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Executive Summary

Artificial intelligence developed by programmers and medical researchers collectively should be used as it has largely benefitted the medical field, allowing for medical procedures to be done more efficiently and with less risk. Artificial intelligence aids medical procedures in being more precise, and ultimately the development of advancements in artificial intelligence. Algorithms could make the implementation of medicine and healthcare much easier, but as of yet, they are still unable to replace actual practitioners due to the fact that many regulations are hard to program and are up to the discretion of individual practitioners. This link shows support towards this. And there is little chance that they will ever be able to do so. Additionally, the aid of artificial intelligence could improve the precision of many medical procedures, such as surgeries. The focus is, however, mostly on diseases such as cancer, nervous system disease, and cardiovascular disease, as well as the research with stem cells and genetics that comes with it. And it’s not just medical professionals that believe artificial intelligence is on the rise. The number of articles written about artificial intelligence specifically has shot up exponentially in the past few years. This link, supports this as it talks about the trend in the number of articles related to artificial intelligence. And last but not least, data collected by researchers in clinical studies in recent years is enough to improve the function of artificial intelligence by a long shot. All of these arguments make it reasonable for the further development and implementation of artificial intelligence in the medical field.

Artificial Intelligence in Medicine (Draft)

Imagine a world in which the precision of artificial intelligence was integrated with the experience of health practitioners to make medicine more effective. Artificial intelligence–or the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages–has, in recent decades, become a popular field of study, and involves the integration of computer programming into various other fields such as medicine. The medical field is defined as: of, relating to, or concerned with physicians or the practice of medicine. It includes nurses, doctors, and various specialists. That being said, artificial intelligence developed by programmers and medical researchers collectively should be used as it has largely benefitted the medical field, allowing for medical procedures to be done more efficiently and with less risk.

The figure above shows the trendline of the number of research articles that have been published about artificial intelligence in healthcare from 2013 to 2017. The number of articles has grown exponentially, reflecting in the increased interest in the role of artificial intelligence in healthcare as well as the growth in technology that allows for new developments to occur.

Algorithms could make the implementation of medicine and healthcare much easier, but as of yet, they are still unable to replace actual practitioners due to the fact that many regulations are hard to program and are up to the discretion of individual practitioners. “We think it’s important to emphasize that these tools are never going to replace clinicians. These technologies will provide assistance, helping care providers see important signals in massive amounts of data that would otherwise remain hidden. But at the same time, there are levels of understanding that computers still can’t and may never replicate” (Strait 2018). Additionally, “defining the qualities necessary for an algorithm to be deemed sufficiently accurate for the clinic, while addressing the potential sources of error in the algorithm’s decision making, and being transparent about where an algorithm thrives and where it fails, could allow for public acceptance of algorithms to supplant doctors in certain tasks. These challenges, however, are worth trying to overcome in order to universally increase the accuracy and efficiency of medical practices for various diseases” (Greenfield 2019). However, some might argue that with the amount that technology is exponentially advancing, it is hard to predict whether it could improve to the point where it could replace health practitioners. But, it is important to note that it is not this simple, for there are many regulations that also must be addressed, and many ethics issues are things that cannot be programmed but must instead be decided by an expert or professional. For example, ethics issues such as whether to save the mother or the baby during childbirth poses a huge issue and is often up to the discretion of the practitioner based on morals and chances of survival.

Also, the aid of artificial intelligence could improve the precision of many medical procedures, such as surgeries. A counter-argument to this could be that the use of artificial intelligence in medicine is unnecessary because for many years, doctors have been performing procedures well enough without it. But it’s also important to note that all fields are constantly evolving, especially fields pertaining to the sciences. Therefore, if there’s a way for doctors to perform procedures with more ease and that simultaneously causes less discomfort to the patient, why should we not take it? Additionally, many kinds of tests are starting to be done with artificial intelligence, and these tests can help diseases be diagnosed earlier so that treatment can begin sooner and the survival rate increases. This is shown by the statement “popular AI techniques include machine learning methods for structured data, such as the classical support vector machine and neural network, and the modern deep learning, as well as natural language processing for unstructured data. Major disease areas that use AI tools include cancer, neurology and cardiology” (Jiang et al., 2017).  Another counter-argument that could be offered is that on top of those issues already discussed, there are many ethics issues that come with the advancement of technology in the medical field, such as genetic engineering. ““The concept of altering the human germ line in embryos for clinical purposes has been debated over many years from many different perspectives and has been viewed almost universally as a line that should not be crossed,” Francis S. Collins, director of the National Institutes of Health, said in response to the news that scientists in China were using gene-editing technology to alter human embryos” (Erickson 2015). “Thus, in summary, genetic engineering is beginning to deliver meaningful products to the market place, proving that it is a viable body of technology on which to base an industry. Its rites of passage serve to drive home the tired lessons that good technical ideas need to be complemented by good management, financial savvy, and an eye to the market to be successful” (Dickson 1984). However, things such as these will never truly have an ethical outcome. Of course, though, this will help many people who may be diagnosed with diseases become healthier, and that is still beneficial to many people.

Lastly, data collected by researchers in clinical studies in recent years is enough to improve the function of artificial intelligence by a long shot. For example, “accurate predictions of sequence repair could allow researchers to computationally predict the precise guide RNAs that will reproduce exact human patient mutations, leading to the development of better research models to study genetic disease” (Yeager 2019). Additionally, “experts in artificial intelligence (AI) are working to bring computers into the clinic. Advances in a technique called “deep learning” help computers to find patterns in massive data sets, which should be very useful in medicine” (Johnson 2017). Some people say that more data should be collected before we let artificial intelligence deal with something as valuable as human lives, though. But there’s a problem with that, since all technology has to start from somewhere. It first has to be implemented for us to be able to study it and know how to improve it. So, it’s a better idea, and more beneficial in the long run, for it to be implemented in certain procedures now so data can be collected. Practitioners could also start with implementing it in relatively safer and easier procedures first, and then moving on to more difficult ones as they collect more data and improve the technology.

The figure above represents where most artificial intelligence and technology goes in terms of research. It is most often used for neoplasms, followed by research into problems such as nervous disorders and cardiovascular diseases.

Artificial intelligence aids medical procedures in being more precise, and ultimately the development of advancements in artificial intelligence. Algorithms could make the implementation of medicine and healthcare much easier, but as of yet, they are still unable to replace actual practitioners due to the fact that many regulations are hard to program and are up to the discretion of individual practitioners. And there is little chance that they will ever be able to do so. Additionally, the aid of artificial intelligence could improve the precision of many medical procedures, such as surgeries. And last but not least, data collected by researchers in clinical studies in recent years is enough to improve the function of artificial intelligence by a long shot. Thus, artificial intelligence developed by programmers and medical researchers collectively should be used as it has largely benefitted the medical field, allowing for medical procedures to be done more efficiently and with less risk.

Outline

Paragraph 1:

  • Artificial intelligence has been on the rise recently, and the medical field has progressed to allow doctors and surgeons to perform procedures that were once thought to be beyond the realm of possibility.
  • Define important terms
    • Artificial intelligence – the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.
    • Medical field – of, relating to, or concerned with physicians or the practice of medicine. It includes nurses, doctors, and various specialists.
  • Thesis: Artificial intelligence developed by computer programmers and medical researchers collectively should be used as it has largely benefitted the medical field, allowing for medical procedures to be done more efficiently and with less risk to the patient and thus saving lives in the long run.

Paragraph 2:

  • First argument: Algorithms could make the implementation of medicine and healthcare much easier, but as of yet, they are still unable to replace actual practitioners due to the fact that many regulations are hard to program and are up to the discretion of individual practitioners.
  • Evidence from sources
    • In fact, medical professionals state that, “we think it’s important to emphasize that these tools are never going to replace clinicians. These technologies will provide assistance, helping care providers see important signals in massive amounts of data that would otherwise remain hidden. But at the same time, there are levels of understanding that computers still can’t and may never replicate” (Strait 2018).
    • “Defining the qualities necessary for an algorithm to be deemed sufficiently accurate for the clinic, while addressing the potential sources of error in the algorithm’s decision making, and being transparent about where an algorithm thrives and where it fails, could allow for public acceptance of algorithms to supplant doctors in certain tasks. These challenges, however, are worth trying to overcome in order to universally increase the accuracy and efficiency of medical practices for various diseases” (Greenfield 2019).
    • Dr. Zhang, a family friend who practices medicine and worked at a research lab previously, stated that “artificial intelligence is able to perform many jobs in the place of humans now, with even more accuracy than many humans do. However, many of the jobs they are able to do are menial, and when it comes to a field as unique as medicine, artificial intelligence is more of a tool than a practitioner.”
  • Counter-argument: With the amount that technology is exponentially advancing, it is hard to predict whether it could improve to the point where it could replace health practitioners.
  • What to say against counter-argument: However, it is important to note that it is not this simple, for there are many regulations that also must be addressed, and many ethics issues are things that cannot be programmed but must instead be decided by an expert or professional. For example, ethics issues such as whether to save the mother or the baby during childbirth poses a huge issue and is often up to the discretion of the practitioner based on morals and chances of survival.

Paragraph 3:

  • Second argument: The aid of artificial intelligence could improve the precision of many medical procedures, such as surgeries.
  • Evidence from sources
    • “Popular AI techniques include machine learning methods for structured data, such as the classical support vector machine and neural network, and the modern deep learning, as well as natural language processing for unstructured data. Major disease areas that use AI tools include cancer, neurology and cardiology” (Jiang et al., 2017).
    • ““The concept of altering the human germ line in embryos for clinical purposes has been debated over many years from many different perspectives and has been viewed almost universally as a line that should not be crossed,” Francis S. Collins, director of the National Institutes of Health, said in response to the news that scientists in China were using gene-editing technology to alter human embryos” (Erickson 2015).
    • “Thus, in summary, genetic engineering is beginning to deliver meaningful products to the market place, proving that it is a viable body of technology on which to base an industry. Its rites of passage serve to drive home the tired lessons that good technical ideas need to be complemented by good management, financial savvy, and an eye to the market to be successful” (Dickson 1984).
    • “We obtained surgical data for 56 (29%) of 192 WHO member states. We estimated that 234·2 (95% CI 187·2–281·2) million major surgical procedures are undertaken every year worldwide. Countries spending US$100 or less per head on health care have an estimated mean rate of major surgery of 295 (SE 53) procedures per 100 000 population per year, whereas those spending more than $1000 have a mean rate of 11 110 (SE 1300; p<0·0001). Middle-expenditure ($401–1000) and high-expenditure (>$1000) countries, accounting for 30·2% of the world’s population, provided 73·6% (172·3 million) of operations worldwide in 2004, whereas poor-expenditure (≤$100) countries account for 34·8% of the global population yet undertook only 3·5% (8·1 million) of all surgical procedures in 2004” (WHO 2008).
  • Counter-argument: The use of artificial intelligence in medicine is unnecessary because for many years, doctors have been performing procedures well enough without it.
  • What to say against counter-argument: But it’s also important to note that all fields are constantly evolving, especially fields pertaining to the sciences. Therefore, if there’s a way for doctors to perform procedures with more ease and that simultaneously causes less discomfort to the patient, why should we not take it? Additionally, many kinds of tests are starting to be done with artificial intelligence, and these tests can help diseases be diagnosed earlier so that treatment can begin sooner and the survival rate increases.

Paragraph 4:

  • Third argument: Data collected by researchers in clinical studies in recent years is enough to improve the function of artificial intelligence by a long shot.
  • Evidence from sources
    • “Accurate predictions of sequence repair could allow researchers to computationally predict the precise guide RNAs that will reproduce exact human patient mutations, leading to the development of better research models to study genetic disease” (Yeager 2019).
    • “Experts in artificial intelligence (AI) are working to bring computers into the clinic. Advances in a technique called “deep learning” help computers to find patterns in massive data sets, which should be very useful in medicine” (Johnson 2017).
  • Counter-argument: More data should be collected before we let artificial intelligence deal with something as valuable as human lives.
  • What to say against counter-argument: But there’s a problem with that, since all technology has to start from somewhere. It first has to be implemented for us to be able to study it and know how to improve it. So, it’s a better idea, and more beneficial in the long run, for it to be implemented in certain procedures now so data can be collected. Practitioners could also start with implementing it in relatively safer and easier procedures first, and then moving on to more difficult ones as they collect more data and improve the technology.

Paragraph 5:

  • Conclusion
  • Summary of key points addressed in each paragraph
  • Recap thesis

Laura Fathauer Reflection

From Laura’s presentation, I learned that user-generated content can largely be made more accommodating to people with various different needs on the internet. What she said about making your ideas reach as many people as possible resonated with me, and I hope to be able to apply that to works that I produce in the future, whether it be just research papers, or a thesis, or graphics, or a computer program. I will use this advice in the future, as previously I had not considered much about things such as accessibility but now I know that it’s important. Additionally, since there is so much work in this age that can be found on the internet, these things become more and more important. In this digital age, it becomes more necessary for us to learn about how to communicate our ideas with those around the world, since that is how information is shared and how everyone remains up-to-date with global events.

Second and Third Argument

Another one of my arguments was that the aid of artificial intelligence could improve the precision of many medical procedures, such as surgeries. This argument is supported by the source Artificial intelligence and the future of medicine, which was written by Strait in 2018. The link to this source can be found at https://medicine.wustl.edu/news/artificial-intelligence-and-the-future-of-medicine/.

A counter-argument to this could be that the use of artificial intelligence in medicine is unnecessary because for many years, doctors have been performing procedures well enough without it. But it’s also important to note that all fields are constantly evolving, especially fields pertaining to the sciences. Therefore, if there’s a way for doctors to perform procedures with more ease and that simultaneously causes less discomfort to the patient, why should we not take it? Additionally, many kinds of tests are starting to be done with artificial intelligence, and these tests can help diseases be diagnosed earlier so that treatment can begin sooner and the survival rate increases.

Yet another one of my arguments was that data collected by researchers in clinical studies in recent years is enough to improve the function of artificial intelligence by a long shot. Since we are continuously learning and new data is continuously being collected, artificial intelligence will only continue to improve, if we let it. This argument is supported by the source Artificial intelligence in healthcare: past, present and future, which was written by Jiang et al. in 2017. The doi for this source is doi: 10.1136/svn-2017-000101.

A counter-argument to this could be that more data should be collected before we let artificial intelligence deal with something as valuable as human lives. But there’s a problem with that, since all technology has to start from somewhere. It first has to be implemented for us to be able to study it and know how to improve it. So, it’s a better idea, and more beneficial in the long run, for it to be implemented in certain procedures now so data can be collected. Practitioners could also start with implementing it in relatively safer and easier procedures first, and then moving on to more difficult ones as they collect more data and improve the technology.

Annotated Bibliography – 3 Additional Sources

Greenfield, D., & Wilson, S. (2019, June 19). Artificial Intelligence in Medicine: Applications, implications, and limitations. Retrieved November 21, 2019, from http://sitn.hms.harvard.edu/flash/2019/artificial-intelligence-in-medicine-applications-implications-and-limitations/.

This source talks about the current possible applications, as well as the limitations, of artificial intelligence in healthcare and the medical field. It also discusses the implications for the future, which are vast in scope due to the exponential way that technology is advancing each and every day (and advancements build upon themselves). The authors are students at Harvard University, and although they’re students, it’s safe to assume that they are still credible as a source of information since they are studying biology. I chose this source because it talks about current applications of artificial intelligence in healthcare and how artificial intelligence can be used to help surgeons and doctors. The source is relevant as it was written in 2019, which means it reflects new data and research. I found this source by doing a Google Scholar search using the term “artificial intelligence”. This is hosted on the Harvard University blog.

Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., … Wang, Y. (2017). Artificial intelligence in healthcare: past, present and future. Stroke and Vascular Neurology2(4). doi: 10.1136/svn-2017-000101

This source talks about how the use of artificial intelligence in healthcare and medical practices has evolved throughout history. The authors are researchers in several top Chinese universities, so it is safe to assume that they are knowledgeable about the topic. This, therefore, makes the source reliable. I chose this source because there are discussions within the text of the future of artificial intelligence in healthcare, which is a topic that I am particularly interested in delving deep into for my paper. The source is relevant as it was written in 2017, which makes the information rather recent. Additionally, it discusses the past, present, and projected future of artificial intelligence, so the information is not outdated but instead a prediction based on past and present trends. I found this source by doing a Google Scholar search using the term “artificial intelligence”. This is hosted in a journal called Stroke and Vascular Neurology.

Strait, W. J. E. (2019, January 8). Artificial intelligence and the future of medicine. Retrieved November 21, 2019, from https://medicine.wustl.edu/news/artificial-intelligence-and-the-future-of-medicine/.

This source talks about how advancements in artificial intelligence and the technology associated with it could lead to many changes to the future of the medical field. The author is a writer at the Washington University in St. Louis, and even though they are a freelance writer first and foremost, their writing mainly revolves around biology and medicine, so I believe that they are still a credible source. I chose this source because it talks about the future artificial intelligence in the medical field and how the scope of medicine can be changed entirely by developments in technology. The source is relevant as it was written in 2018, so it’s recent and most, if not all, of the material shouldn’t be outdated. This is hosted on the Washington University blog.

TradeMark Reflection

I had originally thought that trademark laws were very rigid and that it was illegal to use or download anything without the permission of the original author or without giving them credit/money. However, TradeMark’s presentation challenged my thinking by teaching me that while copyright rules do exist and are quite strict, copyleft rules also exist that allow users the freedom to use and distribute works as they want under the condition that those works, once distributed, offer the same freedoms to other users. This advice helps me in that I now know that while citing sources remains important, using content freely is not always an issue, and that works protected by copyleft laws allow me to use, modify, and distribute them freely as well. Specifically, I am more aware of the legal issues surrounding the use and distribution of online content and media, which I will often need to use as a student. Therefore, I can rest at ease knowing that copyleft laws exist that will allow me to use media for my own academic works both now and in the future.

Example Argument

One of my arguments was that algorithms could make the implementation of medicine and healthcare much easier, but they are still unable to replace actual practitioners. The source Artificial Intelligence in Medicine: Applications, implications, and limitations, written by Greenfield in 2019, supports this argument. The link to this source can be found at http://sitn.hms.harvard.edu/flash/2019/artificial-intelligence-in-medicine-applications-implications-and-limitations/.

A counter-argument to this could be that with the amount that technology is exponentially advancing, it is hard to predict whether it could improve to the point where it could replace health practitioners. However, it is important to note that it is not this simple, for there are many regulations that also must be addressed, and many ethics issues are things that cannot be programmed but must instead be decided by an expert or professional. For example, ethics issues such as whether to save the mother or the baby during childbirth poses a huge issue and is often up to the discretion of the practitioner based on morals and chances of survival.

Annotated Bibliography – Source 4

Erickson, B. E. (2015, June 29). Editing Of Human Embryo Genes Raises Ethics Questions. Retrieved November 13, 2019, from https://cen.acs.org/articles/93/i26/Editing-Human-Embryo-Genes-Raises.html.

This source discusses more about the ethics issues involved with genetic engineering and genome editing, and raises some specific questions surrounding the issues. The author is a member of the American Chemical Society, so they are therefore credible on the topic since I assume they have experience with the chemistry of what goes on in the body, as well as the chemistry behind genome editing. I chose this source because the specific questions it raises against genetic engineering can be used for my counterargument in my paper. The source is relevant as many of the legal concerns are still applicable today. I found this source by doing a Google Scholar search limiting the keywords to words such as “genetic” and “artificial”. This is hosted on Chemical & Engineering News.

Annotated Bibliography – Source 3

Johnson, W. G. (2017, October 26). Where Genome Editing and Artificial Intelligence Collide. Retrieved November 13, 2019, from https://cspo.org/where-genome-editing-and-artificial-intelligence-collide/.

The source discusses the ethical and legal issues that come with the development of the artificial intelligence and genetic engineering fields. The author is credible since he has a masters in Science and Technology Policy and is currently pursuing a career in law. This means that they are a knowledgeable source on both the fields of artificial intelligence as well as the ethics issues regarding it. I chose this source because it offers counterarguments to the use of genetic engineering, if uncontrolled. This source is relevant because it was written in recent years and argues about future implications of genetic engineering. I found this source by doing a keyword search using the terms “genetic” and “artificial” in Google Scholar and it is hosted by As We Now Think.