A Role of AI in Personalized Health Care and Medical Diagnosis

Authors

  • Abhay Purohit Assistant Professor, Electronics and Communication, Arya Institute of Engineering and Technology Author
  • Kamlesh Gautam Assistant Professor, Electronics and Communication, Arya Institute of Engineering and Technology Author
  • Sameer Kumar Science Student, OM Shee Shant Academy, Pipar city, Jodhpur Author
  • Shivam Verma Science Student, Engineers point Sr. Sec. School, Khertal, Alwar Author

DOI:

https://doi.org/10.61841/y5d4b544

Keywords:

Villain robots, takeover, improve healthcare, refined, surgical implants, intrinsic ability, regulation frameworks, transparent reporting mechanism, adaptive, accurate prediction

Abstract

Artificial intelligence is often portrayed as evil robots ready to take over the world but we’re here to make the case that AI can literally save the lives of millions of patients around the world and improve healthcare with tools decide on an accurate delivery. It can have a computer model that, based on the experience of thousands of other patients, knows whether a treatment will work, and based on what is best for that patient and their individual circumstances. AI enables us to gain a deeper and more comprehensive understanding of human health than we had before. Medical software has consisted of medical devices also known as AI-based software for diagnosing, treating or treating diseases such as invasive surgery, or most software with the same effect, whenever used by a patient or physicians’ role. No matter how many times we use it. In other words, AI software behaves very differently from most software in healthcare due to its inherent ability to learn and evolve overtime, ideally intelligently large enough to fit the predictors of the context in which it is used and improving health outcomes. 

Downloads

Download data is not yet available.

References

1. Ahmed, Z., Kim, J. and Liang, B.T. (2019) MAV-click: framework towards management. Analysis and visualization of clinical big data. J. Am. Med. Inf. Assoc. Open, 2, 23–28.

2. Makary, M.A. and Daniel, M. (2016) Medical error—the third leading cause of death in the US. BMJ. 353, i2139.

3. Ritchie, M. D., de Andrade, M. & Kuivaniemi, H. The foundation of precision medicine: integration of electronic health records with genomics through basic, clinical, and translational research. Front. Genet. 6, 104 (2015).

4. Stoner, A. and Elemento, O. (2016) A primer on precision medicine informatics. Brief. Bioinform. 17, 145–153.

5. Zeeshan, S., Ruoyun, X., Liang, B.T. and Ahmed, Z. (2019) 100 years of evolving gene-disease complexities and scientific debutants. Brief. Bioinform. bbz038

6. Karczewski, K.J. and Snyder, M.P. (2018) Integrative omics for health and disease. Nat. Rev. Genet., 108, 1111.

7. Marx, V. (2013) Biology: the big challenges of big data. Nature, 498, 255–260.

8. Combi, C. (2017) Editorial from the new editor-in-chief: artificial intelligence in medicine and the forthcoming challenges. Artif. Intel. Med., 76, 37–39.

9. Ahmed, Z. and Liang, B.T. (2019) systematically dealing practical issues associated to healthcare data analytics. In: Lecture Notes in Networks and Systems 69 Springer Nature.

10. Beam, A.L. and Kohane, I.S. (2018) big data and machine learning in health care. JAMA, 319, 1317–1318.

11. Raghupathi, W. and Raghupathi, V. (2014) Big data analytics in healthcare: promise and potential. Health Inform. Sci. Syst., 2, 3.

12. Alyass, A., Turcotte, M. and Meyre, D. (2015) from big data analysis to personalized medicine for all: challenges and opportunities. BMC Med. Genom., 8, 33.

13. McShane, L.M., Cavenagh, M.M., Lively, T.G. et al. (2013) Criteria for the use of omics-based predictors in clinical trials. Nature, 502, 317–320.

14. Berger, B., Peng, J. and Singh, M. (2013) Computational solutions for omics data. Nat. Rev. Genet., 14, 333–346.

15. Kim, M.O., Coiera, E. and Magrabi, F. (2017) Problems with health information technology and their effects on care delivery and patient outcomes: a systematic review. J Am. Med. Inform. Assoc., 7, 246–260.

16. Sligo, J., Gauld, R., Roberts, V. and Villa, L. (2017) A literature review for large-scale health information system project planning, implementation and evaluation. Int. J. Med. Inf., 97, 86–97.

Downloads

Published

29.02.2020

How to Cite

Purohit, A., Gautam, K., Kumar, S., & Verma, S. (2020). A Role of AI in Personalized Health Care and Medical Diagnosis. International Journal of Psychosocial Rehabilitation, 24(1), 100666-10069. https://doi.org/10.61841/y5d4b544