Artificially Intelligent Machine and Patient Privacy

Artificially Intelligent Machine and Patient Privacy.

Artificially Intelligent Machine and Patient Privacy

Over the past years, scientists have been trying to apply technology in medicine. This seems to have been successful if we consider intelligent computers, which have the ability to store huge amount of information and also assisting medical personnel in various tasks such as diagnosis (Hernandez, 2014). Artificial intelligence in medicine has greatly revolutionized this discipline but this benefit is accompanied with certain setbacks. This paper will, therefore, reflect on the impact of using artificial intelligence to analyze medical information on patient’s privacy.

Despite the fact that artificial intelligence is growing in rapidly and its medical applications broadening as days pass by, computers will never surpass human brain and therefore doctors cannot be substituted with these intelligent machines (Kononenko, 2001). However, these machines are able to perform certain tasks that human beings cannot be able to do therefore creating their necessity. For instance, intelligent computers can be able to store huge amount of data and equally provide quick retrieval(Brumen, Hericko, Sevcnikar, Zavrsnik&Holbi, 2013). It is now possible to store medical records of thousands of patients within a single file and access this information just by a click of a button; something that human brain might not accomplish. It is also becoming a common practice especially in developed countries such as the United States for doctors to use home-based supercomputers to identify those patients having postoperative infections such as kidney failure (Frankel, 2016). This way, the challenge of hospital readmission has been greatly minimized thereby helping to reform the health sector. With such intelligent machines, it is also possible for a doctor to make a comparison between patients’ medical history with data available in databases so that they can come up with the most effective form of treatment.

Various health centers in the United States such as Vanderbilt University Medical Center St. Jude’s Medical Center have so far been recommended for successfully utilizing these supercomputers in their interaction with customers (Frankel, 2016). They usually set up home-based electronic systems for those patients under their care and who are receiving posttreatment services. From such systems, they are able to receive notifications whenever a health complication arises. This way, they are able to reach the patient in time and offer the appropriate medication while at the same time minimizing unnecessary hospital readmissions (Open Clinical, 2003). In addition, the same systems are able to compare signals they receive from the patient and compare them with the already stored information in the database and predict the appropriate medication for the patient and also determining which cases require urgent consideration (Frankel, 2016). As a result, doctor’s work is made easier allowing them to handle numerous patients at a time irrespective of their location. Machines are known to be more precise than humans who are prone to fatigue when intelligence systems are therefore used to predict the appropriate medication the chances of administering wrong medication is are very rare. Research shows that in the United States approximately forty thousand patients die every year from misdiagnosis since doctors cannot be able to recall all diseases at an instance (Parkin, 2016). But intelligent machines can be used to make predictions for the doctors hence avoiding such mistakes.

In addition, usage of intelligent machines to interpret medical tests has been proved to be successful in enabling doctors to diagnose certain infectious diseases. A research conducted by European Respiratory Society’s International Congress indicated that using intelligent machines can improve treatment of lung infections (Frankel, 2016). Through a process known as machine learning, the system was fed with patient medical history such as their body mass index as well smoking pattern. Using installed algorithm the machine was able to analyze the data and make reliable predictions about the appropriate medication. Therefore, if these intelligent machines are programmed appropriately with making any initial faults, they can be of great importance in helping doctors diagnose many diseases.

However, such systems cannot be successful if doctors’ involvement is in exclusion. The structures data in the computer cannot be able to assess all the symptoms that a patient is suffering from (Bauer, 2009). This is because for these machines to function they are programmed mainly to carry out a specific task. Any symptom that is beyond their scope of application may give erroneous results and this might lead into making erroneous predictions. Doctor’s reports and laboratory results are more helpful in providing appropriate treatment to the patient (Appari& Johnson, 2008). For example, suppose we use an intelligent machine to identify symptoms of smoking on patients. If the cigarette has not damaged their respiratory system and the machine is programmed to identify such damages, then it might come up with the wrong conclusion. However, if the doctor is involved in this test, they might consider other factors such as the color of the teeth or lips and give a reliable feedback. It is, therefore, important to note that these intelligent machines can only be used for assistance but not as substitutes. But as technology continues to advance, it is wise that this issue is not taken as a barrier but rather as a challenge which will allow more research to be conducted on how to make intelligent computers make such distinctions.

Despite the huge benefits of using intelligent machines such as a computer to store patients’ records, it is worth to note that if they fail then the patient might be under security risks. If the system is not well protected to avoid access by unauthorized users, then patient medical records can be interfered with and this interprets to the breaching of their confidence (Parkin, 2016). Although a physician is entitled to retrieve all the necessary information for them to provide appropriate medication, strict regulations should always be put in place so as to ensure that anyone who is not legally permitted to access such crucial information does not do so. Respecting patient’s privacy is an obligation that any medical personnel must adhere to(Brumen, Hericko, Sevcnikar, Zavrsnik&Holbi, 2013. With the emergence of social media, security and privacy of patients’ records have been at risk especially due to the presence of internet hackers. Breaching may occur from many instances such as intentional theft of patient’s medical data, accidental accessibility to such data or even fault programs that expose such crucial information to unauthorized recipients (Kononenko, 2001). Cases of breaching are very common especially in developed countries such as the United States of America with statistics showing that almost seven hundred cases of confidential breaching are reported annually.

Hospitals are often considered as targets for many internet hackers with some analysts arguing that this situation is caused by ignorance by many health sectors to provide security to patient’s records. One might be wondering why hackers are interested with patient’s health records. But it is worth to note that just like any other organization, hackers can use patient’s records for financial gains through ill practices such as blackmail (Bauer, 2009). Cases have been reported whereby once a patient is diagnosed with certain infections such as HIV/AIDS, they opt to keep it a secret from relatives to avoid isolation (Bauer, 2009). However, once hackers are able to retrieve such information from a hospital website, they continuously blackmail the patient to give out money or else they release the information to the same relatives.

It is, therefore, necessary that healthcare management takes this issue with the seriousness it deserves in order to end this problem. One of the most promising solutions is to ensure maximum security to storage locations such as databases. One of the tricks that hackers use to access patient information is to use a trial and error method whereby they guess possible passwords using related information (Appari& Johnson, 2008). If it made more difficult to log in into a patient account by using complicated passwords then the problem might be partially solved. Another loophole through which patients records are leaked is through retrieval by unauthorized clinicians who have access to server rooms. If cameras are installed in such rooms or rather physical access is minimized, such records might hardly be reached (Harman, 2012).

In conclusion, it is clear that artificially intelligent machines provide a pack of benefits in the field of medicine. Such benefits include; helping doctors to diagnose numerous diseases through predicting probable medications, storing patients’ records within a single file, therefore, making it easy to retrieve the data and also easing the practice of home-based care services. However, it is equally noted that without putting security measures in place, patient records might be accessed by unauthorized users thereby breaching their confidence as well as interfering with their privacy. It is, therefore, important that medical data is properly secured so that patient do not suffer other than enjoying technological advancements.



Appari, A. & Johnson, M. (2008). Information Security and Privacy in Healthcare: Current State of Research. Retrieved from

Bauer, K. A. (2009). Privacy and Confidentiality in the Age of E-medicine. Retrieved from…

Brumen, B., Hericko, M., Sevcnikar, A., Zavrsnik, J. &Holbi, M. (2013). Outsourcing Medical Data Analyses: Can Technology Overcome Legal, Privacy, and Confidentiality Issues? Journal of Medical Internet Research, 15(12), 1.

Frankel, J. (2016). How Artificial Intelligence Could Help Diagnose Mental Disorders. Retrieved from

Harman, L. B. (2012).Electronic Health Records: Privacy, Confidentiality, and Security. American Medical Association Journal of Ethics, 14(9), 712-719.

Hernandez, D. (2014). Artificial Intelligence is Now Telling Doctors How to Treat You. Retrieved from

Kononenko, I. (2001). Machine Learning for Medical Diagnosis: History, State of the Art and Perspective. Artificial Intelligence in Medicine, 23(1), 89-109.

Open Clinical. (2003). Artificial Intelligence in Medicine: an Introduction. Retrieved from

Parkin, S. (2016). The Artificially Intelligent Doctor Will Hear You Now. Retrieved from

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Artificially Intelligent Machine and Patient Privacy

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