Early Disease Detection through Data Analytics: Turning Healthcare Intelligence
DOI:
https://doi.org/10.47709/ijmdsa.v2i2.2995Keywords:
Keywords: screening, analysis, information technology, big data, algorithms, artificial intelligence, machine learning, predictive modeling, wearable’s, privacy concerns, voluntariness, ownership, bias, health access, patient-physician contracts, inequality, medical moralityAbstract
The incorporation of data analytics into disease diagnosis is still in its primitive stage however, its capability to enhance efficiency and patients’ health is tremendously exciting. The use of artificial intelligence (AI), machine learning, wearable technologies, and predictions analyzed can make healthcare systems enable early diagnosis of diseases and cost less to develop preventative health care. However, the change brings several issues of ethical consideration into the equation. Some of such challenges comprise of privacy and confidentiality of sensitive health information, challenges in getting consent and ownership of the patient data. Furthermore, this work discussed that algorithmic bias issue might expand the health disparities if solutions are not found, thus exacerbating inequalities in of health care treatment. The continued development of added AI tools should also be met with the preservation of trust in the doctor and the patient to prevent the man-made intelligence to lead into a replacement of human cognition. For these technologies to benefit the greatest number of people, the advanced technology solutions must be made readily available to avert further widening of the inequalities in health. These ethical questions are best understood and therefore shall be discussed in this paper, with a focus on privacy, consent, fairness, and accessibility. In assessing these issues, the concern of concern can use the big data analytics to enhance the illness diagnosis without violating the patient’s privileges and/or discriminative the equal rights of health care.
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