority of the decision making practice, with the intention that changes in business strategy can be rapidly and professionally communicated all the way through large organizations (Shao et al., 1995). Another most important benefit of expert systems is that besides quantitative ratios such as productivity, overall earning and profit, long-standing and temporary dept and other, they can deal with qualitative data. They can help banking organizations position at the market, business reputation, marketing flexibility, organization of personnel, etc. In addition, effective monetary investigation allows the banking organizations to determine the bank’s position, to be precise, an intensity of risks for a potential investment or a credit approval. Moreover, the expert systems are extensively used in educating and training managers and other banking staff members. In view of the facts that this kind of knowledge can increase and be improved quickly, thus the knowledge bases of such expert systems are appropriate resources for its management, maintenance, enhancement and reliability (Nedovic & Devedzic, 2002). As discussed above, there are numerous examples of successful applications of expert systems in the banking sector. In this scenario, (Shao et al., 1995) discuss some of the well known examples of banks which have successfully implemented expert systems to automate their business processes. The first example that (Shao et al., 1995) discuss is about The Midland Bank, which considers expert system as an essential element of its information technology operations as well as various experts systems have been implemented in a number of business areas. For instance, it uses a credit card authorization system to determine the maximum limit of the card. It also checks transaction patterns of cards. In fact, this system has been used since 1984. This system is named as a credit assessment system (CAS) and it was created by a credit and risk systems manager along with two other credit experts. If puts through a sieve loan application and renewals, setting the credit approval levels with the availability of data and information stored in its database of all corporate customers, it then determines each customer’s financial statement, examines the financial statement percentages and provides the customer with a credit score varying from 0 (very bad) to 100 (very good) (Shao et al., 1995). In addition, the final decision on whether or not to approve the application then depends on an authorized officer. Historically, the initial prototype of CAS was prepared in November 1987, and in March 1988 main systems were actually implemented at corporate banking centers in Leicester and Coventry, functioning together with the manual or paper-based system with the intention of checking on deficiencies and flaws in the systems. In September 1988, these systems were fixed and the live systems were implemented. And by December 1998 all the bank’s corporate banking centers started utilizing these systems (Shao et al., 1995). In the same way (Sahin & Sawyer, 1987) discuss an example of The CitiExpert system, which was designed and implemented in order to improve the efficiency of a wide variety of banking operations such as managing the labor-intensive area and funds transfer telex processing. In this scenario, text telexes written in the English Language are read by bank officials, as well as significant data and information in the telex is entered manually by using a data entry interface. However, it is essential for the banking professionals that they enter this data and information consistent with both wide-ranging banking standards and appropriate formatting principles. In addition, due to the bank's need to process English text input and to integrate a significant amount of specific knowledge and expertise, customary programming techniques were insufficient. Artificial intelligence technology was recognized as the suitable solution. Thus, to deal with these issues, artificial intelligence offers two groups of techniques which are used by CitiExpert: rule-based expert system techniques and natural language processing techniques. Both these techniques were used in the development of this expert system (Sahin & Sawyer, 1987). Impact of Expert Systems on Healthcare Sector There are many applications of expert systems ranging from medicine, accounting, process control of human resources, financial services etc. (Khan et al., 2008). Up till now we have discussed the impact of expert systems on banking industry. In this section I will discuss the impact of expert systems on medical/health sector. In this scenario, I will discuss how an expert system is used in the healthcare sector, what are the new developments in this sector with respect to expert systems. Similar to other applications of expert systems in other industries the applications of expert systems in health sector engage forecasting and making a diagnosis regarding a specific disease. On the other hand, at the present, the implementations of expert systems are seen in a lot of other healthcare firms in forms of therapy, disease prevention, and treatment of the patient after therapy etc. Additionally, the expert systems are employed by the medical institutes to teach the medical students on a wide variety of medical aspects. In addition, expert systems in the healthcare sector are also helpful in definite conditions no matter what is the case, whether it is very complicated or there is no healthcare professional willingly available for patients. Moreover, since the emergence the key barrier of making use of expert systems in the healthcare sector has been the accurateness of such systems. In view of the fact that the field of medicine is such a complicated and difficult thus the safety is for all time a key concern. On the other hand, still these expert systems provide an excellent support in places where there is a lack of medical experts. Some of the well known examples of medical expert systems include MYCIN, PUFF, DXplain, HELP, etc. (Khan et al., 2008). At the present, the majority of large size heath care organizations are making use of medical expert systems to provide their health care professionals with both structured queries and structured responses within the medical field of specific knowledge or expertise. Basically, the structure is integrated by means of a computer program on the recommendation of one or more healthcare professionals, who are also responsible for suggesting the most favorable queries to think about, as well as offer the most precise results to be extracted from the responses the healthcare professionals select. Similar to all other expert systems, medical expert systems as well use software programs to present sequence structure in clauses of the form: "If (condition), Then (do this), Else (do this)", where last else normally contains the positive value in the closed system of the program. Though, the healthcare professionals can choose any one of the alternatives presented in against each section, however, the healthcare professionals cannot choose that is outside the limits that are given by the expert while writing the program. In this scenario, the functionality of this program is restricted by the fixed input from the expert at the specific time of development. In case, healthcare professionals want to add or update new queries or new data, these medical expert system programs will not be able to accommodate the healthcare professionals (Frenster, 1989). According to (Kinney, 1991), medical expert systems are computer programs that, when well-designed and implemented, provide excellent decision support in the form of correct diagnostic information or, at least, recommend treatment or prediction. In this scenario, diagnostic, therapeutic, or prognostic instructions are provided after the program takes information (input) about the patient; normally this task is performed by the healthcare professional. Many people believe that an expert system is medical software but expert systems have certain features which make them different from other types of medical software. In this scenario, the most important characteristic is the sequence of steps that are performed by the expert systems while suggesting a therapeutic or diagnostic result frequently is intended to imitate the medical way of thinking (Kinney, 1991). Moreover, it is normally believed that the expert system program is based on a large number of details, facts and rules regarding a disease or clause in the query with the intention of delivering correct responses. In fact, some researchers agree that two wide-ranging internal medicine textbooks as well as three specialty textbooks would require more than 2 million facts and rules for the reason that large amounts of data are required. In addition, in the past, expert systems were normally used with huge and costly computers. However, with the rapid advancements of technology and well-organized microcomputer languages, expert systems can be used now by any healthcare professional with a microcomputer. Additionally, it has been recommended that expert systems cannot replicate the medical approach this idea fails to notice the detail that some expert systems follow rules and facts. Another limitation of expert systems with respect to medical sector is that they require constant updating and maintenance and as a result the survival of a large expert system can correspond to a very long-term promise on the part of any institution. In short, healthcare professionals with an interest in expert systems should be familiar with that there are a lot of opportunities to look at them through the ever-increasing number of conferences and publications dedicated to all features of health sector and computing, together with medical expert systems (Kinney, 1991). Conclusion At the present, technology is playing a significant role in every walk of life. It has offered wonderful techniques which allow organizations to carry out their daily tasks smoothly. In this scenario, this paper has presented an overview of expert systems. An expert system is believed to be an information system or decision support system but actually it is different from these systems. This paper has discussed the concept of expert system with respect to two different sectors: medical and banking. This paper has summarized the ideas of different researchers. Expert systems are playing a significant role in both the sectors but their successful survival in both the sectors requires regular updates and maintenance. References Canadian Banker, 1992. Expert systems in banking. [Online] Available at: http://www.accessmylibrary.com/article-1G1-12713281/expert-systems-banking.html [Accessed 30 April 2012]. Frenster, J.H., 1989. EXPERT SYSTEMS AND OPEN SYSTEMS IN MEDICAL ARTIFICIAL INTELLIGENCE. In Congress on Medical Informatics. San Francisco, California, 1989. Proc Am Assoc Medical Systems and Informatics. Khan, F.S. et al., 2008. The Role of Medical Expert Systems in Pakistan. World Academy of Science, Engineering and Technology, 37(2008), pp.280-82. Kinney, E.L., 1991. Medical expert systems. Who needs them? Chest, 1(3), pp.3-4. Laudon, K.C. & Laudon, J.P., 1999. 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