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Artificial Intelligence in Healthcare

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Published: Wed, 04 Oct 2017

ABSTRACT

Artificial Intelligence (AI) is the intelligence exhibits by machines. AI enable machines to think and solve problems somehow human-like and act or perform in human-like manner. AI is incomparable with human intelligence. However, AI can be implemented in humans’ daily lives to aid them with complicated tasks. One of the human intelligence issues is having barriers in performing good decision making. The aspect in decision making is also applied in a few AI field such as healthcare and robotics which will be discussed further in this paper.

INTRODUCTION

Technology is emerging day by day where people are hunger for more sophisticated technology to aid them or give them new perspectives or knowledge. Artificial Intelligence or commonly abbreviated as AI is the intelligence shown by machines or software, which usually involves human-like intelligence. It has become an academic field of study that focused on creating intelligence. The term “Artificial Intelligence” was coined by late John McCarthy of Stanford University in 1956 and after two years, he published his paper which regarded by many as the first one on logical AI (Bogue, 2014). Alan Turing, a British mathematician, cryptanalyst, computer scientist and biologist, proposed a test called Turing test to determine the machine’s ability to displays intelligence. The test requires a human judge to have natural conversations with a human and a machine that is designed to produce human-like performance. If the judge is failed to distinguish which one is human and which one is machine, the machine is considered showing intelligence.

CASE STUDY

HUMAN INTELLIGENCE IN DECISION MAKING

Human intelligence is considered as the most powerful tools in decision making. Definition of human intelligence is that a person has the intellectual capacity of a human, which characterized by perception, consciousness, self-awareness as well as volition. Through their intelligence, humans possess a cognitive ability to learn, form concepts, understand, apply logic and reason. The abilities also include the capacities to recognize patterns, comprehend ideas, plan, solve problem, make decisions, retain and use language to communicate. Intelligence enables humans to experience and think, while decision making can be viewed as cognitive methodology used to determine a conviction or a blueprint among a few options of conceivable outcomes. Each decision settling on procedure delivers a last decision that could possibly provoke activity. Choice or decision making is the investigation of recognizing and picking choices focused around the qualities and inclination of the chief. Decision making is one of the focal exercise of administration and an immense piece of any methodology of usage.

ARTIFICIAL INTELLIGENCE IN ROBOTICS

Application of AI as the most significant and exciting field in robotic development had been argued by many industrial commentators. AI technology has the potential to play a role in a diversity of robots including companion and caring robots such as autonomous land, sea and air vehicles, humanoid types, search and rescue robots, swarm robots, military robots and robotic toys. The element of AI have a role to play for instance dexterous manipulation, autonomous navigation, machine vision, speech recognition, pattern recognition and location and mapping (Bogue, 2014). Humanoid robots and autonomous, mobile robots are two field of robotic that represent the greatest number of AI concept. Honda’s Asimo, humanoid robot is a result of two decades of research in humanoid robotics by Honda engineers. Asimo has the ability to recognize moving objects, gestures, postures, sounds, faces and interact in a human-like manner.

http://asimo.honda.com/ASIMO_DCTM/News/images/highres/Meet_ASIMO.jpg

Figure 1 Honda’s Asimo

The purpose of developing robotic vehicles and autonomous mobile robots is to conduct specific tasks such as search and rescue operations. A robotic vehicle called “Stanley” is developed in 2005 at Stanford University has won the Defense Advances Research Projects Agency (DARPA) Grand Challenge by driving autonomously for 131 miles along a trail that the vehicle never gone through before (Bogue, 2014). In the conference of the Robotic Industries Association (RIA) in November 2006, John Felice, VP Manufacturing Technology and Global Enterprise, Chrysler Group discuss the manufacturing challenge facing Chrysler. Reducing costs while remain competitive in the business is the obvious challenge. However, the main problem arises from the increasing number of car model and the frequency of model changes. These changeovers are time consuming and could cost millions of dollars. John Felice proposed that robotic is the key to solve the problem (Wilson, 2006). Major companies should enhance their research team to applied AI element in industrial robotics.

ARTIFICIAL INTELLIGENCE IN HEALTHCARE

The advancement in machine engineering has swayed the scientists to create programming with the purpose of aiding specialists in settling on choice without counseling the authorities specifically. The software development misuses the capability of human brainpower, for example, reasoning, making choice, adapting (by encountering) and numerous others. AI is not a new idea, yet it has been acknowledged as an issue innovation in software engineering. It has been connected in numerous ranges, for example, instruction, business, therapeutic and assembling. In most creating nation’s deficient of medicinal pro has built the mortality of patients, experienced different infections. The deficient of restorative pros will never be overcome inside a brief time of time. The establishments of higher learning could be that as it may, make a prompt move to deliver whatever number specialists as would be prudent. In any case, while sitting tight for understudies to end up specialists and the specialists to wind up experts, numerous patients may already die. Current practice for restorative treatment obliged patients to counsel master for further analysis and treatment. Other therapeutic specialist might not have enough mastery or experience to manage certain high-hazard sicknesses. In any case, the delayed period for medicines typically takes a couple of days, weeks or even months. When the patients see the specialist, the ailments may have officially spread out. As the greater part of the high-hazard sickness could just be cured at the early stage, the patients may need to languish over whatever remains of their life (Ishak & Siraj, n.d).

Machine program known as Medical Decision-Support System was intended to help well-being experts settle on clinical decision (Shortliffe, 1987). The framework manages medicinal information and learning area in diagnosing patients’ conditions and suggesting suitable medicines for the specific patients. Patient-Centered Health Information Systems is a patient focused restorative data framework created to aid checking, overseeing and decipher understanding’s medical history (Szolovits et al., 1994). Likewise the system gives support to patient and therapeutic specialist. The system serves to enhance the quality of medical choice making, builds patient consistence and minimizes iatrogenic illness and medical errors. In medical, communication is critical as new data or new revelation is the key for the future survival (Shortliffe et al., 2000). In expansion, communication helps specialists sharing their insight or expertise (Detmer and Shortliffe, 1997). As an example, a pro from Sydney can give on-line therapeutic aid to specialist at Kuala Lumpur who is treating a patient that suffers from serious cancer problem. An alternate specialist from other nation, for example, United Kingdom can impart his experience managing the same cases. Communication between specialists or expert from other area helps specialist at Kuala Lumpur diagnosing his patient and gives appropriate treatment.

Figure 2 Example of communication between specialists (Information Sharing)

For example, AI is implemented in Healthcare is Remote Monitoring Of High-Risk Patients Using Artificial Intelligence by using strategy and framework for remote monitoring of high-risk patients using artificial intelligence. A majority of high risk patients can be at the same time checked without patient intercession. A patient hears questions in the specialist’s voice at each monitoring encounter and responds. The patient’s reactions are recorded at a remote focal monitoring station and can be examined on line or later (Langen, Katz, Dempsey, & Pompano, 1993). Artificial intelligence (AI) and voice technology (DECvoice) are consolidated to present to the patient, during an observing session or experience, questions which would be chosen from a majority of distinctive recorded inquiries. Inquiries to the patient are picked utilizing AI, in light of the patient’s reaction, by parsing. The screen could take a few structures, for example, for e.g., uterine action strips, glucometers, blood pressure cuffs, pulse monitors, electroencephalographs, and so forth. Four phone lines are committed to every patient, one for the screen, one for the voice, one as a backup and one to sense failures. Dual tone matrix frequency signals (DTMF) may be utilized for transmission of checked signs and other data which can be perceived by Decvoice, which is yet one sample of the voice engineering which can be utilized (Langen et al., 1993). The Artificial Intelligence framework is determined by an easy to utilize Natural Language interface which guides the Voice framework to send (“speak”) appropriate questions, perceive (“listen for”) the patient’s answers, update the patient’s database, direct the telephone-patient monitoring, and advise the HMO office regarding discriminating patient conditions. The data obtained from the patient calls is accessible to the therapeutic specialist on both a real-time basis when the calls are being made, or on an ad-hoc basis after the calls are logged (Langen et al., 1993).

Figure 3 Example Remote Monitoring Of High-Risk Patients

PROS IN BOTH ARTIFICIAL INTELLIGENCE AND HUMAN INTELLIGENCE

Experts and scientists are eager in making machines which can copy humans’ intelligence. Somehow, AI shows undeniably great performances, in some cases even better than a human being. AI advantageously has tireless performance by doing tasks without feeling tired, unlike human. AI also provides more logical decision-making, which is very useful in some cases. Completing task also easy as AI is like a false mind, taught to do specific jobs.

Human intelligence have barriers to get make a good decision-making. According to Dr. Edward Russo and Dr. Paul J. H. Schoemaker, a simple method have been produce to avoid the decision barriers faced by human intelligence and can be categorized into four main element. The first element is framing which is organizing the inquiry where this implies characterizing what must be chosen and deciding in preparatory way what criteria would make us incline toward one choice to an alternate. Another element is gathering intelligence by looking for bot understandable actualities and sensible evaluations of “mysterious” that we will need to settle on the choice. Third element would be coming to conclusion where sound framing and good intelligence do not guarantee a wise decision. Humans simply unable to consistently make good decisions using seat-of-the-pants judgment alone, even with excellent data in front of them. Humans need to learn from the feedback that they have acquired which is the last element for a good decision-making. Everybody needs to create a framework for gaining from the consequences of past choices. This normally means staying informed regarding what is expected to happen, intentionally guarding against serving toward self-clarifications.

CONS IN DECISION-MAKING OF HUMAN INTELLIGENCE AND ARTIFICIAL INTELLIGENCE’S PERFORMANCE

Good decisions are hard to make and there are several barriers that occurs when a person or people trying to make or find good decision. A good decision-maker must, consciously or unconsciously go through each phase of decisions making process (Westernberg, 1993). As in the aspect of human intelligence, one of the most common barrier that can interrupt brilliant decision making is plunging in. In this situation, people begin to gather information and reach conclusion without first taking a few minutes to think about the core of the issue they are facing or to think through how they believe decisions like this one should be made. People also undergo frame blindness, which is another barrier to a good-decision making. Frame blindness is where people setting out to solve the wrong problem because they have created a mental framework for their decision with little thought, which causes them to overlook the best options or lose sight of important objectives. Lack of frame control is another barrier faced by human being in decision making where they failed to consciously define the problem in more ways than one or being unduly influenced by others. Some people also tend to feel overconfidence in their judgment. This situation also could obstructed a good-decision making as people failed to collect the key factual information because they are too confidence and overly assured of their assumptions and opinions. Another obstacle faced by people in getting a good-decision making is shortsighted shortcuts, where they rely in appropriately on “rules of thumb” such as implicitly trusting the most readily available information or anchoring too much on convenient facts. When making a decision, humans have the tendency to believe that they can keep all the information they discovered straight in their heads and therefore, improvise with little preparation. They should follow a systematic procedure when making the final choice. When making decisions within a group, common thing that happens is a group failure. People in the group assume that with many smart people involved, good choices will follow automatically and this action will caused failure in managing group decision-making. Humans are likely to protect their ego causing them fooling themselves about feedback. In this case, they are failed to interpret the evidence from past outcomes for what it really says. Humans also expecting that experience will make lessons accessible naturally and they tend to neither keeping track of the consequences of their choices, nor investigating the results in ways to uncover their key lessons. Decisions process needs to audited and failure to this action means failed to create organized approach to understanding their own decision-making, so that they remain constantly exposed to all the mistakes mentioned before.

As in the matter of AI, machines have the possibility of breakdown which is disadvantageous. No matter how easy the task can be completed by AI, if there is a case of malfunction occurring, the whole thing means nothing. AI also have the tendency to lose the essential information or mistakenly modified or overwrite them. AI or a computer system needs to be switched off on a daily basis as results for maintenance which restrain the output and efficiency of the machine.

RECOMMENDATION AND CONCLUSION

AI has the potential in various field of technology such as computer science, robotics, healthcare and even music. There are now growing efforts to unite these fields of research and create new technologies out of them. However, despite of all the manner of innovative approaches, there are still a far gap between artificial intelligence and human intelligence. Some people might argue that Ai is only the matter of processing power, but some people believe that true AI will uncover the deep understanding of how human intelligence works. AI capabilities are still questionable but in several decades to come, AI can promise infinite possibilities of growth in technology.

REFERENCES

Westenberg, M. (1993). Decision traps: The ten barriers to brilliant decision-making & how to overcome them. Acta Psychologica, 83(1), 67-69. doi:10.1016/0001-6918(93)90036-q.

Ishak, W. H. W., & Siraj, F. (n.d). ARTIFICIAL INTELLIGENCE IN MEDICAL APPLICATION: AN EXPLORATION.

Langen, P. A., Katz, J. S., Dempsey, G., & Pompano, J. (1993). REMOTE MONITORING OF HIGH-RISK PATIENTS USING ARTIFICIAL INTELLIGENCE. Paper presented at the United States Patent.

Szolovits, P., Doyle, J., Long, W. J., Kohane, I., and Pauker, S. G. (1994). Guardian Angel: Patient-Centred Health Information Systems. Technical Report MIT/LCS/TR-604. Massachusetts Institute of Technology.

Shortliffe, E. H., Fagan, L. M. and Yu, V. L. (2000). The Infectious Diseases Physician and the Internet. In Mandell, G.L., Bennett, J.E. and Dolin, R. (Eds.), Mandell, Douglas, and Bennett’s Principles and Practice of Infectious Diseases, Churchill Livingstone, Inc., Pennsylvania, pp. 3258-3263.

Shortliffe, E. H. (1987). Computer Programs to Support Clinical Decision Making. Journal of the American Medical Association, Vol. 258, No. 1.

Detmer, W. M. and Shortliffe, E. H. (1997). Using the Internet to Improve Knowledge Diffusion in Medicine. Communications of the Associations of Computing Machinery, Vol. 40, No. 8, pp. 101 – 108.

Wilson, M., & Wilson, M. (2007). Feature Robotics Industry Forum – 2006. doi:10.1108/01439910710727432

Bogue, R., & Bogue, R. (2014). The role of artificial intelligence in robotics. doi:10.1108/IR-01-2014-0300.


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