Artificial Intelligence in Healthcare

 

Challenges

Lack of accuracy in the interpretation of the data. It is important to note that the artificial intelligence is still in its infancy. Although some of the training concerning the comprehensive datasets has been done, it is still a challenge when it comes to issues that it has not been trained well and this makes then less accurate and less reliable thus putting the life of the patient at risk. (Thrall et al., 2018).  A study that was done on the some popular smart wearable showed that the reading they gave where not the actual individual. It is therefore important to involve interpretation together with the artificial intelligence to bring accurate and reliable information.

Issues concerning the privacy confidentiality. All the personal information are usually stored in the system and thus they are likely to hacked and key personal information retrieved. Proper securities need to be put in place to ensure that all the data that is stored in the system concerning the patients are safe (Thrall et al., 2018). This can be done through data encryption and also employing good security systems. This will address the issues of possible hacking and data falling to the wrong hands.

Lack of trust in the system, some of the recommendations that are made by the system are not readily accepted by the patients for example if a patient is recommended by the system to go for myomectomy. It is necessary to develop the system such that it provides transparency and also can give explanations on how it arrived to it, this will foster trust and will facilitate its use in improving patient outcomes.

            Poor training on the use of the equipment, recent studies have shown that many healthcare workers have little training on machine use therefore, the desired information and proper use might not be achieved (Wong & Bressler, 2016). In this it is necessary to expand the medical education to include training on new technology. This can also be improved through dedicated training on the use and functions of various equipment in the facility

References

Thrall, J. H., Li, X., Li, Q., Cruz, C., Do, S., Dreyer, K., & Brink, J. (2018). Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success. Journal of the American College of Radiology, 15(3), 504-508.

Wong, T. Y., & Bressler, N. M. (2016). Artificial intelligence with deep learning technology looks into diabetic retinopathy screening. Jama, 316(22), 2366-2367.