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The original deﬁnition of cybernetics was proposed by Norbert Wiener (1948) as “the science of control and communication in the animal and the machine.” Cybernetics can be deﬁned as the science that studies the communication and interactions between autonomous complex systems (machines and living organisms) through the use of information and control of their processes. In the medical ﬁeld, as cybernetics evolves, physicians have resorted to support systems with intelligent and adaptive features to help them in many diagnostic and treatment tasks. These systems use artiﬁcial neural network and fuzzy logic algorithms. Moreover, these improvements have helped clinicians in decision-making, to offer an accurate diagnosis or to deliver a better treatment (i.e., wearable robots, such as prosthesis and mechanical substitutes), resulting in the possibility to develop new approaches for a higher quality in healthcare systems (i.e., e-health). However, the use and research of high-technology developments can give rise to ethical issues. Thus, it is crucial to address the bioethical concerns that surround the application of cybernetics in medicine. Dealing with medical bioethics, physicians are forced to face several moral theories in order to get a better approximation about speciﬁc problems and generate solutions based on a bioethical reﬂection process.
During the Second World War (1939–1945), Norbert Wiener was working in a military program for the American government when he and Julian Bigelow conceived a science that could predict behaviors and control processes. This science would be known as cybernetics. With the establishment of cybernetics, a different point of view to approach the way of studying systems was provided, and rapidly, many disciplines adopted principles proposed by this newborn science. One principle that was signiﬁcant and made cybernetics an attractive science was the “feedback” control, which consists of a process that counteracts errors that a system can make. The expansion of cybernetics consolidated disciplines and ﬁelds such as artiﬁcial intelligence, neural networks, control engineering, etc. (Interview to Bigelow by Richard R. Mertz, 1971; Heylighen and Joslyn 2001).
Medicine and healthcare have adopted supporting systems constituted by concepts and principles derived from cybernetics. These systems use artiﬁcial neural networks (ANNs) and fuzzy logic (FL) approaches composed of intelligent algorithms and adaptive characteristics to apply them mainly for decision-making. Moreover, new therapeutic options have been developed thanks to cybernetics; for example, robotic exoskeletons have provided a new hope for patients that need prosthesis to recover a limb’s functionality. Also, e-health has been introduced as a proposal for making healthcare systems more efﬁcient because this approach could reduce costs and beneﬁt patients by making them active participants (Hass and Burnham 2008; Pons et al. 2008; Demiris 2004).
The use of technology in the medical ﬁeld has arisen many bioethical questions about responsibilities and ethical issues derived from machines and devices that are used in daily clinical encounters. Also, decision-support systems have generated ethical issues, such as the purpose and moment when these systems must be used, the individual who is responsible for management of the system, etc. Furthermore, the bioethical principles (autonomy, beneﬁcence, non-maleﬁcence, and justice) can be outrageous from some points of view, when clinical trials are performed, new technology is used, or some researches are conducted (Miller and Goodman 1998; Snapper 1998; Caplan 2004).
History And Development Of Cybernetics
Back in ancient Greece, Plato (427–347 BC) applied the Greek word kubernήtZB (kubernetes) to refer to how governors ruled and directed society. Similarly, this word, which means “steersman,” made reference to the person who was in charge of the steer in large ships in old Greece and who controlled the ship’s destination course. But it was not until the publication of the Essai sur la Philosophie des Sciences by the French André-Marie Ampère (1775–1836) in the nineteenth century that the word “cybernetics” was used as it is known nowadays. Despite the meaning given to this term later, Ampère used it to describe the science of government. However, in the twentieth century, the American mathematician Norbert Wiener (1894–1964) in his widely known book Cybernetics: or Control and Communication in the Animal and the Machine (1948) established an innovative theory for the organization and control of systems and, more importantly, the development of a new science called cybernetics (Francois 1999; Heylighen and Joslyn 2001).
During the Second World War, the American government was trying to ﬁgure out a method to predict the exact position of the military alliance’s (Axis) airplanes in combat for a more effective attack. Therefore, the government created a classiﬁed military program, which was in charge of N. Wiener and Julian Bigelow (1913–2003), an American engineer and mathematician who graduated from the Massachusetts Institute of Technology with experience working in companies such as the International Business Machines Corporation and Sperry Corporation. The purpose of the program was to employ computational methods to predict the airplanes’ ﬂight. Consequently, after 3 years of work, Bigelow and his co-workers invented the “curved ﬂight tracking computer” (Interview to Bigelow by Richard R. Mertz, 1971). Furthermore, thanks to this project, Wiener was able to conceptualize the idea of cybernetics more clearly and began to develop it.
Afterward, while working in the military program, Wiener and Bigelow consulted Arturo Rosenblueth (1900–1970), a Mexican physician and physiologist that worked extensively in the ﬁeld of neurophysiology together with an American physiologist, Walter Bradford Cannon (1871–1945), and asked him if there was any nervous system disorder, referring particularly to tremors, which could appear when someone executed an action but not in a rest state. Rosenblueth answered that there were pathologies called “intended tremors” where the problem was located in the cerebellum that is responsible for the muscular activity. Therefore, Wiener and Bigelow conﬁrmed that “feedback” played an important role in the control of different variations in the functionality in human beings (Wiener 1982). Finally, in 1943, all the concepts and principles were put together and the paper titled Behavior, Purpose and Teleology by Wiener, Bigelow, and Rosenblueth was published in the Philosophy of Science. This work laid the foundations of cybernetics and marked the path that it would follow (Markoff 2003).
In addition, Wiener continued developing cybernetics in the following years and suggested that the way to control systems was to regulate feedback through the transmission of information. This approach was based on the work published by Claude Elwood Shannon (1916–2001) A Mathematical Theory of Communication (1948) which describes how information ﬂows in a communication system. As a result of this, Wiener wanted to create a new perspective on cybernetics, considering it useful in the application for different sciences (i.e., physiology, biology, social sciences, etc.), so he correlated cybernetics with other points of view in mathematics, thermodynamics, and logics in order to give a wider point of view to this new evolving science (Francois 1999).
It is important to point out that the general systems theory (GST) founded by the Austrian Ludwig Von Bertalanffy (1901–1972) has been taken into account by cybernetics. This theory studies the systems from multiple angles; despite the fact that cybernetics focuses mainly on functional systems, both GST and cybernetics have helped for the development of systems. The entire trajectory that cybernetics took led to the consolidation of a variety of disciplines and the development of other ﬁelds such as computer science, artiﬁcial intelligence (AI), neural networks, and control engineering (Heylighen and Joslyn 2001).
Concept And Approach Of Cybernetics
The original deﬁnition of cybernetics was proposed by N. Wiener (1948) as “the science of control and communication, in the animal and the machine.” Bearing this in mind, cybernetics can be deﬁned as the science that studies the communication and interactions between autonomous complex systems (machines and living organisms) through the use of information and the control of their processes. For this reason, it is a multidisciplinary science complemented by different sciences or disciplines such as biology, engineering, sociology, and psychology, among others. This science mainly focuses on the functionality of the system for the achievement of its goals rather than its components (Ashby 1957; Heylighen and Joslyn 2001). In addition, it is important to understand what a system is, and according to Hass and Burnham (2008), a system is a “combination or assemblage of interdependent, interrelated, or interacting elements which perform a set of functions” (p. 1).
Therefore, what really matters to cybernetics is how things behave and function. It offers a form of organization on how systems may be conducted, related, and conceived in such a way that William Ross Ashby (1903–1972) thought of the signiﬁcant applications in the biological sciences. Likewise, Ashby considered that cybernetics could generate “parallelisms,” for example, between machines, brain, and society, making the application of a common language to discoveries possible, where they can be interchangeably used in different ﬁelds. One of the most important features is that cybernetics offers methods for studying and controlling complex systems; therefore, it offers ideas on how to approach the complexity of systems (Ashby 1957).
For the control and regulation of complex systems, cybernetics proposes a series of processes that can be applied feedback and feed forward as well as buffering. Feedback and feed forward are controlled and regulated through actions generated by the system, counteracting or inhibiting the effects of variations. Speciﬁcally, feed forward exerts actions before there is affection to the function of the system. In other words, it anticipates before something happens. Otherwise, the feedback process deals after something has happened, so it counteracts the errors. That is why it is also known as error-controlled regulation. Besides, there are negative and positive feedbacks that preserve stability and allow growth, the same as self-organization. In contrast, buffering is referred to as the way of reducing changes, so it can be considered as a stable equilibrium (Heylighen and Joslyn 2001). This shows a very important contribution of cybernetics to complex systems.
Cybernetics In Medicine And In The Healthcare Systems
Throughout history, cybernetics has followed two trends; thus, it is imperative to emphasize them. During the foundation of cybernetics, the attention was centered in a mechanistic approach; conversely, in the 1970s, there were concepts and principles that were strongly highlighted such as autonomy, self-organization, cognition, and the possibility of modeling systems. This transition of cybernetics was known as the second-order cybernetics (Heylighen and Joslyn 2001). As a result, there was a signiﬁcant number of sciences and disciplines that implemented these concepts and principles to adapt systems into their ﬁelds in order to enrich them (i.e., medicine and healthcare).
For instance, in the history of medicine, discoveries have led to generate a great amount of knowledge, and for this reason, medical physicians have resorted to support systems with intelligent and adaptive features to help them achieve many diagnostic and treatment tasks. Moreover, these systems have helped clinicians in decision-making, to offer an accurate diagnosis or to deliver a better treatment, resulting in the possibility to develop new approaches for a higher quality in healthcare. The systems include software and hardware that have to fulﬁll some special characteristics, as the ones previously mentioned, in order to provide that kind of actions. Thereby, intelligent systems must be able to learn to transform their processes, when needed, based on the environmental conditions. This property of arrangement is called “adaptation,” which consists in the internal reorganization within the system to change its operational dimensions. These two properties are essential to satisfy the goals and functions of a system (Hass and Burnham 2008).
The software-based systems are composed of several kinds of algorithms designed to be intelligent and of course to permeate an adaptive nature.
Therefrom, the systems must be “trained” with historical or expert data that correlates situations or conditions to generate knowledge with the objective to create an illustrative model. The main examples of intelligent and adaptive systems are artiﬁcial neural networks (ANNs) and fuzzy logic (FL). However, heterogeneous and complementary systems can be built up from these two main categories. For instance, the mixed systems are neurofuzzy (NF), adaptive neurofuzzy inference system (ANFIS), and fuzzy cognitive maps (FCM) (Hass and Burnham 2008).
Likewise, for the design and inception of the intelligent systems, human behavior and physiology have been the main insights for the designers. This last idea can be clearly exempliﬁed by systems using ANNs, because they were developed based on how neurons function. Additionally, the development of the systems is constantly improving and algorithms are trying to incorporate biological and AI principles to reﬁne them. There are many signiﬁcant applications of intelligent and adaptive systems in medicine that have been used to diagnose or to treat pathologies such as cancer, pulmonary embolism, coronary disease, etc. (Hass and Burnham 2008).
Furthermore, as in medicine and in the healthcare system, the management of information is immense; mechanisms for processing information have to be present in the systems to select the correct data and provide the best options (Hass and Burnham 2008). Depending on the necessities, three different approaches for processing information have been created. For example, the integral-differential approach uses numbers to formulate mathematical integral and differential equations in order to explain how a process functions but is rarely used in the medical ﬁeld. Another example is the empirical-data approach, which uses tools to calculate nonlinear functions. Therefore, this approach is able to predict outcomes of systems, based on their behaviors. In order to reach this goal, systems must be previously trained with the correct data. Hence, this kind of approach is used by ANN to create relationships between sets of data, and consequently, it can readily be applied to medicine. Finally, the linguistic approach uses tools that facilitate the utilization of words in computers. Thus, FL-based systems are used for this approach. Some FL systems used by clinicians assist them to make inferences or deductions taking this approach into account. Because factual situations use a diversity of information, the modeling of systems must consider these three types of approaches in order to produce a ﬂexible efﬁcient system (Dourado et al. 2008).
Artificial Neural Networks And Fuzzy Logic: Decision-Making In Medicine
The basic element of an ANN is an artiﬁcial neuron capable of simulating what happens in a physiologic environment. Thus, this neuron mimics to receive an electrical impulse to reach a particular threshold in order to bring forth certain actions. In the artiﬁcial neuron, numerical values are provided to the input, as an analogy of an electrical impulse. Then, these values are summed up and pondered to give a particular activation function that is later transformed into an output signal. Also, ANN can be arranged as a network organized in series or parallel in order to create a model with nonlinear relations between a set of inputs and outputs, for example, the multilayer feed forward neural network and the radial basis function neural networks (Dourado et al. 2008).
Concretely, ANN is used to make relationships between two sets of data, the input and the output. In order to make accurate relationships and to get the efﬁcient working of ANN, the training phase is one of the most essential steps. This phase is achieved by presenting a series of inputs to the network in order to generate an output that is desirable, in other words, a goal outcome. For this task, a training algorithm is in charge for minimizing the errors by using degrees of freedom to ﬁnd the most accurate output, based on the criteria previously assigned to the network. In order to minimize the errors, the network manages its control and regulation by using feedback or feed forward processes. Finally, when the network is trained, it can prognosticate a behavior or it can give different solutions based on new inputs. So it is important to point out that when more quality of the inputs is provided to the ANN, according to a speciﬁc situation, a more experienced network will be available (Dourado et al. 2008).
For instance, as it is mentioned before, ANN can be applied to medical decision issues. First of all, signs and symptoms are provided to the input of the network as numerical values, which correspond to a precise diagnosis. Then, the values are summed up and pondered by the network in order to produce an output with the accurate diagnosis, also represented by a numerical value. This function of ANN resembles how physicians are trained during all their clinical encounters. When medical students are in their clinical clerkships or rotations, known cases with speciﬁc signs, symptoms, and diagnosis of pathologies are reviewed in order to prepare these physicians to give an accurate output when they encounter a patient with similar characteristics (Dourado et al. 2008).
Moreover, there are particular applications of ANN in medicine seen in the daily practice. In prostate cancer, for example, the prostate-speciﬁc antigen (PSA) secreted by epithelial cells of this gland is produced over normal range values in prostate hyperplasia or cancer. Therefore, there is a correlation between high levels of PSA and prostate cancer. In order to analyze this correlation, a computer-assisted diagnosis for prostate cancer is available under the brand name of Prost Asure. This system uses ANN, and based on different parameters of PSA, it can assist the diagnosis of prostate cancer. Also, the application of an ANN system can be used to make a more accurate diagnosis for breast cancer. For this task, a computer-assisted diagnostic system based on radiology studies assists physicians to detect suspicious lesions in order to denote a more precise diagnosis for this pathology (Fisher et al. 2008).
In addition, FL deals with a wide scope of true and false statements; in other words, a system that uses FL does not consider that there is a completely “true or false” outcome. First, it is important to point out that FL systems function through a set of rules that are previously assigned and clustered, named fuzzy rules. These rules are the knowledge (i.e., symptoms, signs, tests, etc.) for diagnosing pathologies. To have a clear idea of how this system works, it is easier to exemplify an application in the medical ﬁeld. Thus, after the rules are set to the system, the input values are presented as symptoms or level of health of a patient’s case provided by numerical values and then transformed into linguistic variables. Afterward, the inputs and the fuzzy rules are combined and relationships are made of each case. The result is the diagnosis of the case based on the relationships generated between the inputs and the fuzzy rules assigned. In brief, FL systems permit the clarity between knowledge and decision-making; hence, they are helpful as decision-support systems (Dourado et al. 2008).
Robotics: A Bridge To A Therapeutic Option
Many disciplines were developed due to the foundation of cybernetics. One of them is robotics, a branch of AI which nowadays has been introduced to many ﬁelds, such as medicine. An interesting application of robotics in medicine is wearable robots. This refers to person-oriented robots, which an individual can manage in order to support or supplant a function. This provides therapeutic options for patients who have suffered amputations or dysfunction of their limbs; thus, these robots offer tools to supplement, back up, or augment limb functions (Pons et al. 2008).
Furthermore, wearable robots can be classiﬁed as empowering robotic exoskeletons, orthotic robots, and prosthetic robots. Empowering robotic exoskeletons are the robots that offer a “magniﬁed function” for a speciﬁc anatomic part; in other words, the function is not the natural one. The orthotic robots are mechanical devices that help to restore physiologic functions especially in neurological pathologies. And ﬁnally, prosthetic robots are electronic and mechanical structures that supplant an anatomic part for amputated limbs (Pons et al. 2008). This classiﬁcation portrays many applications of robotics into medicine, but more importantly, it provides a new hope for patients.
E-Health: A Proposal For The Improvement Of Healthcare Systems
Nowadays, healthcare systems deal with high costs, expenses, and an immense demanding of patients. Hence, new proposals and strategies are being adopted in order to reduce these issues without jeopardizing the quality of care. These proposals and strategies are directed to outpatient services to use technology in order to reach speciﬁc goals. For instance, e-health offers a strategy that uses advanced telecommunications, such as networks, devices, the Internet, etc., for the transformation of the medical care and its delivery. Moreover, e-health provides a new method for supporting healthcare processes, empowerment to patients to be active in their health, and also, new perspectives for decision-making (Demiris 2004).
There are a variety of devices that e-health may be used for prevention and to monitor vital signs or other parameters. These two approaches may be the key features for anticipating diseases and, if necessary, to provide an early treatment. Also, e-health could be a strategy to overcome the borders of territories for healthcare systems to reach a massive population and to provide a quality of care at all levels. However, the policies and ethical framework of e-health are not well established; thus, for implementing this way to reach patients with so “inmost relationships” between care providers and people, these issues must be discussed (Demiris 2004).
One current application of e-health is the home healthcare which offers a combination of care services to patients, families, and caretakers at their homes. In this setting, issues previously mentioned such as high costs and reaching a vast population could be approached. Two ways in which home healthcare can comply with these objectives are telehomecare and smart homes. Telehomecare uses telemedicine systems installed in patients’ homes that enable communication for patients and care providers. This way to approach patients can be affected by a variety of factors such as the correct primary diagnosis gave to patients, the induction of them to use this kind of system, and their physical limitations, among others. Additionally, smart homes can be a new approach that can help to prevent from diseases and to promote a healthy life by installing monitoring devices, not only for measuring vital signs, but activities that people do in a daily basis. Speciﬁcally referring to smart homes, the transfer and management of patients’ information is the main concern, because privacy policies are not well established and this information could be used for other purposes, with the risk of jeopardizing ethical issues (Demiris 2004).
Bioethical Dimension Of Cybernetics
In the last 25 years of the twentieth century, bioethics has evolved enormously, thanks to the collaboration of many disciplines. This evolution has been possible, mainly, due to the development of new technologies in the medical ﬁeld. Therefore, bioethics has provided a framework with a multidisciplinary perspective to locate the problems that compromise the principles that support this discipline, as well as to try to ﬁnd the best solutions for daily encountered issues. However, ambiguity and controversy might be sometimes present in the resolution of the issues (Miller and Goodman 1998).
Moreover, as science and technology progress, healthcare professionals’ dreams of curing fatal diseases and enhancing human capacities could become real. For instance, prosthesis and mechanical substitutes are a perfect approach to reaching this goal. However, the use and research that surround this kind of devices have given rise to ethical issues that can be extended to other types of high-technology developments (Caplan 2004). Thus, it is crucial to address the bioethical issues that surround the application of cybernetics into medicine, such as medical decision-support software (MDSS), health informatics, intelligent machines, etc.
Medical Decision-Support Software
It was previously described in this document that decision-making systems are being broadly used in medicine to help physicians make right decisions for the improvement of medical care and an accurate therapy choice. Furthermore, it has been said that these systems are software based and useful in clinical practice; thus, these kinds of systems are also known as MDSS. The constant development of technology has impacted directly to the MDSS, making it necessary to embrace a range of abilities and capabilities to solve issues found in daily clinical encounters, such as the calculation of drug doses, emission of warnings for possible drug interactions, assistance for the interpretation of arterial blood results, monitoring of patients in case of lethal arrhythmias, etc. (Miller and Goodman 1998).
It is evident that when any of these MDSS or other systems is used to make decisions, the patients’ health is involved and ethical issues emerge. One of these ethical issues addresses the purpose and moment when these support systems must be used; it is clearly that the purpose of MDSS is to improve healthcare and the way to provide it to patients. Moreover, this software must be used when physicians face a problem and when the usage of this system can solve this problem efﬁciently. Another ethical issue that is transcendental deals with the people who are responsible for using these systems. In order to use MDSS, there are elements that the healthcare personnel must have in order to use this kind of support systems; thus, they must possess speciﬁc qualiﬁcations. Prior to the personnel assignment, studies must reveal the safety and efﬁcacy of this kind of software in the hands of different users, as well as the level of training that they must have to use it efﬁciently. Although speciﬁc qualiﬁcations are needed in order to use these support systems, in the case of advanced software, high-quality trainings have to be developed to meet the safety usage requirements by healthcare personnel (Miller and Goodman 1998).
The use of MDSS or other support systems is and will be constantly used in the clinical encounters, not just to improve the healthcare quality and delivery, but also to prevent and promote health. In addition, there are speciﬁc factors that will lead to the extensive usage of these support systems, for example, the new development of technology and the socioeconomic issues. Finally, the ethical issues must be developing in a parallel way to the evolution of the MDSS (Miller and Goodman 1998).
Responsibilities And Ethical Issues Assigned To Intelligent Machines
Intelligent machines have been used in medicine for many years. They have been helpful to monitor and to evaluate patients, and they also helped physicians to interpret laboratory data and to base their clinical decisions, among others. In the hospital life, it is common to adjust functions of machines in order to modify an abnormal parameter presented by a patient. But when these intelligent machines perform more complex tasks, the judgment they apply is similar to that made by humans; therefore, ethical issues and responsibilities must be assessed (Snapper 1998).
The way of application of ethical issues and responsibilities for judgments made by machines will affect their usage. This is because physicians may trust the judgments made by the machines more than their own, and they may also rely on the conclusions made by the machines more than their own. Moreover, if responsibility is completely attributed to the machine, then it has to be legally deﬁned who is to blame for a problem: the operator, the owner, the manufacturer, the designers of the machines, or the physician who trusts the judgment given. In order to reduce the impact of these ethical issues, some physicians have resisted using computerized systems to make medical decisions, mainly to control their own decisions and responsibilities (Snapper 1998).
Furthermore, as in the medical ﬁeld, the activities are increasingly growing; the duties have been distributed in order to focus on speciﬁc ones. Some of these duties have been transferred to machines (i.e., diagnostic procedures and therapeutic options); therefore, some physicians oppose the use of these systems because they feel they leave their roles behind. A solution for reducing this feeling is that physicians preserve their traditional role combining it with the use of machines but always applying their judgment (Snapper 1998). With the globalized and constantly evolving medical knowledge that is generated every day, it is difﬁcult to abandon the support system, mainly because they help us to diagnose and treat patients efﬁciently. But, it is obvious that they only help, and that is why the physicians must never leave their judgment and responsibility behind.
A Principlism Approach To The Applications Of Cybernetics In Medicine
Certainly, when dealing with medical bioethics, physicians are forced to face several moral theories in order to get a better approximation about speciﬁc problems and to generate solutions based on a bioethical reﬂection process. One of those moral theories, and seemingly the most popular among physicians, is the principlism. This term refers to the four basic principles: autonomy, beneﬁcence, non-maleﬁcence, and justice, in order to enhance moral assessments in the daily practice of healthcare providers.
To understand these principles, a brief deﬁnition of each one of them is exposed. Autonomy can be understood as the way a moral subject rules his own life and chooses the best options for himself without any external constraint. Beneﬁcence points out that every single decision must be made by the person confronting an issue, because only the subject knows exactly what he wants for his own beneﬁt. Non-maleﬁcence implicates the response of an external moral agent; it deals with the concern that nobody should do any harm to another subject. Finally, justice can be understood as the equal consideration and distribution of beneﬁts in a society.
The autonomy principle could be endangered when patients are to encounter “death situations.” For example, when individuals participate in clinical trials, they are always susceptible to “death” probabilities or getting worse, if the therapeutic tested in this trial is not provided. Though, clinical researchers consider that the informed consent is enough for patients to accept or reject their participation in these clinical trials. But with other perspective, the patients, despite knowing the possible risks and beneﬁts, are constrained to accept the therapeutic because of the risk of dying or getting worse (Caplan 2004).
Additionally, beneﬁcence and non-maleﬁcence could be threatened with the availability of new technologies, therapeutics, or devices in the medical ﬁeld. When there is accessibility of these options, questions can be raised of who are the ones in charge of choosing them for healing or improving life quality… physicians or patients? According to the beneﬁcence principle, the one in charge of choosing therapeutics should be the patient that is facing a health issue. However, the physicians have the knowledge and the background of the condition that the patient suffers and they know both the risks and beneﬁts of the therapeutics, so they must make the decision in order not to outrage the non-maleﬁcence principle. Moreover, there must be counterbalance between simply elongating lifetime and improving life quality by the healthcare professionals (Caplan 2004).
Furthermore, the principle of justice in medicine can be infringed with the allocation of a great amount of budget to expensive new medical technology. This idea raises questions, whether where this money should be destined for and which diseases should be prioritized and also if there is a beneﬁt in spending so many resources in research for medical devices that are not going to be available for all the patients. Hence, medical researchers should be focused on the development of new technologies that could be available for all patients that need the device to improve their health. In the same way, another question that may arise is whether it is right and fair to develop expensive methods, while millions of individuals die before reaching adolescence from diseases and injuries that can be already prevented at a lower cost (Caplan 2004).
In summary, these issues presented lead to propose new points of view and solutions for daily clinical problems that compromise ethical principles in medicine.
It is evident that cybernetics has provided a framework with concepts and principles to enrich many disciplines and ﬁelds. These concepts and principles proposed have constructed new ways to understand complex systems and they have created support software for decision-making. In medicine and healthcare systems, support software is very important to make diagnosis and to offer therapeutics to patients, but many ethical issues have arisen because the ethical framework is not well established. Therefore, it is substantially important to address an ethical framework and policies to rule the use of support systems, research, intelligent machines, devices, prosthesis, etc., because the main goal of physicians and health professionals is the pursuit of the quality of life and patients without harming them.
- Ashby, W. R. (1957). An introduction to cybernetics. London: Chapman & Hall.
- Caplan, A. L. (2004). Artiﬁcial hearts and cardiac assist devices. In S. G. Post (Ed.), Encyclopedia of bioethics (pp. 225–230). New York: Thomson Gale.
- Demiris, G. (2004). E-health: Current status and future trends. Amsterdam: Ios Press.
- Dourado, A., Henriques, J., & De Carvalho, P. (2008). Neural, fuzzy, and neurofuzzy systems for medical applications. In O. C. Hass & K. Burnham (Eds.), Intelligent and adaptive systems in medicine (pp. 127–146). Boca Raton: Taylor & Francis.
- Fisher, M., Yu, S., & Aldridge, R. (2008). Some applications of intelligent systems in cancer treatment: A review. In O. C. Hass & K. J. Burnham (Eds.), Intelligent and adaptive systems in medicine (pp. 283–288). Boca Raton: Taylor & Francis.
- Francois, C. (1999). Systemics and cybernetics in a historical perspective. Systems Research and Behavioral Science, 16, 203–219.
- Hass, O. C., & Burnham, K. J. (2008). Intelligent and adaptive systems in medicine. Boca Raton: Taylor & Francis.
- Heylighen, F., & Joslyn, C. (2001). Cybernetics and second-order cybernetics. In R.A Meyers (Ed.), Encyclopedia of physical science & technology. New York: Academic Press.
- Markoff, J. (2003). Julian Bigelow, 89, mathematician and computer pioneer. The New York Times. Retrieved from http://www.nytimes.com/2003/02/22/business/julian-bigelow-89-mathematician-and-computer-pio neer.html.
- Miller, R. A., & Goodman, K. W. (1998). Ethical challenges in the use of decision-support software in clinical practice. In K. W. Goodman (Ed.), Ethics, computing and medicine. Informatics and the transformation of health care (pp. 102–112). New York: Cambridge University Press.
- Pons, J. L., Ceres, R., & Calderón, L. (2008). Introduction to wearable robotics. In J. L. Pons (Ed.), Wearable robots. Biomechatronic Exoskeletons (pp. 1–5). West Sussex: Wiley.
- Smithsonian National Museum of American History (1971). Computer oral history collection, 1969-1973, 1977: Interview to Julian Bigelow by Richard Mertz. Retrieved from http://invention.smithsonian.org/down loads/fa_cohc_tr_bige710120.pdf
- Snapper, J. W. (1998). Responsibility for computer-based decisions in health care. In K. W. Goodman (Ed.), Ethics, computing, and medicine. Informatics and the transformation of health care (pp. 43–55). New York: Cambridge University Press.
- Wiener, N. (1982). Soy un matemático. México: Consejo Nacional de Ciencia y Tecnología, Colección Ciencia y Desarrollo.
- Wiener, N. (1965). Cybernetics: Or control and communication in the animal and the machine. Cambridge, MA: The Massachusetts Institute of Technology.
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