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Table of Contents
Year : 2020  |  Volume : 3  |  Issue : 1  |  Page : 134-135

AI in oncology

Department of Surgical Oncology, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, Maharashtra, India

Date of Submission05-Jan-2020
Date of Acceptance05-Jan-2020
Date of Web Publication24-Feb-2020

Correspondence Address:
Devayani Madhav Niyogi
Department of Surgical Oncology, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, Maharashtra
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/CRST.CRST_6_20

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How to cite this article:
Niyogi DM. AI in oncology. Cancer Res Stat Treat 2020;3:134-5

How to cite this URL:
Niyogi DM. AI in oncology. Cancer Res Stat Treat [serial online] 2020 [cited 2020 Apr 4];3:134-5. Available from: http://www.crstonline.com/text.asp?2020/3/1/134/279146

I read with interest the article on 'Artificial intelligence in healthcare in developing nations: The beginning of a transformative journey' by Mahajan et al.[1] The advent of artificial intelligence (AI) in healthcare, in general, and oncology, in particular, is inevitable. AI chiefly trumps human learning and decision-making in its ability to learn from and remember patterns emerging out of big data. Like the authors rightly mention, the biggest application of AI in medicine in today's day is in diagnostics: chiefly radiology and pathology. There is emerging evidence to show that AI can be as good as or even better than medical professionals at diagnosing medical conditions, decreasing the time required, and interobserver variability.[2] In countries like India, with limited availability of skilled resources in remote areas, this can be a boon for timely diagnosis and initiation of treatment. However, what about its role in treatment planning and the actual treatment process? The experience with Watson, IBM's AI in the field of oncology, revealed that AI was able to come up with succinct treatment plans on the basis of available patient information, after scanning all records and all available literature in the field.[3] This is more than can be expected from a human doctor. Does AI stand to replace the doctor? The answer to this question lies in what role a doctor plays in making a patient better. Is it as mathematical as making an accurate diagnosis and coming up with all possible treatment plans? Or, does a large part of the treatment process depend on experience, keeping in mind that there can be no 'always' and 'never' in medicine? So long as there are no absolutes in medicine, the human doctor can never go out of vogue!

There is no denying the fact that AI is here in a big way and is here to stay. The challenge the medical fraternity faces today is in finding the most appropriate place and the role of AI in the various aspects of medical care. Issues regarding patient privacy and attribution of negligence merit attention and robust solutions. This calls for aware governance, ready to explore the realm of possibility AI has to offer. India's NITI Aayog is a right step in this direction. The authors aptly call AI 'Augmented Intelligence' because it needs to be exactly that. Combining the benefits of big data and the intuition and experience of conventional medical practice are required to achieve the best possible outcomes for the patient.

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  References Top

Mahajan A, Vaidya T, Gupta A, Rane S, Gupta S. Artificial intelligence in healthcare in developing nations: The beginning of a transformative journey. Cancer Res Stat Treat 2019;2:182-9.  Back to cited text no. 1
  [Full text]  
Erwin L. Medicine and the rise of the robots: A qualitative review of recent advances in artificial intelligence in health. BMJ Leader 2018;2:59-63.  Back to cited text no. 2
Patel NM, Michelini VV, Snell JM, Balu S, Hoyle AP, Parker JS, et al. Enhancing next-generation sequencing-guided cancer care through cognitive computing. Oncologist 2018;23:179-85.  Back to cited text no. 3


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