Artificial Intelligence in Health Care

 

Artificial Intelligence is becoming prevalent in all genre of life. As education, healthcare is another potential field where AI can perform certain key healthcare activities such as diagnosing disease, patient monitoring, etc. Healthcare data has been increasing exponentially and it requires predictive analytics with precision. AI can perform healthcare tasks (Davenport, 2019) equivalent or better than human beings. This article discusses the relevance of Artificial Intelligence in the field of healthcare, its impact and the use of information processing in supporting this field.

ML & NLP

The various technologies of AI which are relevant to the field of healthcare include neural networks and deep learning, natural language processing, rule-based expert systems, robotic process automation and robots. AI focuses on diagnosing and treating disease. However, its integration with clinical workflow and medical records in order to acquire diagnosis equivalent to human diagnosis is still under consideration. Machine Learning is a technique used in precision medicine in order to predict which treatment shall be most suitable. This prediction is made on the basis of patient attributes using training datasets. Natural language processing is used in classifying clinical documentation and research to establish understanding. It is used in analyzing patient condition and prepare medical reports. Rule-based expert systems involve certain rules which apply to clinical decision support. Machine Learning algorithms are robust and are a suitable choice for such decision making.

The interesting area in medicine where Artificial Intelligence can be applied is for resolving healthcare related issues. Data sets can be efficiently treated and quickly accessed using AI. It is also predominantly used in diagnosing and predicting diseases. Accurate diagnosis of disease requires urgent need of AI. Health professionals can acquire accurate and quick diagnostics for different diseases. As stated in (Rong, 2020), machine learning is used in artificial intelligence  data interpretation for classifying and detecting abnormality. The technology uses biosensors which help to predict diagnosis of grave diseases at early stage like cancer, cardiovascular diseases (Jiang, 2017), etc. Various other fields where AI is applied include dermatology, radiology and ophthalmology (Gomez, 2020).

Impact of AI in Healthcare 

The impact of AI on healthcare is tremendous (Ahuja, 2019). Brain computer interfaces supported by AI can be used to restore the nervous system inabilities to a substantial extent thus improving quality of life for patients who feared the loss of speech or movement forever. Another aspect of AI includes radiological tools which are very useful in diagnostic processes which require biopsy. The accuracy and reliability of these tools is proven with their use in place of tissue samples, as per certain researcher’s and expert’s predictions. Another important role played by AI is in eliminating the anomalies caused by electronic health records which involved cognitive overload, excess documentation and human efforts. The tools for voice recognition and dictation have tremendously contributed to reduced documentation, medication refills, result notifications, etc.

The technology can further be used for containing risks related to antibiotics resistance. It can be used to analyze electronic health records in identifying infection patterns thus recognizing patients at high risk even before the symptoms arise.AI and machine learning tools can be leveraged to enhance the analysis in its accuracy for healthcare providers. Precision analytics can be created for pathology results as it can delve to the pixel level on large images thus allowing identification of nuances that may not be visible with naked eye.

Conclusion

Artificial Intelligence plays an important role in healthcare offerings and this paper has demonstrated the various aspects of healthcare where AI can be used. Furthermore, AI has a broad application in environmental pollution monitoring, immune analysis, drug screening, etc. However, the ethical issues have emerged as a great challenge in monitoring key issues, reacting in responsible manner and establish governance. Consideration of ethical issues in AI is a promising aspect for future research.

References

Ahuja A. S. (2019). The impact of artificial intelligence in medicine on the future role of the physician. PeerJ, 7, e7702. https://doi.org/10.7717/peerj.7702

Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future healthcare journal, 6(2), 94–98. https://doi.org/10.7861/futurehosp.6-2-94

Gomez-Gonzalez E, Gomez E, Marquez-Rivas et al. (2020), Artificial Intelligence in Medicine and Healthcare: a review and classification of current and near-future applications and their ethical and social impact. arXiv:2001.09778.

Gudivada, A., Tabrizi, N. (2018), A Literature Review on Machine Learning Based Medical Information Retrieval Systems, IEEE Symposium Series on Computational Intelligence (SSCI), Bangalore, India, pp. 250-257, doi: 10.1109/SSCI.2018.8628846.

Jiang F, Jiang Y, Zhi H, et al (2017). Artificial intelligence in healthcare: past, present and future, Stroke and Vascular Neurology;2: doi: 10.1136/svn-2017-000101

Rong, G., Mendez, A., Assi, E. B., Zhao, B., Sawan, M., (2020). Artificial Intelligence in Healthcare: Review and Prediction Case Studies. Engineering, Volume 6, Issue 3, March 2020, Pages 291-301