Artificial Intelligence in the Medical Field


Artificial intelligence (AI) continues to advance in various sectors because computers are acquiring cognitive functions. Robots, which can now perform most tasks that only human beings were able to do in the past, can serve as a notable example. AI has been applied successfully in different branches of science such as science and technology where advanced computational methods have been used in biology, chemistry, and physics. Additionally, AI has been applied in engineering and weather forecast. One of the areas in which AI is gaining wide recognition is the medical field because of the increased needs currently witnessed in health care systems, the emergence of new illnesses and ailments, or a need for medical knowledge. It is noteworthy that there are different types of AI networks that are currently in use in the medical field, each contributing differently to the specific needs. The significance of AI in the medical field cannot be underestimated; however, there are long held fears and assumptions that need to be dispelled from the minds of the people and physicians, in particular, to make the technology more acceptable. Evidently, the importance of AI in the medical field, particularly its impact on the doctor and patient, cannot be underestimated.

Identifying the positive impacts of AI in the medical field can make the technology more acceptable to medical practitioners. A clearer and better explanation of how AI affects patients’ lives and the performance of doctors’ duties is critical in understanding the medical field and the current challenges that are experienced. From numerous research studies conducted so far, one can say convincingly that AI has improved disease diagnosis and treatment, prescription of medication to patients, examination procedures, and laboratory testing, which are important aspects of medical analysis.

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As such, although medical health providers were for a long time reluctant to accept AI because this kind of technology had the potential of replacing them, this technology should be embraced fully in the medical field because it assists greatly in improving the performance of medical practitioners.

Background: Types of AI Networks Used in the Medical Field

According to various research studies conducted, there are different types of AI networks that medical practitioners can use in their day-to-day activities. One of the networks, according to Amato, is the artificial neural networks (ANNs). The researchers claim that an ANN has a similar architecture as the human neural because of its ability to learn and generalize. Consequently, the networks have been used in medical research to model non-linear systems with complex or unknown variables. This kind of network is usually formed using nodes or neurons that exist in layers with neurons from different layers connecting together at some point. Input and output layers form the structure of any neural network. It is important to learn that the number of neurons and layers is always dependent on the strength and complexity of the system that is being studied. ANN learning takes place when examples forming a training database are identified. Training helps to estimate the functions of vectors. This intelligence network is important because it has the ability to tolerate noise in the data, thereby allowing for accuracy prediction.

The second AI network that is applicable in the medical field is the fuzzy logic. This network is crucial because it has systems such as the clinical decision support system, MYCIN program, as well as computer-aided diagnosis (CAD), which are important for practitioners. Pannu also considers the pathfinder as a vital system because it has been used in the diagnosis of the diseases of the lymph node. The fuzzy logic reasoning is claimed to be similar to that of human beings because of its ability to mimic how humans make their decisions through “Yes” or “No” answers that are provided digitally. The logic is that a computer is capable of taking accurate input and giving a specific output for “True” or “False” answers, which are equivalent to “No” or “Yes” provided by humans. There has been the assertion that the specific outputs also referred to as fuzzy sets can support quick reasoning by associating loosely similar patterns and, at the same time, providing an avenue for dealing with scientific subjectivity. The ability to deal with the subjectivity is important because the territory has traditionally been ignored in science. Just like the ANN, the fuzzy logic system has the ability to take distorted, noisy, or imprecise input information.

In addition, clinical decision support systems have been reported as important systems in AI because they allow physicians, patients, staff, as well as individuals with person-specific information to enhance health care service provision. Such systems have been reported to come with tools that are capable of improving decision making of clinicians and other healthcare professionals. Computerized alerts sent to patients, as well as to those providing health care services, are notable examples of such tools. Besides, various systems designed to help in decision making have assisted in managing knowledge since they offer support to different clinical processes or use knowledge gathered from the investigation and diagnosis for treatment or long-term care.

MYCIN is another system that was developed to perform human expert level functions or human reasoning. It is claimed that the system is meant to assist physicians in diagnosing or treating bacteremia and meningitis infections because it offers a consultative tool for bacterial infections and infections involving inflammation of the meninges. The diseases that the tool can help to diagnose are fatal and manifest during hospitalization, but it is now asserted that MYCIN has a computer program that aids attending physician in giving comparable advice that a specialist in meningitis or bacterial infections would give.

Pannu claims CAD involves the integration of the idea of image processing, mathematics, computer vision, statistics, and physics into medical decision-making. Therefore, the techniques have found relevance in the examination of structures, disease detection, lesion classification, disease quantification, risk assessment, or physiological evaluation. Various methods used in CAD are designed to maximize retrieval of information by augmenting subjective, qualitative, and quantitative image data.

Further, nonsubsampled contourlet transform (NSCT) occurs when there is a coupling of the nonsubsampled pyramid and nonsubsampled direct filter. The NSCT has both multi-resolution and multidirectional properties. The least squares support vector machines and particle swarm optimization have been helpful population optimization techniques. The neural networks and decision trees are important systems for generating patient classification concerning certain diseases.

Furthermore, Tomar and Agarwal have discussed the K-Nearest Neighbor (K-NN) as an important tool in AI. According to the authors, K-NN can be helpful in determining the relationship between different ailments while the decision tree has been used to test the outcomes and disease class labels. It is also worth mentioning the support vector machine that classifies medical data based on different planes.

Data Mining and Application

Research studies conducted in the recent past have highlighted an increased use of AI in generating data mining for medical purposes. Gallagher says that AI has been used in drug trials to obtain data for drug efficacy in the research that was conducted to gauge the effect of the drug in slowing down the growth of cancer. This report from the British Broadcasting Corporation media claims that scientists are at an advanced stage in their endeavor to fuse computing with medicine in their fight against cancer because of the data that have been obtained from the trials. The research highlighted by the journalists was meant to determine if AI could help in the identification of how a cancerous cell would be turned back to a normal cell. Using AI, it appears that a drug that can reverse the Warburg effect could be in the pipelines for use in the near future. Indeed, the outcomes of the research show that the approach used would lead to the death of the tumors. The journalist quotes the scientists who were interviewed, saying that even though it still early to say if the drug will be efficacious, supercomputing remains important for cancer research.

Similarly, Amato acknowledge the use of AI in generating medical data during biochemical and laboratory analyses, as well as in magnetic resonance. According to the researchers, ANNs have found a wide use in nonlinear systems, especially where there is a need to generate variables in complex situations. Further, Tomar and Agarwal in their research acknowledge the importance of data mining in the management of hospital resources. For instance, the researchers identified the detection of diseases as one of the areas in the medical field where the role of data mining remains critical. Data mining has been helpful in ranking hospitals depending on the types of risks that different hospitals can handle. Finally, McMillan and Dwoskin argue that through AI, it is possible to extract millions of images from various hospitals and keep such data in a common database from where different hospitals can share such data or extract them for use. Specifically, radiologists, dermatologists, and health practitioners would find the images helpful in the course of analyzing images for similar medical conditions. Therefore, while data mining using AI methods continues to advance, there is still an opportunity for researchers to come up with novel ideas and techniques to enhance data mining and application in the medical industry.

Diseases Diagnosis and Listing of Clinical Symptoms

AI is regarded highly in disease diagnosis and listing of clinical signs and symptoms. Of note, there are thousands of human diseases currently, but physicians can remember only a few of such diseases and ailments at any given time. In their research, Bennett and Hauser discuss a type of AI that involves a combination of Markov decision and dynamic network approaches. The two approaches can develop complicated plans after learning from available clinical data using simulation decision paths. The outcome of such simulation can be helpful in diagnosis and symptom listing. Should the design be formulated and designed carefully, this AI framework has the ability to approximate decisions in uncertain or complex environments. This would be a step forward in the near future as most hospitals will be shifting toward machine learning in the provision of personalized medicine. The research by Amato supports the argument presented by Bennett and Hauser regarding the important role that AI plays in disease diagnosis. The researchers noted that doctors currently use AI to list symptoms that can be relied on when conducting biochemical analyses. Specifically, Amato note that ANNs and CAD are two networks that have been used in the early detection of diseases and in the diagnosis of colon and colorectal cancers, multiple sclerosis, gynecological complications. ANN is a thorough method that is not only fast but also adaptive when diagnosing diseases such as thyroid and breast cancers. In fact, Amato note:

A novel, general, fast, and adaptive disease diagnosis system has been developed based on learning vector quantization ANNs. This algorithm is the first proposed adaptive algorithm and can be applied to completely different diseases, as demonstrated by the 99.5% classification accuracy achieved for both breast and thyroid cancers.

Diagnosis and treatment of tumors, bacterial infections, cancers, and congenital heart failure, as well as choosing of medications, have been possible because of AI known as MYCIN. MYCIN is a rule-based system that can diagnose bacterial pathogens. Once a diagnosis has been made using the MYCIN method, the next step is to give recommendations for the appropriate antibiotics to administer to the patient based on the outcomes of the diagnosis. The use of AI in diagnostic sciences is further evident from the research of Deepa and Devi who cite melanosis, breast cancer, osteoporosis, and ulcers as some of the disease conditions that have been diagnosed using the technology that was once despised by medical practitioners. Tomar and Agarwal list diseases such as asthma, pneumonia, lung cancer, cardiovascular and hypertension as having been diagnosed using AI technologies. Moreover, AI has been used to diagnose psychiatric conditions. Finally, Mcmillan and Dwoskin also observe that the international business machines (IBM) have developed an AI network system to diagnose breast and chest problems using specialized mammogram and scans to be developed by the company. Therefore, disease diagnosis is an area where AI plays an important role. In particular, AI technologies can diagnose disease conditions that human beings have forgotten or lack knowledge about, meaning that medical practitioners cannot ignore it.

Use of AI in Imaging

AI techniques have been useful in medical imaging. CAD, which is one of the AI techniques, has been used to generate endoplasmic and tumor images. CAD helps in the analysis of retinal images for patients suffering from diabetic retinopathy. Equally, it is possible to use neural networks to classify images depending on the type of disease. Furthermore, through magnetic resonance imaging, it is possible to segment brain tumors to come up with the most appropriate treatment strategies. Pannu argues that MRI supports the use of computerized analysis to identify mammographic calcification in patients. The argument is anchored in the premise that CAD is currently used as an alternative option by radiologists during the interpretation of images. Various steps that may have to be followed when interpreting images include feature analysis and data classification using various tools, for instance, ANNs.

Medical imaging works successfully when there are many images to use for comparison. Mcmillan and Dwoskin report on the current imaging technology used by the IBM that would revolutionize examinations conducted in hospital rooms. According to the authors, the company intends to store in its database numerous medical images. Even though the corporation has billions of MRI, CT scans, and X-rays images at its disposal currently, the proposed software will help in the addition of more images to the database. This is particularly significant as the focus now shifts to images that would help to identify cancer and heart diseases. The proposed AI technology will provide a reprieve for medical imaging companies that mostly depend on computerized techniques to interpret their images. Likewise, hospitals are bound to benefit from the imageries, especially teaching and referral hospitals where medical research is conducted. The images to be stored in a common database will be shared by hospitals, as well as among health practitioners who analyze images. The technology proposed by IBM has been used by companies such as Google and Yahoo, which reported positive outcomes. Nonetheless, this project is bound to require a large capital outlay. From the arguments presented, imaging is an area that AI needs to exploit further because, currently, there are not enough images to be used in hospitals for diagnosis of diseases and medical research.

The Range of Populations Where the AI Technologies Can Be Applied

The AI techniques proposed so far can be applied in large or small populations. For instance, ANNs can be applied in large populations. The ANNs can process data from many sources, thus managing millions of symptoms of disease combinations. Similarly, systems such as the K-NN, decision tree, and the support vector machine are AI methods that can gather large amounts of electronic data. Such massive data are easily collected using predictive models and then analyzed to determine relevant information that can be helpful in disease diagnosis. In contrast, MYCIN and pathfinder networks cannot be applied in the entire patient population; rather, they can be applied in a small patient population.

Impact of AI Use in the Medical Field

AI will continue to play an important role in helping care providers to deliver personalized care options to patients. Patient-centered care is critical for both the patient and care provider because it ensures that the examinations, diagnoses, imaging conducted, and treatment options selected are geared toward achieving positive outcomes. Personalized care responds effectively to the patient’s genetic changes and helps to harness numerous advances that have been witnessed in computing.

Different AI technologies will result in reduced medical costs. It is highly likely that clinicians will find the technologies appropriate for constant monitoring of patient medical information. For example, the use of an alert system will enable patients to enjoy personalized health care support. The alerts will remind patients when to take medications, chat with the physician, or report abnormal signs and symptoms after visiting a caregiver. Applications that come with options for video chat, call, or sending texts between the patient and the physician are convenient tools that will continue to make AI more acceptable in the medical field. The ability of the systems to predict the possibility of occurrence of diseases will be vital in case the physician or the patient needs to take additional precautionary measures.

The use of ANNs will help to streamline and improve the diagnosis of various diseases. It is worth noting that ANNs possess learning ability; therefore, they can learn with ease from existing examples. Consequently, these networks not only improve disease diagnosis, but they are also flexible in their application. The neural networks can deal with perceptual problems while at the same time helping to model data.

CAD improves the consistency and accuracy of disease diagnosis. An accurate diagnosis means that the physician prescribes correct medication for the patient. False negatives are avoided eventually, and the cases of disease resistance to medication are avoided, as well. This can significantly reduce the time it takes for patients to recover. Moreover, medical costs are reduced while quality is improved in the process. The major beneficiaries include insurance providers who are bound to benefit significantly, when medical costs for patients reduce.

AI is beneficial when it helps to detect fraud. Through data mining, it will become possible to detect fraudulent actions, thereby resulting in further reduction of medical costs. Through the creation of appropriate policies, early detection and warning signs will be easily instituted. It should not be forgotten that the methods would make it easier to provide accurate diagnosis.

Weaknesses of AI Networks/Counterclaim

The methods that are still under trial have limited options for the application. In fact, even when networks show great potential, there is still a need for authentication. Hence, the scope of some tools remains limited. Further, fuzzy methods are likely to support the inclusion of unnecessary data, resulting in false negative or false positive results. Some tools, such as the ones that are used to store data regarding imageries, remain insufficient; therefore, information sharing has not been achieved fully. Moreover, there are currently no clear policy guidelines for the use of applications in making diagnosis. In addition, the networks cannot analyze large quantities of data, which reduces their clustering, dimension, and summation of data. Finally, the major setback is the proprietary weakness and the use of standard approaches for the construction of the data warehouse.


AI networks were initially opposed by medical care providers based on unfounded fears. However, the perceptions appear to have changed in the recent years after the realization that the machines are not meant to take away human functions but to supplement and enhance service provision health care. Among the areas in the medical field where AI has been applied effectively, one can outline disease diagnosis, imaging, and data mining. Therefore, even though AI may not have achieved its full potential, its benefits in the medical field are evident. The techniques not only complement the work of medical practitioners, but they also go beyond human understanding, particularly in disease diagnosis, meaning that AI cannot be ignored in the medical field.

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