AI in Healthcare and Ethical Concerns Now

“AI in Healthcare and Ethical Concerns Now” is a wide topic to discuss. Artificial intelligence (ai) technology is growing more sophisticated and rapidly being adopted in healthcare. While there are many potential benefits to using ai in healthcare, there are also ethical concerns that need to be addressed. Some of the ethical concerns include data privacy, physician workload, and patient autonomy.

Data privacy is a major concern with ai in healthcare. As technology becomes more sophisticated, it becomes easier to collect and store large amounts of data. This data can be used to create profiles of patients, which could be used to make decisions about their care. Patients may not be comfortable with their data being used in this way.

Another concern is that ai may increase the workload of physicians. As ai gets better at diagnosing illnesses, physicians may start relying on it more and more. This could lead to physicians becoming overwhelmed with work.

Patient autonomy is also a concern. Patients may not want to be treated by ai, especially if they are not able to understand how it works. They may feel like they are not in control of their own care.

These are just a few of the ethical concerns that need to be addressed when using ai in healthcare. It is important that we take these concerns into account as we move forward with this technology.

Why ai in healthcare should keep ethical concerns?

There are a few reasons why ai in healthcare should keep ethical concerns:

  • First, as society becomes more and more reliant on ai, it is important that the technology is used ethically to benefit humanity as a whole.
  • Second, there are concerns that the implementation of ai in healthcare could have a negative impact on patients’ privacy and data security.
  • Finally, it is important that ai in healthcare is transparent and accountable to patients, healthcare providers, and other stakeholders.

Data collection Hindrance

A big amount of data needed to train Machine Learning to use ai in healthcare. In data acquisition the big issue is the privacy of patients. For example, according to a survey conducted in the UK approximately 63% of the population is intolerable with sharing their personal data in order to improve artificial intelligence technology.

Patients are scared regarding their privacy. So real data of patients is a hindrance that discourage the grow of developing and deploying artificial intelligence models in healthcare.

Data collection can be a hindrance for ai development for a few reasons.:

  • First, if data is not accessible or is corrupted, then ai development may be hindered.
  • Second, if data is collected in an unregulated or unorganized manner, it can be difficult to make use of for ai development.
  • Lastly, if data is not up to date, then it may not be suitable for use in ai development.

Automation in Healthcare

There is wrong perception about ai that it will replace the physicians with its machine learning models. According to some case studies based on surveys, AI could replace up to 35% of jobs in the UK within the next 10 to 15 years. But this not a true picture. AI automation will absolutely help the physicians dealing with digital information, radiology, and pathology, as opposed to those dealing with doctor to patient interaction.

Automation will cause the improvement in doctors in terms of diagnosing the deceases and other defects. In this way doctors who are taking benefits of ai in healthcare can furnish greater quality healthcare than doctors and medical establishments who do not. AI will not replace the physicians and doctors but it will help them to be perfect in their knowledge and make them more reliable and professional.

AI will ultimately help come up with progression in healthcare which include better communication, automation and improved quality of healthcare.

AI Biased Applications in Healthcare

AI could be very dangerous for patient’s insights and predictions if it has training with bias data.

Biased data means that information of patient’s history or decease in data, is not sufficient for machine to learn. Biased data could also contain wrong information or lack of information about patients.

Electronic Health Records (EHRs) must be maintained by administration. EHR data involves the perfect information of patient itself and medical history. It must have true records. Because AI will train itself on the basis of EHR data. After training, AI decisions will reflect as per input data. Once machine has training with bias data, then it will turn a biased machine. It will never provide accuracy. It may diagnose TB (Tuberculosis) instead of just a normal cough. Similarly it may detect HIV in a patient having TB (Tuberculosis). Similarly in case of X-Rays image, it could produce wrong insight.

The data must have accurate representation of patient demographics. Patient’s data from minorities can lead to ai making more accurate predictions for majority populations, leading to the worst medical results for minority populations. White males are excessively represented in medical data sets. Data collection from minor communities may lead to medical discrimination.

There are many ethical concerns about the use of ai in healthcare:

  • One worry is that ai could be used to make decisions about people’s health without their consent. For example, a computer might be used to decide whether or not to prescribe a particular medication to a patient.
  • Another concern is that ai could be used to gather sensitive personal data about people without their knowledge or consent. This could include information about people’s health conditions, their race or ethnicity, or their religious beliefs.


There are ethical concerns with using ai in the healthcare industry. Some of these concerns include the impact on jobs, the impact on patient privacy, and the impact on the quality of care.

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