Evolution of AI and ML in Medical Infrastructure

Version	Summary	Created by	Modification	Content Size	Created at	Operation
1		Kamlesh Kumar	--	2387	2023-01-31 05:14:06	\|
2	format correct	Conner Chen	+ 2 word(s)	2389	2023-01-31 08:43:45	\|

This entry is adapted from the peer-reviewed paper 10.3390/healthcare11020207

People in the life sciences who work with Artificial Intelligence (AI) and Machine Learning (ML) are under increased pressure to develop algorithms faster than ever. The possibility of revealing innovative insights and speeding breakthroughs lies in using large datasets integrated on several levels. However, even if there is more data at our disposal than ever, only a meager portion is being filtered, interpreted, integrated, and analyzed. Both an increase in the learning capacity and the provision of a decision support system at a size that is redefining the future of healthcare are enabled by AI and ML.

medical infrastructure healthcare infrastructure artificial intelligence

1. Introduction

Artificial Intelligence (shown in Figure 1) was initially introduced in the medical sector in 1976 when a computer algorithm was used to determine the reasons for intense abdominal pain ^[1]. From the first healthcare implementation of Artificial Intelligence (AI) to today, numerous applications of AI have been introduced to enhance the strength and overcome the shortcomings of available medical infrastructure. These implementations include assistance in disease detection, like diabetes detection or cancer detection; enhancement of pathology classification, such as classification of radiology scans and outlining electrocardiogram qualities for cardiac study ^[2]; and forecasting illnesses with algorithms based on Machine Learning (ML) and Deep Learning (DL) developed to solve problems such as the pandemic of COVID-19 ^[3]^[4], serving as an epitome. However, despite the healthcare industry’s considerable investment in technological advancements, its deployment and integration in healthcare are still in their preliminary stages ^[5]. Workforce scarcity and exhaustion, and the transition to long-term illness care, are among the most significant concerns in healthcare. Thus, AI can significantly enhance the healthcare infrastructure through its extensive applicability.

Figure 1. Supervised and Unsupervised machine learning with Convolutional Neural Network (CNN), Recurrent Neural Neural Network (RNN), Generative Adversarial Network (GAN) as branches of Deep Learning.

AI is revolutionizing medical infrastructure significantly in diagnosing various diseases, using medical imaging from various available medical imaging formats like- X-rays, MRI, CT, etc. AI can easily detect diseases related to the skin, lungs, organs, and viral issues. For instance, some skin diseases include skin cancer, acne, and rashes. Early identification of such skin illnesses can prevent critical future problems. Furthermore, in this direction, researchers like- Shoieb et al. ^[6] classified skin cancer using available data consisting of cancer images. Their results showed a considerable increase in skin diagnostic accuracy and precision compared to earlier studies. Zaher et al. ^[7] and Charan et al. ^[8] presented such a model to detect breast cancer using radiology scans. Moreover, like skin and breast cancers, lung cancer is amongst the deadliest ailments across the world ^[9]^[10] that causes 7.6 million yearly deaths worldwide ^[11]. Moreover, early detection of such a deadly disease is the only possible cure to reduce this number ^[12]. Many researchers ^[13]^[14]^[15]^[16]^[17] have proposed AI and ML-based approaches for predicting lung cancer using various sources. Apart from these applications, researchers have used AI for the detection of tumor ^[18], tuberculosis ^[19], and even COVID-19 diagnosis ^[20] as well, mainly using chest X-rays. Medical imaging for disease diagnosis and prognosis is widely accepted and increasing with boundless expectations and improvements in conventional medical infrastructure.

The imaging data is machine-readable, allowing the ML and DL algorithms to be run after adequate preprocessing or quality check steps. Moreover, a substantial chunk of healthcare data, including clinical laboratory reports, physical examinations, discharge summaries, and operation notes, usually remains narrative, which would be amorphous and inaccessible to computer algorithms. In this situation, Natural Language Processing (NLP) aims to gather relevant data from the available chunk to support clinical judgments ^[21]. Based on existing records, NLP uses text processing to define disease-related phrases in medical documentation ^[22]. Subsequently, keywords are selected after assessing their influence on categorizing normal and abnormal instances. For example, Miller et al. ^[23] employed NLP to track undesirable events in the laboratory environment. In addition, NLP pipelines can aid in illness detection. This technology has also been used for detecting various disease-related factors for cerebral aneurysms using clinical notes ^[24] to distinguish normal individuals from patients suffering from cerebral issues.

Moreover, Afzal et al. ^[22] used NLP to extract peripheral arterial disease-related keywords from clinical narratives. These were then utilized for differentiation between peripheral arterial disease and normal patients. Not only to collect documentation about disease-related information but NLP is being explored to learn various suicide factors ^[25] from suicide notes by developing a vocabulary or language-specific database. Moreover, this branch of Artificial Intelligence is utilized for evaluating mental illness ^[26], understanding the clinical workflow ^[27]^[28], classifying medical prescriptions ^[29], forecasting patient predilection ^[30]^[31], predicting risk and stratification of a patient ^[32], making decision support system ^[33], and question answering ^[34]. Juhn et al. ^[35] have also introduced an autonomous system that can significantly reduce the burden of medical triage by collecting patient data and understanding it with NLP to help the patient while choosing a consultant and completing other procedures, which usually take a long time in any hospital building.

Robotics focuses on designing and developing robots. When combined with AI, the result is an intelligent machine that can be taught to undertake complicated processes requiring much thought and continual learning. Consequently, a further branch of AI is interested in educating a robot to interpret the world in predicated but generic ways, control things in intractable surroundings, and communicate with humans. Robots that may undertake complex surgical treatments, such as minimally invasive and surgeon-less surgeries, are known as “Surgical Robots”. The systems represented ^[36]^[37] are the gold standard of care in many laparoscopic operations, with approximately a million operations performed each year. Robotic surgery enhances the effectiveness, precision, and reliability of surgical operations allowing quicker recovery and better patient outcomes. Apart from surgical tasks, in healthcare, there are several duties related to management. The application of AI in this domain has less adaptability than acute services, but it can deliver substantial productivity. It is necessary for hospitals because, for instance, a US nurse spends an average of 25% of her job tenure on administrative tasks ^[38]. This aim is most likely connected to robotic process automation technology. It is used in various medical systems, such as user registration, medical documentation, payment flow administration, and clinical record-keeping ^[39]^[40]. Besides patient interactions, mental well-being, telemedicine, and chatbots are often used in other medical contexts.

Research and development are some of the most critical areas, and boosting these areas can significantly strengthen healthcare infrastructure. For example, machine (and deep) learning algorithms have been used in a variety of drug discovery processes, including physio-chemical, poly-pharmacology, drug repositioning, quantitative structure-activity relationship, pharmacophore modeling, drug monitoring and revealing, toxicity prediction, ligand-based virtual screening, structure-based virtual screening, and peptide synthesis activities ^[41]. In addition, pharmacogenetics and molecular biomarker technologies may forecast drug efficacy and medication reactions within subjects, essential to precision medicine progress ^[42].

A significant number of studies ^[43]^[44] conducted in revolutionizing the conventional drug design include DeepMind at Google and AlphaFold, a tool based on AI, trained on protein binding domain spatial information to estimate the multi-dimensional shape of a protein from the sequence of amino acids. AI has become an effective tool in today’s technology because it saves time and money. Such rapid discovery and development of drugs can save millions of lives in critical conditions like a pandemic, which can be defined as an explicit strengthening of overall infrastructure by reducing overall development costs with increased drug efficacy ^[45]^[46]^[47].

Furthermore, supplying incorrect dosage is one of the hackneyed issues in this sector that not only causes the loss of millions of dollars but also weakens the whole infrastructure by increasing the mortality rate with undesired and deadly side effects ^[48]. With the rise of AI, numerous scientists are turning to ML and DL techniques to identify optimal medicine dosages. For example, Shen et al. ^[49] created an AI-based system called AI-PRS to discover the best medication doses and combinations for HIV treatment using antiretroviral therapy. Julkunen et al. ^[50] also created comboFM, a unique ML-based tool for determining optimal medication coalescing and dosing in pre-clinical investigations such as cancer cells. CombinationFM uses factorization machines—a machine learning framework to analyze multi-dimensional data and discover optimum medicine combinations and doses. Xue et al. ^[51] have also identified a suitable bioactive agent and inspected the drug delivery.

As discussed above, AI has become an expert in many stages of drug distribution and optimization. Studies also show how AI can further help in rapid discoveries and development of drugs by working on various stages like predicting interactions between proteins and their foldings ^[52], ligand and structure-base virtual screening ^[53]^[54], quantitative structure-activity relationship modeling and drug re-purposing ^[55]^[56], estimating physicochemical properties and bioactivity ^[57]^[58], toxicity and mode of action prediction of the compound ^[59]^[60], recognition of molecular pathways polypharmacology ^[61]^[62], de novo drug designing ^[63], pharmaceutical manufacturing and clinical trial design ^[64], and related ones ^[65]^[66], to various crucial, even may be incurable, diseases with its unbounded intelligence and memory power on 0.9-micron thick silicon bridges, also known as memory chips. All these studies show how AI enhances drug research and development for strengthening medical infrastructure economically and in terms of rapid processing.

2. Evolution of AI and ML in Medical Infrastructure

As shown in Figure 2, there are numerous possible applications of AI and ML algorithms to develop efficient tools to strengthen the healthcare infrastructure. Over the last five decades, AI has dramatically impacted the medical infrastructure. The scope of AI and ML applications has increased, opening the doors to individualized medicine rather than algorithm-based treatment. Predictive models that predict illness, treatment responses, and even preventive medicine in the future may be developed using such models ^[67]. AI may strengthen the healthcare infrastructure by improving diagnostic accuracy, clinical operations and workflow, procedure accuracy, treatment monitoring, and overall patient satisfaction. The evolution of AI and ML in medicine is detailed in the following timeline.

Figure 2. Applications of AI and ML in Medical Infrastructure.

The late nineteen seventies were driven by a perceived limit of AI, which increased till the early 1970s, driven by high expenses in establishing and sustaining an expert database of information in digital forms. Nevertheless, while reluctantly drawing public attention, many researchers continued their studies in this field with mutual collaborations. As a result, in 1971, Saul Amarel of Rutgers University began working on his study on applications of computers in bio-medicine. In 1973, Stanford University also made significant efforts to enhance communication strength among many universities to focus on clinical and biomedical studies ^[68]. Rutgers University also funded an AI workshop in 1975 to spread awareness about its applications towards strengthening medical infrastructure ^[69].

Further, a causal-associational network ^[70] was used to construct a glaucoma consultation tool, one of the prototypes to show that AI might be used in medicine. This system comprises model development, a database, and consultation. This model can apply certain illness information to individual patients and provide treatment suggestions. Another system called MYCIN, an AI system that uses “backward chaining” was created in the early 1970s ^[71]. Physicians may enter patient information into MYCIN to get a list of probable infections for the system to offer antibiotic treatment alternatives tailored to each patient’s weight.

The University of Massachusetts launched DXplain in 1986 as a decision support system. This software generates a list of diagnoses based on a patient’s symptoms. An electronic medical textbook provides extensive explanations of illnesses and links to supplementary resources. It could offer an analysis of 500 different diseases with its early versions. Later on expanded to more than 2400 disorders ^[72], which can help the healthcare sector to collect important information about many crucial diseases from a single place that helps to escalate many processes of research and developments. At the end of the 1990s, accelerated beliefs in machine learning capabilities, notably in the medical field, helped pave the way for the contemporary era of AI in healthcare infrastructure, coinciding with the aforementioned technical advancements.

Another system, similar to DXplain, Watson, was built by IBM in 2007, a question-answering open-source system that earned first place on Jeopardy’s television show in 2011. Unlike traditional systems, using reasoning in forward-backward methodologies or hand-crafted rules, various searches along with NLP were used to analyze unstructured content and find probable answers ^[73]. Using this method was more convenient, and it was also less expensive and simpler to maintain.

Medical records of patients, including other electronic resources, helped to use technologies like DeepQA to provide professional medical suggestions and related information. It provided new opportunities for making therapeutic decisions based on evidence ^[74]. The binding of RNA proteins was effectively identified by Bakkar et al. ^[75] using IBM Watson in 2017. Digitized medicine became more accessible due to this impetus, combined with enhanced computer hardware and software applications. Natural Language Processing has also revolutionized chatbots by allowing them to engage in meaningful conversations. In 2011, a virtual assistant known as Apple’s Siri used this technique. Amazon also used a similar technique for its virtual assistant, called Alexa. Pharmabot and Mandy are chatbots established in 2015 and 2017 to help young patients and their parents better understand their medications ^[76]^[77].

In image processing, convolutional neural networks (CNNs) are widely used for feature detection and learning. To develop specialized filters, CNN uses several layers that evaluate an image and look for certain patterns. Several CNN algorithms, such as Le-NET ^[78], AlexNet ^[79] (shown in Figure 3), VGG ^[80], GoogLeNet ^[81], and ResNet ^[82], are now readily accessible. Such models are useful for medical image analysis and working in many other domains to strengthen the overall infrastructure by entitling every component of the healthcare sector. For example, MetaAI is one of the major research organizations working in the direction of utilizing AI and ML algorithms for more generalized purposes, like Computer Vision, Conversational AI, Integrity, Natural Language Processing, Ranking and Recommendations, Systems Research, Speech and Audio, Robotics, and Graphics- MetaAI and other big tech companies, like Google, Amazon, and Microsoft, work in these fields with enormous resources. One can find their research in various healthcare domains, for example, Google’s research on disease detection in eyes using external photographs ^[83], and Microsoft’s research in the biomedical natural language field processing ^[84]. These advanced researches are dedicated to healthcare because they validate million-size datasets and their diversity.

Figure 3. AlexNet architecture.

The analysis presented above shows the significant development in AI and ML-related research and its active utilization in healthcare infrastructure and its enhancements in every possible manner, with the employment of such advanced technologies that enable machines to think intelligently. Therefore, AI and ML’s role is to empower medical infrastructure from its roots by following or enhancing the fundamental requirements. Furthermore, the involvement of AI and ML ensures precision and safety simultaneously without breaking any ethical substance.

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Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :

Kamlesh Kumar

Prince Kumar

Dipankar Deb

Mihaela-Ligia Unguresan

Vlad Muresan

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Update Date: 31 Jan 2023

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1. Introduction

2. Evolution of AI and ML in Medical Infrastructure

References