Jennifer Lynch
Prof. Bob McGovern
English 122
4 May 2015
Paper 3
Recent advancements in artificial intelligence have proved to be very beneficial, especially in assisting doctors in making medical decisions regarding patient health. Medical practitioners are faced with the challenge of collecting and analyzing large amounts of patient data and then utilizing applicable knowledge to solve complex patient problems. Combining artificial intelligence with human clinicians in the medical field facilitates doctors in developing an accurate diagnosis while improving both the cost and quality of health care (Corwin).
Modern medicine’s advance depends upon constant improvement. With the rapid growth of medical knowledge, physicians find themselves without enough time to dedicate to each case. Elliot Siegel, a professor at Maryland University Medical School, says, “Watson could prove valuable one day in helping diagnose patients by sourcing journals and other medical literature that physicians often don't have time to keep up with” (qtd. in Robertson). This lack of time opens the opportunity for new computer tools to help organize, store, and collect appropriate medical data needed by practitioners. Advancements in artificial intelligence can fully transform electronic medical records into software that can support doctors in delivering patient data analysis in rapid time (Corwin). The development of these systems allow doctors to quickly determine if an anticipated medicine or medical procedure will cause the patient greater harm than good (Hernandez).
There have been a variety of systems created over the years in order to assist users and provide early diagnosis to prevent serious illness. Early studies in medical intelligence developed systems such as MYCIN, CASNET, and ICHT. CASNET was developed in the 1960’s and was a general tool for diagnosing and treating a variety of diseases, particularly glaucoma. MYCIN was developed in the 1970’s to diagnose certain antimicrobial infections and to suggest possible treatments. Lastly, ICHT was developed to reduce children mortality rate by monitoring the patients weight, immunization dates, development milestones, and nutrition in order to find the best treatment. Recent medical systems have improved the development of those mentioned by monitoring patients more closely and accessing vast amounts of data to provide more than just a list of possible treatments, but a faster and more accurate diagnosis. Gradually, physicians and hospitals around the country are exercising these systems to combine patients’ health data with the abundance of material available in public databases and journals to help discover more exact treatments.
Doctors at Vanderbilt University Medical Center in Nashville and St. Jude’s Medical Center in Memphis receive alerts within their individual patients’ electronic medical records advising them of suitable treatments for that patient. The alerts tell them, for instance, when a drug might not work for a patient with certain genetic traits or if there is a better treatment option available. Electronic medical records are used on patients to help computers predict which patients are likely to need certain medications in the future. The system uses any previous tests the patient may have had and history of medical issues to decide which medication may be needed. For example, some people cannot break down certain drugs, such as Plavix, an anti-blood clot medication. According to Dr. Joshua Denny, a researcher at Vanderbilt University Medical Center, the system will warn doctors in such situations to give patients that are likely to need such medication a genetic test to see whether it is safe. If not, the system suggests alternative drugs for the physician to consider.
Two researchers at Centerstone Research Institute ran patient clinical data through their artificial intelligence system that develops complex treatment plans and compared the results traditional treatment.