Discuss About The Artificial Intelligence Transforms Medicine.
Technology has already been used to improve many aspects of medical care. Application of modern day machines and computerized equipment has been used to increase efficacy and work output in areas such as medical records, diagnostics such as imaging and drug development. The central aim of creating artificial intelligence was to create a system that would pass the Turing test. The system of artificial intelligence to be created was to display features of human intelligence with cognitive memory, empathy, judgement and the ability to make human-like decisions. In 2010, substantial strides forward were made with the invention and success of deep learning. Deep learning is comprised of a machine learning in which several layers of nodes are present between output layers as well as input layers. Therefore, simulating these output and input neuron layers is known as artificial neural network. Artificial neural network play a vital role in underpinning many recent advances in; (1) self-driving vehicles, (2) image classification, (3) text translation and (4) speech recognition (Hamid, 2016).
Review of previous study state that, these types of events illustrate a real challenge for both legal as well as ethical frameworks for sharing confidential data. 2017, has marked as a change for artificial intelligence in healthcare sector. Also, this change bought numerous changes in data scientists and clinicians as they are supported by clinical informatics and started yielding positive results (Johnson, 2018).
This change made clinicians to understand about informatics in huge datasets. The insights drawn from informatics have become an important pillar for various clinical practices. However, there is no doubt; artificial intelligence remained overhyped in healthcare sector at the risk of commercial explosion (Greenberg, 2017).
MYCIN received an acceptability rating of 65% by the evaluators; the corresponding ratings for acceptability of the regimen prescribed by the five faculty specialists ranged from 42.5% to 62.5%. So, better than the human experts considered individually. However, MYCIN failed simple because expert professionals are able to explain their reasoning, which is essential if they are to be used for diagnosis while neural networks cannot. Deep learning and similar approaches might be useful in two areas though: interpretation of images, e.g. what’s on this X-ray or ultrasound scan, or what type of rash is this; and undiscovered associations, e.g. are patients who were given drug X combined with drug Y for disease P more likely to get disease Q later on in life. Deep learning in medicine also has a downside even supposing it works: lots of patient records are required, and anonymous ones can be linked to the people they describe, so there’s a confidentiality problem. This was the MYCIN project, and in spite of the excellent research results, it never made its way into clinical practice. One of the problems of expert systems was getting them adopted by end users.
Artificial intelligence has made tremendous steps in the modern error of robotic technologies. However, in the health care sector, the adaptation of the artificial intelligence solution to efficient medical care has been painfully slow. It was therefore essential to identify factors and challenges that influenced or hindered this systematic adoption of artificial intelligence in the health sector. The future of medicine is in artificial intelligence. Several developments and technological innovation are in progress to meet the need for artificial intelligence in the field of medicine. However, these steps are met by bundles of uncertainties that do not allow smooth implementation and usage of the robotic artificial intelligence solutions.
The innovation and inventions of artificial intelligence system in the field of medicine has not been fully looked into ever since because most of the innovators and researchers believe that the most of the medical related problems which are problems affecting human beings are not meant to be checked using the artificial intelligence systems.
This assumption has made the sector to suffer very much since most of the functioning of the health sector needs human brains in most of the cases. This to some extend has led to the increase in the number of deaths since wrong diagnostic using human evaluation and reasoning might take place hence leading to the increased number of deaths.
If the same challenge might be looked into, this problem of wrong diagnosis might be hardly present. Though some countries are employing the use of robotics in the surgery of human beings though up to now this is not fully functioning. The above is the most challenge that faces the field of medicine as far as the employment of technological techniques which uses human interaction is concerned.
In the field of medicine and health care delivery, technology has been used to improve diagnostic procedures, management and drug development. This is for the simple reason that technology has been acting as the back born of almost every process in the health care delivery system. Straight from the patient record capture, management and data retrieval to complex diagnosis and also currently on some surgeries using robots.
In the diagnostic field of medicine for instance, technology such as imaging using complex machines has been used to enhance efficiency and speed of diagnosis. Machines include but not limited to computed tomography scans, magnetic resonance imaging modalities and ultra sounds, this has helped many patients with various medical connected problems and illness to get quick and more accurate diagnosis since almost everything is done by the artificial intelligence machines. This is the most effective part of medical field where artificial intelligence has played a very practical part and has also been more efficient in the delivery of service.
In the laboratories, machines are used to increase the efficacy of pathologic diagnoses for cytology or tissue diagnosis.
A 3D visualizations system assists the surgeons to have a strong magnified view. The surgeon controls Da Vinci ensuring that complex, tiny and routine surgical activities are of the highest level of accuracy. By doing so, the whole process of surgery becomes so fast that even as compared to manual process where the whole process is taken by the surgeons, this is so fast and its accuracy is very high. The only problem with the robotic system is that its rate of spoiling or experiencing mechanical problem is do easy. And this can take place while the process of operation is underway.
This is an artificial intelligence app that analyses symptoms of its users and offers a management plan. The AI tool uses patient’s past medical history, genetics and symptoms to plan treatment. The tool also is connected to other portable devices that record patients’ heart rate and sleep patterns and advices them on healthy living. Though some illness varies with regions, hence making the application limited and also its accurate can be compromised when the illness mutation takes place.
Using computer algorithms and data about normal and pathological retina, this tool examines the images of a patient’s retina for signs of diabetes. This AI system allows for quick diagnoses of subtle changes to the human retina that could be missed by the naked eye thus reducing retinopathies as a diabetic complication.
However, artificial intelligence programs have met tremendous resistance to penetrate into clinical practice. This formed the basis for the research.
Artificial intelligence requires a systematic assessment in order to integrate clinical care in routine. Authors are of opinion that the term AI (artificial intelligence) is often refer to various machine learning techniques. In other words, artificial intelligence makes an important difference in reality, therefore it is important to reach ANI (artificial narrow intelligence) (Krisberg, 2017).
Artificial narrow intelligence (ANI) does more that required for completing the tasks. In brief, artificial narrow intelligence can defeat humans in complex scenarios. International Business Machines Corporation Watson’s victory is one best illustration for this. The researchers also quoted that, when artificial narrow intelligence come in to existence, the pieces will ultimately fall in to correct place (Johnson, 2018).
The basis of the project was:
To determine the factors that are necessary to the process of adoption and implementation of artificial intelligence.
To identify challenges facing the implementation and use of artificial intelligence in the practice of health care
What was the process of implementation of artificial intelligence?
What were the factors that the health organizations considered for smooth adoption of the artificial intelligence?
What are the challenges that are facing the implementation of artificial intelligence in facilities that had not yet implemented AI?
Empirical analysis was used as the primary research method. Empiric data was quantitatively and qualitatively observed and experienced in the setting of the health scenarios. Considering the implementations that were in place before, the advantages and limitations that resulted from the implementation of artificial intelligence solutions in the health facilities was recorded. Secondary sources of information were reviewed and used to formulate the literature review.
This being the process of gathering information from different sources and using different methods which are available data was collected through direct observation, experience of the artificial intelligence system in practice and questionnaires. Data was collected largely on the implementation of artificial intelligence in the health scenario. Very few health care centers had implemented and applied artificial intelligence in the practice of medical care even though the numerous ways of implementation that had been brought forward and developed. The process of data collection was focused on the scenarios which included implementation of artificial intelligence. This data collection was instinctively based on the qualitative and quantitative determining of the insights towards artificial intelligence implementation, adoption for use into practice and the challenges the organizations faced before, during and after implementation.
Hospital data and patients’ data is private and confidential. Several key ethical issues were considered in the study that included:
Respect for persons, autonomy, dignity and personal opinions.
Participants were only selected from groups of health facilities that would benefit from the research in implementation of artificial intelligence to ensure a smooth transition.
Ethical considerations were strictly observed during the research period and data that is open for public access and non-personal information will be used to analyze the data.
Since this process involves taking the assortment of the data which is collected and recorded then now analyzing the data; Data analysis was focused in two phases. In phase one, the implementation of artificial intelligence in the organizations that had set up a robust mechanism for smooth transition was reviewed. In phase two, current challenges that were encountered by the facilities that had not implemented the artificial intelligence solution were analyzed. In accordance with the main objectives of the study, importance is based upon finding, understanding and developing solutions to the major challenges that impact implementation of artificial intelligence.
In the paper “Adapting to Artificial Intelligence Radiologists and Pathologists as Information Specialists” by Saurabh Jha, the author believes that artificial intelligence is becoming a reality in medicine. The mimicking of human brains by computer robotics would change the spectrum of practice of medicine. The author outlines how the fifth revolution of technology would help improve the practice of medicine in radiology and pathology. The writer uses the example of deep learning. He compares it to the registrar in medical radiology. Just like how the human brain learns by interpreting more images, so does the deep learning artificial intelligence computer. However, the author describes a challenge to the adoption of artificial intelligence in radiology and pathology to the fears attributed to human radiologists and pathologists being replaced by computers. Although he points out the fear as being exaggerated, it is a challenge worth to note against implementation of artificial intelligence.
Artificial intelligence has crept into entirely every aspect of human life. From the text by Jeffrey Allan “Deep Learning Algorithms for Detection of Lymph Node Metastases from Breast Cancer” in helping artificial intelligence be seen, the author notes that radiology and pathology having to use images for diagnostics, can adopt and implement artificial intelligence. The author describes the advantages of deploying artificial intelligence in the practice of medicine in radiology and pathology departments by using the digital images developed over time to develop a computerized robot loaded with the images that would be able to detect pathologies by comparing new images to its stored images. He however notes that apart from the advantages, limitations to implementation of the artificial intelligence program are restrained by practical and financial obstacles. Digitalization of pathological and radiological images would not reduce the anatomic workflow nor remove the need for glass slides in pathology, it would therefore require additional personnel, equipment and storage services that would increase financial requirements on the already stressed health care system.
In the use of technology to improve the quality of health care delivery, artificial intelligence has been deployed in the form of a robot that demonstrates ability to converse to patients about their problems. The technology, named Gabby, is a racially ambiguous female in her mid-forties. ( Adam Miner, Arnold Milstein and Jeffrey Hancock,2017) The software program is designed to converse with patients with chronic pain and depression like a human being would do. The author illustrates the feasibility of its implementation, citing advantages in the field of psychiatry to do clinical assessments such as mental state exam and treatment. The paper however, determined that implementation of the program Gabby faced challenges in the presence of a conversation gap between the machine and human intelligence that would pose a safety threat to patients and may even cause more harm.
Several clinical trials have been attempted to illustrate the need for adoption of artificial intelligence in health care. The study by David Crawford, Joseph Batuello and Peter Snow, November 2000 assessed the artificial intelligence methods to identify patients with prostate carcinoma risks for lymph node metastasis. An artificial intelligence tool was created using patient data from a database of 4133 patients with prostrate carcinoma to use the prostate specific antigen values and Gleason sum to identify the risk levels for lymph metastasis. The simple algorithm showed tremendous success in identifying patients at low risk of lymph prostate metastasis. However, the tool was unable to determine the relationship between TNM score and lymph metastasis. This was attributed to either a bug in the intelligence program or the fluctuating values of the TNM data.
There are several diverse artificial intelligence techniques available capable of solving many clinical problems from imaging, pathology to clinical assessments. However, despite the optimism for developing and adoption of artificial intelligence in the practice of medicine, this has not been met with enthusiasm (AN Ramesh, C Kambhampati, JRT Monson and PJ Drew, Artificial intelligence in medicine,2004.) this is so because a number of previous technologies adopted and implemented in the field of medicine such as electronic medical records did not meet their projected advantages.
Medical data can be categorized into data that can be extracted by the eyes and that can be extracted by computer software. Use of computer software in extraction of data is the basis for development of artificial intelligence programs that can use the extracted data to form decisions concerning diagnosis and management. This description moves the art and science of medicine from a subjective display of skills to an objective science. The development and invention of more and more information has equipped medical professionals with more knowledge. However, this addition of data has challenged the computational abilities of the physicians, radiologists and pathologists due to the great quantity and density of data. Thus development creates a path for necessitation of computers to supplement the medical professionals. The computers would enable of fast and accurate acquisition of data from medical situations that would otherwise mot be obvious to the human eye. In order to adapt to artificial intelligence and avoid replacement, the author Saurabh Jha, 2016 recommends that the medical professionals allow to be displaced by the computers. This new situation would allow categorization of medical into automated computer extracted data and human analysed data. In this situation, thus, computers would only compliment medical professionals in the delivery of quality health care. The reservations revolving around the development and implementation of the artificial intelligence technology should not be waivered off. These challenges have formed from the basis that most of the medical professionals do not understand the technology well. As a human instinct, we do not accept what we do not understand. For a smooth adoption and implementation of the artificial intelligence technology in medicine, all the health care professionals should be included in the design framework. This inclusion would include training of the professionals and equipping them with enough knowledge to understand the complimenting functionalities of the artificial intelligence. By so doing the most part that will be remaining will be the control of the computers and also decision making and implementation of the decision of choice that the computer based system has given out.
The current health care system is constrained by financial stresses that prevent maximization and offering of top quality service of care. Artificial intelligence is a new technology that comes with its own financial implications and requirements. The requirements of digitizing many aspects of the medical field in order to accommodate artificial intelligence and utilize the program maximally would require a huge financial input into the health care system. For example, from the text by Jeffrey Allan, 2017, digitization of the radiological images would increase the success of the artificial intelligence. However, in the same text, the digitization would not work in the field of pathology because there is no substitute for glass slides in pathology lab diagnoses. The acquisition of more personnel, computers and machines and storage facilities would be a huge financial constraint. To maneuver around this task-hill, the author recommends a slow process of implementation of artificial intelligence that Is spread over a period of time. A number of artificial intelligence tools acquire donor funding and government support initiatives after showing great steps towards improving the quality of health care delivery. The quality of service at any health care is determined by how much have we spend in keeping and loading the facility every before. This is so important as the facilities of the health care delivery system is very expensive and therefore needs sacrifices I order to equip the system for smooth handling of the problem which comes across.
The difference between human responses and machine responses has been greatly reduced. The way that this has been achieved has never been made clear to the consumers of the products. The challenge facing implementation of such programs if artificial intelligence lies within the robots’ inability to respond to the needs both conversational and emotional of the patients. This shortcoming is projected to cause ineffective care and harm to patients and therefore affects the streamline implementation of the technology for example The Gabby. The machines’ inability to pass the Turing test becomes a huge set back.
In the adoption of artificial intelligence, the author suggests randomized clinical trials to compare the effectiveness of the artificial intelligence technology with its implications on the cost and safety of the patients. The process of developing such systems is not an easy and smooth task. Therefore, a lot time and human effort is recommended to write the codes for such algorithms and data should also be extensively and intensively collected about the different disease processes to ensure a close to human intelligence performance is obtained.
Artificial intelligence programs use algorithms for form a set-tree of decision making and formulations. The algorithms analytical tools that act on data by performing many operations such as comparisons. However, the data stored in always past data from the patients. The algorithms perform analytical operations. Since the artificial intelligence are a set of instructions, are written by human to try and merge the informal language of human to the formal language of computers to form an organized system. These written algorithms are prone to system failures and bugs. The delivery of health care and the art and science of medicine backers for the do no harm, principle. Since the artificial intelligence programs are manmade computer programs are bound to periodic systemic failures and thus one cannot predict the extent of damage that might result from such technical errors and glitches. The medical field is a very conservative field with the primary objective of maintaining patients’ safety. The set of procedures practiced from the old times and are very efficient are documented. Therefore, the prospect of using machines to carry out the same is a subject of serious debate due to the argument that technology fails. This time of belief protects the patients but reduces the chances of innovation as the medical field is not ready to risk new technology.
Artificial intelligence systems are built on algorithms that depend on hospital and patient data to form bases for analysis and decision are made from the analysis. There data challenges are primarily with the highest impact in the development of artificial intelligence. Although steps have been made using the available data, the systems manufactured and implemented in the health facilities are not on 100% data and information concerning the diseases. Data has been collected about diseases and forms the biggest part of the resource for the decision making process of the artificial intelligence systems. However, the data is not sufficient enough to design a system of artificial intelligence that would otherwise replace or displace the medical professionals. Artificial intelligence systems therefore require more data that is efficiently organized, that is currently not available.
While the process of acquiring of data in the health care sector is pretty difficult with a lot legal and ethical consideration, a lot has been achieved in the development of artificial intelligence. However, such systems are not set at optimal working capabilities hence failures in the Turing test or in the clinical trials renders them disapproved by the FDA.
The term artificial intelligence is misleading in the field of medicine. At the moment, the system is not that highly developed as the term itself hypes. Artificial intelligence as at the moment is as system that is capable of detecting changes in patterns. These capabilities are used to create reports of medical assessments such deviations from the normal. Artificial intelligence is hyped with the belief that the system can extract concepts and knowledge and make informed decisions about life and medical wisdom. With the failures of the previous technologies that were hyped in advertisements and the media such as electronic health records, that destroyed the healthy and public image about artificial intelligence. The solution to this is to provide understanding of the capabilities of the artificial intelligence technology. Consumers need to understand that the system is dependable on human intelligence to achieve ,maximum performance.
The projected took a duration of 31days to completion. A total of 112 respondents, managing directors and chief executive officers of the major health centers in the country were interviewed on the factors they would consider necessary before adopting the artificial intelligence system. The responses were analyzed and categorically placed according to rank. The respondents who had adopted and implemented artificial intelligence in their practice of medicine were asked to narrate their experience in the use of the system focusing on the challenges they faced during the process. In centers without the artificial intelligence programs in place, respondents described the factors that inhibited the adoption process or the challenges they faced during the adoption process that resulted in ditching the whole system all together.
The following factors were identified as requirements for the smooth implementation of the artificial intelligence.
Trained personnel and technical expertise: 81% of the respondents ranked technical expertise as one of the primary factors that enabled the adoption and implementation of artificial intelligence. The chief executive officer and medical supervisor of the brain and spine hospital complained of limited human resources to the adoption of the artificial intelligent system.
Adequate infrastructure and computing power: artificial intelligence require specific machines and an ambient environmental set up that would enable reliability, efficiency and speed of the system. Since the technology also involves massive computations and data analysis that should be accurate and fast, 795 of the respondents ranked the need for super computers with high power computational abilities as a factor necessary for adoption and implementation of artificial intelligence.
Financial muscle: financial resources in form of startup costs, installation costs, maintenance costs and repair costs were ranked top of the factors affecting adoption and smooth implementation of the artificial intelligence.
Enthusiasm, desire and willingness to risk new technology: The medical profession has remained strictly conservative over the years. Strict observation of medical procedures to protect the patients are practiced. Therefore, the health care professionals and health facility management are very reluctant to risk new technology on the patients and thus this practice kills the innovative drive in the medical sector.
Cited as the major complaint against implementation of medical artificial intelligence system, the human fear of losing jobs in large scale due to their jobs being substituted by the computers. Another concern raised that impacted negatively on the implementation of the artificial intelligence system was the possibility of job relocation to other positions of work that would require more creative skills than the knowledge acquired through the education system. Despite the fact that there would be a need for employment of technical expertise or training on skills related to artificial intelligence, the idea of implementing the system that would see “computers take away” peoples jobs, was a matter of fierce debate. The implementation and introduction of computer based systems in most of the fields which are related to medical has been receiving set backs since human beings could replaced with the machine hence creating unemployment to the country. This is the major reason as to why the implementation is taking too long to be completed
Medical practice is a very conservative field. The principle of “do no harm” to the patient are strongly held and passed from one generation of medical professionals to the next. With this principle, issues of professional negligence, errors and/ natural disasters were easier to determine and find the individual for legal or ethical punishment in case of intentional human error. However, the implementation of artificial intelligence raises the question of “who should be implicated when technology fails and does harm to the patients?”.
Artificial intelligence is unable to display other aspects of human intelligence apart from knowledge, such as emotions. Such disabilities question the ability of the artificial intelligence to make decisions that affect patients that require the basis of emotions such as love, sympathy and empathy. Another serious issue of contention is when the artificial intelligence system or bots become more intelligent than humans and become dangerous or some malicious individual takes control to cause destruction. Most of the robotics are made by humans and can be malfunctioned or confused, this machines are not perfect and cannot employ full reasoning in decision making. For complex diagnosis the artificial intelligence system might give out wrong findings in relation to the specific fact impacted.
Serious uncertainties are placed on the implementation of artificial intelligence and its effects on the confidentiality and privacy of patients’ data and medical records. The use of technology, as determined by the failures of previous technologies such electronic medical records, on the protection of individual privacy evokes extreme reservations for the implementation of artificial intelligence.
Conclusion
Artificial intelligence is the future of medical practice. Features of human intelligent can be represented by the formal computer language and form algorithms that would be used to analyze medical aspects and form decisions like the expert medical professionals do. From the clinical trials, artificial intelligence can outperform medical professionals and thus becomes a challenge against itself implementation as a new form of technology. The factors limiting the smooth adoption and implementation of artificial intelligence in healthcare are budgetary requirements, infrastructure and computing power, technical expertise and the will to try new technology. The top ranked challenges impacting the implementation of the artificial intelligence is the fear for job losses due to substitution by computers, technological limitations such as robots being excessively intelligence than humans and thus becoming autonomous weapons of havoc and mass destruction and the privacy reservations once the artificial systems have been implemented. Artificial intelligence spells quality, fast and affordable future health care. However, the process requires a lot of input in terms of data about diseases, patients’ data including risk factors and prevalence and the computed algorithms to enable analytic calculations of determinants of medical judgement and decisions to compliment the medical professionals in delivery quality health care.
From this , its is therefore recommended that the implementation of artificial intelligence should be employed with no intentions of replacing human effort and assistance in the health sector as this can make matters more worse than the way it is now.
The employment of the robots to take part most of the minor processes will fasten and lighten the whole processes that are taking place in the health care delivery system.
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