In this fast paced world, new innovations to provide solutions to prevalent problems that people are faced with constantly are being released and used, especially in the medical world. Without the assistance of technology and interdisciplinary fields, healthcare would not be as efficient or effective. Over the past decade, many new areas of study have emerged that may be key to helping us cure rare or currently incurable diseases. Of these new fields, artificial intelligence has been one of the most innovative and progressive.
Artificial intelligence (AI) will continue to play a huge role in improving approaches to scientific problems, and making technological advances because it increases the capacity to register and analyze information. With this analytical capacity, artificial intelligent technologies can improve tools accessible to patients outside of hospitals and clinics, as seen through applications such as Augmedix. that have the ability to learn lifestyle patterns of users. AI can also contribute to the the detection of health risks and can alert medical professionals about diseases (Lexton, 1-2). Through the use of computer programs, medical professionals will be able to approach problems such as cancer or cardiovascular disease more efficiently and more effectively, which will overall improve the ability to combat the biggest questions in science and medicine today.
Although the concept of artificial intelligence, defined as the intelligence exhibited in machines (Artificial Intelligence Will Redesign Healthcare), seems foreign and straight out of a science fiction movie, it is already a huge part of every aspect of life. AI technology today is commonplace with the increasing integration in, such as smart phones andor computers. Artificially intelligent devices can not only exhibit human thoughts, but can emulate human actions as well. This concept was instigated by an Alan Turing, an English computer scientist, in the 1940s (Alan Turing Biography). The basis of the test is to examine if a computer program or machine can truly imitate human actions. A human administrator has to interpret responses from a computer and another human. If the human administrator is unable to discern the difference between the computer and human examiner, then the computer is deemed artificially intelligent (Yampolskiy, Turing Test as a Defining Feature of AI-Completeness). With this technology, concepts of humanlike robots as healthcare professionals or patients having the ability to utilize a computer program to enter symptoms for a certain condition and receive diagnosis become possible. These ideas can not be made possible without the large data storage capacities of artificial intelligence (Yampolskiy, Turing Test as a Defining Feature of AI-Completeness). This magnitude of datae storage allows diagnostic images with large amounts of data or unstructured sets of data will be able to be interpreted, which is vital for healthcare provider’s ability to produce the most accurate analysis of their patients’ conditions.
Artificial intelligence can be divided into two categories: machine learning and natural language processing (Roberts, Assessing Intelligence: Past, Present, and Future). Machine learning can predict results of diseases and has ability to construct complex algorithms to derive information from data (Roberts, Assessing Intelligence: Past, Present, and Future). Any effective form of artificial intelligence must incorporate machine learning to be able to handle the large quantities of data (Roberts, Assessing Intelligence: Past, Present, and Future). Machine learning technologies can also be used for analyzing the efficacy of certain treatments by interpreting medical data. Programs that utilize artificial intelligence and machine learning organize information about age, gender, disease history, and many other medical characteristics that are derived from clinical notes. In a traditional medical setting, clinical notes have to be manually inputted into spreadsheets and hospital data bases. Through the use of artificial intelligence and machine learning, doctors will no longer have to manually input and interpret mass amounts of patient data (Roberts, Assessing Intelligence: Past, Present, and Future). In addition to this, they can benefit from having a vast amount of medical data at their disposal for future diagnosis or prescriptions.
In tThe other category of artificial intelligence, which is natural language processing, a system that interprets information from large unstructured data sets, often seen in clinical notes, has very similar applications as machine learning (Roberts, Assessing Intelligence: Past, Present, and Future). Technologies that utilize natural language processing in artificial intelligence can be programed to self correct and build on past knowledge to minimize errors that can be made by human practitioners (Roberts, Assessing Intelligence: Past, Present, and Future). Similar to machine learning, natural language processing systems can analyze large data quantities that can then be incorporated into medical assessments. With advanced tools such as this, healthcare providers can begin to specialize technologies to suit their specific needs. This can include supplying updated medical information from journals or clinical practice or to get a more accurate diagnosis. An example of artificial intelligence being utilized to get more accurate diagnosis can be observed in its ability to interpret data sets to identify early detection for cancers, stroke, and many other deadly diseases.
Concepts such as Big Data or the Internet of Things are also big components of artificial intelligence in relation disease detection and prevention, as well as other medical applications. Big Data is simply defined as an incredibly large and complex data set that allows medical professionals to analyze patterns and trends (Bairong, Healthcare and Big Data Management). This concept is relatively new, but it is already largely impacting medical fields such as vaccine development, disease pattern research, and or drug improvement. A, and as scientists further delve into the potential and possibilities of Big Data, they will be able to use it to address a multitude of problems that have faced the medical community for years. Through the use of Big Data, scientists are able to build better profiles of patients using computers, which then in turn improves their ability to treat and diagnose diseases. Using this data, computer scientists can create models, algorithms, or programs that will simulate the evolution or process of certain diseases; for example, there are now smart phone apps that can help diagnose skin conditions (The Future of Big Data, Artificial Intelligence, and the Internet of Things). This will have a large impact because one of the most significant restraints in medical research can be the lack of information and understanding of the nature of certain diseases. Big Data can also contribute by giving scientists a deeper understanding of how a certain disease works and allow them to generate and test more effective cures (The Future of Big Data, Artificial Intelligence, and the Internet of Things). This entire process will work to help cure diseases that were once incurable and eliminate questions about certain conditions, contributing to enhancing knowledge in healthcare fields. For example, a major health issue that can be aided by Big Data is Alzheimer’s disease and memory loss. A key component to developing adequate treatments as well as early diagnosis for Alzheimer’s is finding biomarkers, which are substances in a body that can indicate the presence of a certain disease or condition (Shen, 56). Through the use of large amounts of data, medical researchers can more easily find biomarkers, therefore being able to more effectively treat Alzheimer’s (Shen, 56). Many hospitals, as well as independent research facilities that look into biomarkers in relation to diseased involving mental deterioration amassed large amounts of data. Despite having large quantities of data, a majority of the data is contained to each individual institute, and is not often shared. This method of research often restricts knowledge and advancements of research (Shen, 58). As a result of this, research facilities look to having uniform data platforms to combine data to make it accessible to all research institutes, allowing a more effective research and data generation process (Shen, 58). With the incorporation of all data found in research studies, it becomes easier for medical professionals to treat those who suffer from diseases such as Alzheimer’s, as well as many others.
In addition to Big Data, The Internet of Things is a network of devices that is also a large component of artificial intelligence, and can have salient uses in healthcare. The Internet of Things, which is intertwined with the concept of Big Data, is a complex of different appliances and technologies that are found in ordinary object such as microwaves or cars that are utilized to send and obtain data that becomes Big Data (The Future of Big Data, Artificial Intelligence, and the Internet of Things). As seen with Big Data, The Internet of Things has the ability to collect mass amounts of data, which can have very advantageous medical implementations. Due to the vast number of sources of data collection, The Internet of Things enables doctors to have the ability to more accurately diagnose a patient. The Internet of Things also allows many manual tasks to become automated due to its efficient data collection methods (Costa, Internet of Health Things: Toward Intelligent Vital Signs Monitoring in Hospital Wards). Nurses often have to spend a majority of their time writing clinical notes and putting those notes into spreadsheets. With the incorporation of The Internet of Things, the process of collecting vital signs could potentially become computerized, giving nurses the ability to see and spend more time with patients, improving the quality of the healthcare system (Costa, Internet of Health Things: Toward Intelligent Vital Signs Monitoring in Hospital Wards).
Many large companies are already utilizing these artificial intelligence and data mining technologies, interpreting the data, and using them to further medical tools or sell to medical companies. Some examples of these companies include Berg Health, Google Deepmind Health, and Medical Sieve. Berg Health looks at disease data and history to try to understand what qualities of people can be the determining factor of whether or not they can survive diseases (BERG). Then, they take this data to attempt to enhance current treatment tactics or establish new methods of treatment (BERG). This company uses artificially intelligent technologies in coordination with patient’s biological data to discover deviations between healthy and disease prone environments, aiding the development of drugs and diagnosis in healthcare (BERG). Google Deepmind, originally Deepmind was a London based company that did research into artificial intelligence technologies that was bought out by Google. Research into artificial intelligence is crucial to advancements in medical technology as seen by research initiatives such as Medical Sieve (Deepmind). This project aims to pair cognitive technologies with artificial intelligence to advance clinical understanding in radiology and cardiology (IBM Research People and Projects). This method of disease research will be seen more often in many other companies, as artificial intelligence is the path into the future of improving healthcare.
While having many benefits, artificial intelligence presents certain ethical issues. A major question is the elimination of jobs(Top 9 Ethical Issues in Artificial Intelligence). When scientists are able to develop robots that can imitate human thought, certain jobs are often replaced. This becomes a problem especially in the healthcare fields, as doctors could possibly become replaced with computers. If doctors were to be replaced by computers, a question of the patient’s best interest would arise. Doctors are often faced with many ethical and moral decisions concerning a patient’s health. If an automated healthcare provider were to be faced with the same ethical decisions, it could become a serious issue of principle in healthcare (Luxton, 255). An important aspect of medical careers is the interaction between the patient and the doctor. A computerized doctor would be able to give the proper diagnosis, but it would also undermine the importance of the human connection that is so significant in healthcare.
Oftentimes, in clinical settings, the process of diagnosing conditions and prescribing medications is a discussion between multiple doctors and professionals. Healthcare workers that specialize in many different aspects of the field join to debate the course of action that would be most beneficial for a patient. In an article in the New England Journal of Medicine, Dr. Danton Char states that “The use of machine learning in complicated care practices will require ongoing consideration, since the correct diagnosis in a particular case and what constitutes best practice can be controversial. Prematurely incorporating a particular diagnosis or practice approach into an algorithm may imply a legitimacy that is unsubstantiated by data”(Implementing Machine Learning in Health Care — Addressing Ethical Challenges). The journal also discusses the potential of racial or socio-economic biases possessed by humans being incorporated into medical algorithms. This could raise a large ethical problem because it allows prejudice and bias to integrate itself into the hardware of hospitals and healthcare fields, and places the lives of the patients into those who are coding the programs.
Race based biases are already evident in healthcare. If Algorithms are programed to account for race based genetic differences, it is a possibility that the programing could be a gateway into more bias diagnosis than seen in hospitals or clinics that do not utilize artificially intelligent technologies (Implementing Machine Learning in Health Care — Addressing Ethical Challenges).
Another issue arises through keeping the computer programs secure (Top 9 Ethical Issues in Artificial Intelligence). Hackers have already been able to established many advanced tactics to break into highly safeguarded computer programs (Top 9 Ethical Issues in Artificial Intelligence). It becomes a large safety concern for artificial intelligence programs that store confidential medical information of patients, increasing the necessity of cyber security. When incorporating artificial intelligence into healthcare, it is necessary to take necessary precautions to protect data programs. As these technologies begin to permeate the medical world, policy makers and computer programmers will be faced with additional stresses and strains as to how to approach these issues. Many ethical issues already face the healthcare world, involving research with stem cells or cloning. Despite ethical issues that are riddling the healthcare world, it is necessary for the advancement of technology and science to partake in this research.
With the assistance of artificial intelligence technologies, medical professionals can begin to expand their realm of care. Artificial intelligence, paired with machine learning, natural language processing, Big Data, and the Internet of Things will be a driving force in the advancement of future healthcare practices. Through these technologies, hospital systems will be more efficient, as artificial allows for large scale data storage and more systematic clinical note taking. In addition to this, artificial intelligence will allow for applications and online forums that patients can utilize to discuss their disease symptoms, and receive the necessary diagnosis. This type of technology is essential for areas without easy access to healthcare, and will revolutionize the healthcare system. With an increased efficiency in hospitals as well as easier access to healthcare as a result of artificial intelligence, will come significant improvements and advancements to healthcare to create a better future world.