AI in healthcare

Full text

Page 1 of 10
© Journal of Hospital Management and Health Policy. All rights reserved. J Hosp Manag Health Policy 2021;5:28 | http://dx.doi.org/10.21037/jhmhp-20-126
Introduction
An individual patient data when aggregated with others
across the health system artificial intelligence (AI)’s
predictive capabilities are enhanced as its to make better
decision making via improved public health surveillance
besides producing leaner, quicker and more focussed
research and development (1). AI is poised to be an aide
for all healthcare professionals as it becomes an integral
part of healthcare whether it is to automate patient data
documentation or fast tracking image analysis as well as
assist with virtual observation, diagnosis, rehabilitation,
mental health support and patient outreach (2-4). AI can
prove to be an efficient tool for identification of early
infections, developing treatment protocols, drugs and
vaccine development (5). Whether medical, epidemiological
or molecular applications AI in healthcare can help.
Presence of AI in Indian healthcare
Several research studies over the last two years have
increasingly brought out the fact that AI technologies have
the potential to bridge the gap particularly in rural areas to
better access quality healthcare (5-7).
A multitude of stakeholders including Microsoft and
Google, have also come together to work on variety of
initiatives to build AI infrastructure across India (8,9). They
have conducted pilots with hospital chains in India (10).
Review Article
Use of artificial intelligence in healthcare delivery in India
Keerti Pradhan1, Preethi John2, Namrata Sandhu1
1Chitkara Business School, Chitkara University, Punjab, India; 2Chitkara School of Health Sciences, Chitkara University, Punjab, India
Contributions: (I) Conception and design: All authors; (II) Administrative support: P John; (III) Provision of study materials or patients: K Pradhan, P
John; (IV) Collection and assembly of data: All authors; (V) Data analysis and interpretation: P John, N Sandhu; (VI) Manuscript writing: All authors;
(VII) Final approval of manuscript: All authors.
Correspondence to: Keerti Pradhan. Chitkara University, Punjab, India. Email: keerti.health@gmail.com.
Abstract: The growth of articial intelligence (AI) has seen an exponential growth in India. Recent policy
initiatives favouring the acceleration of AI application in Indian healthcare is discussed. This article then
captures the range of AI applications in healthcare in India. The AI applications range from those used in
early screening or diagnostic space, to those used in treatment and rehabilitation. In the Indian healthcare
space innovations that can improve rural healthcare is greatly valued. Several of the start ups featured in
this article have sought to apply AI to rural Indian healthcare to address the gaps. With smartphone boom
AI enabled reality can be possible. However there are several challenges to scale up use of AI in healthcare
delivery in India about which this article concludes with. Currently most of the applications are still at a
regional level. Several issues are there to scale up the data level needs to be addressed before AI can truly be
a reality for India. Data availability, data pooling, data collection, data sharing, data protection, data privacy
are among the multifaceted issued which must be sorted out. Other challenges range from human resource
issues to lack of awareness to need to address ethical issues in AI based innovations, to cyber security issues,
lack of infrastructure, besides the high cost of investing in AI based innovations. Several policy mechanisms
and regulatory frameworks are being brought out to address these challenges via NITI Aayog Towards
Responsible #AIforAll. The article concludes on the fact that AI in healthcare can potentially change the
landscape if done right.
Keywords: Healthcare; articial intelligence (AI); India
Received: 01 September 2020; Accepted: 24 December 2020; Published: 25 September 2021.
doi: 10.21037/jhmhp-20-126
View this article at: http://dx.doi.org/10.21037/jhmhp-20-126
10

Journal of Hospital Management and Health Policy, 2021Page 2 of 10
© Journal of Hospital Management and Health Policy. All rights reserved. J Hosp Manag Health Policy 2021;5:28 | http://dx.doi.org/10.21037/jhmhp-20-126
Indian policy space accelerating use of AI in healthcare
delivery
‘NITI Aayog’ the policy think tank of the Government
of India was authorised to formulate the national strategy
on AI and other emerging technologies in 2018 (11).
NITI Aayog focused on ve sectors that are envisioned to
benet the most from AI in solving societal needs of which
healthcare is one (11). The motto adopted by NITI Aayog
in this venture is ‘AI for all’ (#AIforAll).
NITI Aayog also seeks to ensure adequate data privacy,
security and balancing ethical considerations with the
need for innovation (11). The following recommendations
are being put in place to accelerate the pace of AI related
innovations in healthcare.
 Creating a multistakeholder marketplace—The
National Articial Intelligence Marketplace.
The development stage requiring a multitude
of specialized processes. In order to encourage the
development of sustainable AI solutions in healthcare it
is crucial to address information asymmetry and promote
effective collaboration. This may be possible by creating a
‘marketplace’ which could:

Enable access to the required AI component, be it
data or business models, and services, such as data
annotation, and enable rating of these assets;

Serve as a platform for execution and verification of
transactions.
Ensure data today which is being collected at the
individual hospital level but not analysed be collected to
build the big dataset envisaged to accelerate AI work.
Unraveling new sources of data and facilitating more
efficient use of computational and human resources. It is
estimated that only 1% of data today is analyzed due to lack
of awareness and availability of AI experts. For instance,
several medical imaging centers are collecting valuable data;
however, these databases are not analysed since AI models
cannot be created without computational infrastructure
and trained personnel (11). In the presence of a formal
marketplace, diagnostic centers would have an incentive
to collect these data and provide access to the data in the
market with requisite security measures in place.
 Provide an opportunity to address ethical concerns
regarding data sharing: creation of a formal
marketplace for data transactions would ensure the
development of data security measures to prevent
misuse of valuable information.
The development of a data protection law in India is
currently underway. This, along with the promotion of local
innovators and leaders in AI technologies, are crucial steps
to ensure that the big data generated in India is used to
empower local populations and provide them with services,
rather than to exploit them for commercial gains (12).
Partnerships and collaboration
A stratied approach to partnerships is proposed as follows:
 Low transfer: informal interactions such as
conferences and social networking;
 Medium mobility: training of industrial employees,
internship programs, and academic entrepreneurship;
 High relationships: sharing infrastructure and
interorganizational arrangements for pursuing R&D.
For instance, a radiologist and computer scientist
could work together to develop solutions and enhance
knowledge in real time (11).
Promotion of AI in India
The Union Health Ministry has also publicly expressed
eagerness to incorporate AI into Indian healthcare. The
Union Minister stated that potential of AI in public health
was being explored for India. NITI Aayog proposes the
creation of ‘Big Data’ sets (11). This could then be utilised
by those entering this space like the Indian AI developers.
Department of Biotechnology is also facilitating the
implementing of solutions for rapid deployment of AI in
healthcare in India (11).
In India health is both a national priority as well as
priority for states to deliver on. Several states have taken on
the initiate to accelerate AI innovation in healthcare (12-14).
A major hurdle to the adoption and promotion of AI is
the lack of awareness with regards to the work being done
across the country. The promotion of AI is can be overcome
by creating an online portal such as an AI Database for
registered people to access and obtain information (11).
Promoting startups
Availability of nancial support and adequate infrastructural
facilities is important to ensure their participation in AI
projects (11).
Accelerating the HR pool in India with skillset to match
the AI in healthcare needs
Start of multidisciplinary programmes have been initiated in

Journal of Hospital Management and Health Policy, 2021 Page 3 of 10
© Journal of Hospital Management and Health Policy. All rights reserved. J Hosp Manag Health Policy 2021;5:28 | http://dx.doi.org/10.21037/jhmhp-20-126
several Universities to accelerate the production of human
resource skills trained with the right skill set (11).

PG programme in Medical Science and Technology
programme offered by IIT Kharagpur which
provides an option for MBBS graduates to enter into
engineering for those with an interest in AI;

AIIMS and IIT Delhi are working on several
collaborative projects of AI in healthcare from
government/semi government front;
 Centre for AI has been established in JSS Medical
College, Mysore, Karnataka;

Centre of Excellence in AI has been launched in India
(www.opengovasia.com). The center of excellence has
been developed by the national informatics center
to use AI to improve the delivery of government
e-services;

The Hope Foundations—International Institute of
Information Technology launched its Centre for of
Excellence in Articial Intelligence at Hinjawadi;

Narayana Health uses data analytics, and IA to provide
affordable healthcare high quality healthcare;

Health Ministry signs MOU with AI based work
solutions in combating TB;

Amazon Alexa, launch in Pune on Aug 30, 2019,
Chandra Kumar—for the first time Amazon team
will conduct hands on workshop for developing Alexa
assistants;

TISS and CII—National Conference on AI in Health;

Chitkara University has been leading with a lot
of innovation programmes which it is offering to
computer science students with specialisations
in different areas of AI like Big data; Artificial
intelligence;

IIIT Delhi is another institute actively working on AI
solutions in healthcare.
Classification of AI use in Healthcare in India
There are four broad categories for AI in healthcare (15):
 Descriptive: this is currently the most widely used.
It involves quantifying events that have already
occurred, and using this data to detect trends and
other insights; ‘what wellness monitoring or clinical
episode happened’;
 Diagnostic: why a specific clinical episode or
healthcare case had happened;
 Predictive: this uses descriptive data to make
predictions about the future, about what healthcare
issues are likely to happen;
 Prescriptive: not only detects trends but also suggests
possible treatments in public health or more targeted
clinical trials in research and development. It seeks to
‘prescribe to the relevant actions required to mitigate
or eliminate healthcare problems and to exploit
specic healthcare trends in improving patient or care
outcomes’.
Applications of AI in Indian healthcare
Indian Institute of Technology Bombay incubated a startup
Matra Technology which developed a mobile based AI
technology Naima which reduces pregnancy risk (16)
(https://www.wadhwaniai.org/). Maternal, newborn and
child health—smartphone based anthropometry technology
which allows frontline health workers to screen for low
birth weight babies (17).
In 2016, Niramai Health Analytics (Bangalore),
developed a non-invasive, low-cost solution to screen early
breast cancer based on mapping body heat embedded
with AI technique (18). ‘Niramai’ is an acronym for Non-
Invasive Risk Assessment with Machine-learning and
Artificial Intelligence. It can detect tumours five years
earlier than mammography or clinical exams based on
‘Thermalytix’ technology (18,19).
Janitri Innovations focusing on pregnancy healthcare
with different innovations like Daksh a paperless
labour monitoring system (20). Keyar a non-invasive
cardiotocography CTG device that can monitor the heart
rate of a baby in the mother’s womb and track uterine
contractions of pregnant women. This device is portable,
non-invasive, and easy to use in rural remote areas (21).
The performance of the device when tested against the gold
standard CTG machine at St Johns Medical Hospital in
Bangalore was equivalent to the gold standard (21).
Predictive AI has shown that for predicting suicide
attempts, AI triumphs over human beings (22,23).
Taking family history using chat bots will be great boon
in Indian setting because patients usually travel from far off
places to city’s or town’s only to sit and answer questions
(24,25). This can be a great time saver. Family history is
extremely important in genetics. In India voice bots may
overcome several challenges of literacy which chatbots face.
Challenge in chatbot development is—how to translate
most of it in regional languages that is comprehensible by
a lay person; also they have limitation in areas with limited
literacy.

Journal of Hospital Management and Health Policy, 2021Page 4 of 10
© Journal of Hospital Management and Health Policy. All rights reserved. J Hosp Manag Health Policy 2021;5:28 | http://dx.doi.org/10.21037/jhmhp-20-126
AI in screening
AI has been found to be as effective and in some cases, more
effective particularly in diagnostics areas like radiology and
pathology (11). CARING (Centre for Advanced Research
in Imaging, Neuroscience and Genomics) applied AI to
imaging as well as the integrative research in the field of
neuroscience and genomics and they have taken forward
cutting edge scientic and clinical research but also focussed
on producing relevant products in collaboration with others
(26,27).
AI-based Radiomics project by NITI Aayog in
collaboration with Tata Memorial Centre Imaging Biobank
(Machine learning and Articial Intelligence Database and
Tumor Radiomics Atlas Project for Cancer unit) is currently
underway. There are several other interesting innovations
happening with NITI AAYOG (11).
Currently it has been used in medical diagnosis, in
psychiatry and for treatment of certain medical ailments
(19,20).
Diabetic retinopathy: Remidio Fundus on Phone—
portable affordable retinal camera. This AI innovation
shifts DR screening to primary care centres (28). Medios AI
automated screening of referable DR on the Remidio FOP
works offline, and is deployed on camera smartphone. It
works with non-mydriatic image and output and provides
referral for ophthalmologist and visualization of the
detected lesions.
There are AI innovations for Histopathological image
analysis as well as TB screening using X-rays (29,30).
Wadhwani Institute for AI is also doing tremendous work in
this areas as ofcial AI partner for India central tuberculosis
division technologies (31).
Medical Devices Based on IOT/IOMT—are uploading
and analysing data in real time; used for quarantined self
to telemonitoring/teleICU consultation to AI predication
from sepsis (32,33). There are AI enabled devices like
wheelchairs- sensor for automatic parking of wheelchairs (34),
thermometer, weighing scale, ECG (35).
Founded in 2018, Kolkata based medtech startup is
building affordable non-invasive AI based solutions for
early diagnosis of chronic diseases. This device aims to
make the adoption of preventive healthcare approach more
feeling accessible for Indians by providing easy affordable
diagnostic solutions (36). The startup first product AJO
which stands for anemia jaundice and oxygen saturation
is a non-invasive IOT enable device that test for anaemia
liver and lung related medical problems without any blood
work and for less than Rs 1. The user friendly device does
not require medical knowledge or expertise to operate once
the test is completed the result can be transferred by email
or text message in less than 1.5 seconds. The device cleared
clinical trials at NRS Medical College Kolkata with high
accuracy (36).
Osteoporosis Prediction Models for Patients’ Risk
Assessment
The aim of this invention ˜Osteoporosis™ is to design
the predictive model for early and accurate detection of
osteoporosis. In most of the situations patient’s age, weight,
and gender are taken as the clinical features. It assesses the
risk of patient having developed osteoporosis by classifying
the patient into at risk or not at risk category (37).
AI in treatment interventions
Kochi based startup founded in March 2017 BAGMO
(blood bag monitoring) device addresses lack of blood
availability in rural India. It has developed a blood bag
monitoring device bag more which monitors temperature of
blood bank blood bags during transportation and storage. It
improves logistics and communication issues (38,39).
Prantaeis is Bhubaneswar based biotech startup born out
of experience of the founder suffering from a pregnancy
disorder called pre-eclampsia; this startup develops devices
and diagnostic solutions mainly for pregnancy related
health care. So far 4 products are there this includes EyeRa
for early detection of preeclampsia; ProFoIU to monitor
kidney health Salubrious, which provide solution for hidden
hunger and Embargo which can detect antibiotics in food
products (40).
Waferchips Techno solutions developed in Kollam
Kerala is a wearable electrocardiography ECG device called
‘Biocalculus’ to transfers data to an Android application
via Bluetooth if a smartphone is not available. It will store
the data up to a month of recording. The device uses AI to
generate a clinically actionable report for further diagnosis
and treatment (41).
AI in rehabilitation
One of the areas where AI is helping is in the area of
rehabilitation. There are several AI based products which
are coming out which help in mobility. They include
rehabilitative prosthetics, exoskeleton, augmentations, Brain

Journal of Hospital Management and Health Policy, 2021 Page 5 of 10
© Journal of Hospital Management and Health Policy. All rights reserved. J Hosp Manag Health Policy 2021;5:28 | http://dx.doi.org/10.21037/jhmhp-20-126
Computer Interface (BCI) EMG based interface to take
inputs from thoughts (42).
AI in times of COVID
AI played an important role in times of COVID (43,44).
Various AI applications have been tested for
(I) Early detection and diagnosis of the infection;
(II) Monitoring treatment;
(III) Contact tracing;
(IV) Futuristic projections of cases and mortality;
(V) Development of drugs and vaccines;
(VI) Reducing workload of healthcare workers;
(VII) Prevention of disease.
Challenges to be addressed to scale up use of
AI in healthcare delivery in India
The challenges and barriers to the implementation of AI
in healthcare in India must be understood to facilitate the
scaling up of use of AI in healthcare India (45).
Lack of trained personnel and expertise for AI
Replacement of humans because of AI is another huge
worry which affects the support for the adoption of AI in
healthcare (46). The lack of AI trained professionals can
also be a key barrier to using AI in healthcare (45).
Awareness of AI and quicker deployment of AI innovations
There is a lack of awareness about potential and benefits
of AI use in healthcare delivery at level of multiple
stakeholders. Neither healthcare owners, healthcare
professionals nor patients have much idea about it in India.
There is still a lack of understanding of AI and its benets,
among medical professionals particularly among those
in leadership positions and the general population (47).
Information asymmetry among the stakeholders is another
huge challenge (48). Negative media in India of the impact
AI will have on jobs has resulted in an increased struggle for
start-ups to acquire funding (47).
Ethical issues of AI in healthcare delivery
Inequality concerns in the adoption of AI in healthcare in
India include the under-representation of minority groups
in the data used to develop algorithms and solutions; the
prominence of males in the software industry, resulting in
a male bias in technologies; and greater benets to higher
income populations with access to technologies (47).
Data integrity is another burning issue that requires.
Datasets based on large and diverse population would be
required to offset bias (48). This could have disastrous
effect on increasing the divide in society (49,50). AI design
is prone to being a reflection of all the bias that exists in
society if care is not taken (50). Algorithms can generate
data that may be based on race, gender, age, and religion,
resulting in discrimination and unfair results which might
be better for some demographics in India than others (51).
Legal liability and attribution of negligence
Liability for AI is also a key issue that needs to be resolved
as, currently, liability falls solely on the doctor, rather than
the technology (51,52). Explainability, when making its
decisions, is very important (52).
Cybersecurity in India
Cyberattacks on all types of organisations globally are on
the rise, rendering private digitised data vulnerable to being
hacked and accessed by other parties (53,54). In 2016, the
laboratory database hacking, resulted in the leaking of over
35,000 patient records from across India. This is despite a
prior hack, the laboratory had not taken action to secure the
data (55). This requires increase higher standards in data
privacy and data security (55,56). There are concerns in
India about multinationals accessing to local data, without
local benets. Issues of condentiality and cybersecurity also
need to be addressed, in order to prevent the compromise
of sensitive health information (55-57).
High initial cost in AI ventures
In India, the infrastructure required for AI to grow remains
inadequate (5-9). Smaller organisations in the health
sector also struggle, in particular, with limited resources
and insufcient data backup systems (5,7). Start-ups in the
medical eld also face issues in accessing data from outside
of India. Data protection laws, in the EU for example, do
not allow for interoperability.
Lack of infrastructure
While the Indian government has increased spending in

Journal of Hospital Management and Health Policy, 2021Page 6 of 10
© Journal of Hospital Management and Health Policy. All rights reserved. J Hosp Manag Health Policy 2021;5:28 | http://dx.doi.org/10.21037/jhmhp-20-126
the healthcare industry, the amount of public funding it
invests in healthcare is small compared to other emerging
economies (4). Government investment specifically in
health-related AI in India is limited (6). The infrastructure
necessary for AI to take off in India remains neglected by
policy makers (6). This includes availability of internet
and electricity. Hospitals that do not have their own IT
infrastructure can produce difficulties for managers using
IT technologies (5).
Challenges to data availability in healthcare for use by AI
in healthcare
There are several problems facing India the large number of
unstructured data sets and problems with interoperability (2).
Concerns about the absence of open sets of medical data;
inadequate analytics solutions capable of working with big
data; and concerns that algorithms may generate data that
reflect cultural biases (1,4-6,8,57). Lack of access to open
data sets is a particular challenge for start-ups (6). For
example, gathering and uploading all the data from intensive
care unit monitors, deciphering signicant medical patterns
and triggering a medical action (4).
Data pooling and data collection
Access to data is essential for AI implementation (6). India
has extensive amounts of health data available. India lacks,
however, a structured regime in terms of sharing health-
related data (57).
A key obstacle to the adoption and implementation of AI
in healthcare in India is the absence of robust open sets of
medical data. Accessing healthcare datasets can be difcult,
legally and due to other reasons. This is a particular
challenge for start-ups, in particular, as larger actors often
already have access to such data (6). Start-ups thus often
rely instead on publicly available datasets from the US,
Europe, and elsewhere (6,9,57). This undermines the
effectiveness of using AI in healthcare as it does not cater to
the Indian demographic (6,50). Reliance on open data from
other contexts results in algorithms that reect the bias of
such data and development of solutions trained to a specic
demographic (6,8). It would be necessary to adjust for these
biases in the application of AI tools and to retrain solutions
on Indian data, particularly when it involves drug discovery
and genomics (6,8). While there are some scattered
examples of open source data in the Indian context, such as
the state of Tamil Nadu and the National Cancer Registry,
they are insufcient (13).
There is in India a lack of necessary historical health
data due to a plethora of reasons. Health records are often
hand-written in local languages, which may make it more
challenging to digitise (50).
Data protection and privacy
Information privacy concerns are identied as a tremendous
obstacle to big data adoption in healthcare in India (4,50).
There are concerns in India that international companies
in the past have drawn on intangible knowledge from the
healthcare sector in India in order to develop a hospital
information system using the resources of Indian hospitals.
However, these same hospitals were later not able to
access these products they helped to develop, having to
buy licenses for the next versions of the same or similar
products (9).
Consent for collection is a key data challenge (6).
Technical deployment seemed to precede policy
development, adequate privacy legislation, and ethics
constraints (9,45,46,57). The Aadhaar Act [2016] and other
existing regulations fail to provide robust consent provisions
and address privacy issues in regards to the collection of
biometrics (55).
Lack of regulatory compliances and policy guidelines
Problems with standardisation of digitised health data
and interoperability, contribute to ineffective health data
governance (2,57). While the absence of such regulation
has allowed for greater flexibility for start-up companies
to collect data and adopt self-regulatory practices of
anonymising data prior to further use, the regulatory
vacuum produces uncertainty about potential changes
(6,9,53).
Insistence of proof of acceptable results in the form of
costly and time-consuming clinical trials is a key obstacle
for start-ups in India (48,49).
There is a dearth of guidelines regarding data collection
in India, however, especially in healthcare (46). This, in
addition to errors of data entry and tabulation, is considered
to be a key problem—as identified by AI and healthcare
practitioners, start-ups and thank tanks at a workshop on AI
in India (46).
Regulatory challenges include the need for an
appropriate certication mechanism (50). Given that AI is
not limited to any one subject or aspect, there is also a need

Journal of Hospital Management and Health Policy, 2021 Page 7 of 10
© Journal of Hospital Management and Health Policy. All rights reserved. J Hosp Manag Health Policy 2021;5:28 | http://dx.doi.org/10.21037/jhmhp-20-126
for self-regulation and/or for the use of different regulators
for different aspects—such as medical aspect, or for data
aspect (52).
Further, there is no clear regulation to adhere to in
conducting such clinical trials (9,53). Certification system
can help to build trust amongst health practitioners and
patients (9). A possible solution is for doctors and start-
ups to partner to conduct clinical trials (6,53). In addition,
a ‘regulatory sandbox’ could be adopted, which is a testing
box with relaxed regulations to allow a product to be
launched. This can offer an incentive to people working
in the field of AI and health to innovate and to receive
certication (9,53).
Interoperability
In India, the healthcare industry is rarely standardised,
resulting in fragmented and non-standardised clinical data.
Implementation of electronic health record policy has yet
to be harmonised across relevant segments of the healthcare
sector in India. This leads to different interpretations
of digitising records and the absence of comprehensive
implementation across all hospital data (9,50). The absence
of collaborative efforts between various stakeholders
exacerbates this obstacle (57).
Lack of partnerships and collaboration
Public-private partnerships are essential, in order to
avoid duplication of investment, particularly with limited
resources (9,45,46).
Conclusions
With start-ups and large tech companies offering AI
solutions for healthcare challenges the use of AI in
healthcare in India is exponentially increasing (58).
Though developing nations like India lag the superpowers
in fundamental research and resources, they enjoy
advantages in the form of a vast engineering workforce, a
burgeoning startup scene, and a large pool of data waiting
to be tapped (59). India also has the entrepreneurial spirit
to help businesses derive value from real-time data and the
ambition to carve a niche for themselves in an increasingly
AI-driven world (60). With the correct navigation of policy
space, coordination between different stakeholders, and
increased benefits of AI among healthcare leaders the
exponential increase in use will become embedded and
become a part of the Indian healthcare framework.
Acknowledgments
Funding: None.
Footnote
Provenance and Peer Review: This article was commissioned
by the Guest Editors (Sandeep Reddy, Jenifer Sunrise
Winter, and Sandosh Padmanabhan) for the series “AI in
Healthcare - Opportunities and Challenges” published in
Journal of Hospital Management and Health Policy. The article
has undergone external peer review.
Conflicts of Interest: All authors have completed the
ICMJE uniform disclosure form (available at http://dx.doi.
org/10.21037/jhmhp-20-126). The series “AI in Healthcare -
Opportunities and Challenges” was commissioned by the
editorial office without any funding or sponsorship. The
authors have no other conicts of interest to declare.
Ethical Statement: The authors are accountable for all
aspects of the work in ensuring that questions related
to the accuracy or integrity of any part of the work are
appropriately investigated and resolved.
Open Access Statement: This is an Open Access article
distributed in accordance with the Creative Commons
Attribution-NonCommercial-NoDerivs 4.0 International
License (CC BY-NC-ND 4.0), which permits the non-
commercial replication and distribution of the article with
the strict proviso that no changes or edits are made and the
original work is properly cited (including links to both the
formal publication through the relevant DOI and the license).
See: https://creativecommons.org/licenses/by-nc-nd/4.0/.
References
1. Raghupathi W, Raghupathi V. Big data analytics in
healthcare: promise and potential. Health Inf Sci Syst
2014;2:3.
2. Gujral G, Shivarama J, Mariappan M. Articial
intelligence and data science for developing intelligent
health informatics systems. Proceedings of the National
Conference on AI in HI & VR, SHSS-TISS; 2019
Aug 30-31; Mumbai. Available online: https://www.
researchgate.net/publication/338375465_ARTIFICIAL_

Journal of Hospital Management and Health Policy, 2021Page 8 of 10
© Journal of Hospital Management and Health Policy. All rights reserved. J Hosp Manag Health Policy 2021;5:28 | http://dx.doi.org/10.21037/jhmhp-20-126
INTELLIGENCE_AND_DATA_SCIENCE_FOR_
DEVELOPING_INTELLIGENT_HEALTH_
INFORMATICS_SYSTEMS
3. Murali, A, Jayadevan, PK. India’s bid to harness AI for
healthcare. Factor Daily [Internet] 2019 April 4. Available
online: https://factordaily.com/ai-for-healthcare-in-india/
4. Jagdev G, Singh S. Implementation and applications
of big data in health care industry. Int J Sci Tech Adv
2015;1:29-34.
5. Ajmera P, Jain V. Modelling the barriers of health 4.0–the
fourth healthcare industrial revolution in India by TISM.
Oper Manag Res 2019;12:129-45.
6. Paul Y, Hickok E, Sinha A, et al. Articial intelligence in
the healthcare industry in India. Bengaluru: The Centre
for Internet and Society (India); 2018. Available online:
https://cis-india.org/internet-governance/les/ai-and-
healtcare-report
7. Vempati SS. India and the articial intelligence
revolution. Massachusetts: Carnegie Endowment for
International Peace (USA); 2016. Available online: https://
carnegieendowment.org/les/CP283_Vempati_nal.pdf
8. Dhanabalan T, Sathish A. Transforming Indian industries
through articial intelligence and robotics in industry 4.0.
J Mech Eng 2018;9:835-45.
9. Haider H. Barriers to the adoption of articial intelligence
in healthcare in India. Brighton: Institute of Development
Studies (UK); 2020. Available online: https://opendocs.ids.
ac.uk/opendocs/handle/20.500.12413/15272
10. NITI Aayog national strategy for articial intelligence.
New Delhi: National Institution for Transforming
India, Government of India (India); 2018. Available
online: https://niti.gov.in/writereaddata/les/document_
publication/NationalStrategy-for-AI-Discussion-Paper.pdf
11. Mahajan A, Vaidya T, Gupta A, et al. Articial
intelligence in healthcare in developing nations: The
beginning of a transformative journey. Cancer Res Stat
Treat 2019;2:182-9.
12. Animation, visual effects, gaming and comics policy
2017-2022. Karnataka: Department of IT, BT and S&T,
Government of Karnataka (India). Available online: http://
registration.k-tech.org/docs/KAVGCPolicy2017-2022.pdf
13. Sharma K, Manchikanti P. Regulation of articial
intelligence in drug discovery and health care. Biotechnol
Law Rep 2020;39:371-80.
14. Balsari S, Fortenko A, Blaya JA, et al. Reimagining
Health Data Exchange: An Application Programming
Interface-enabled Roadmap for India. J Med Internet Res
2018;20:e10725.
15. Istepanian RS, Al-Anzi T. m-Health 2.0: new perspectives
on mobile health, machine learning and big data analytics.
Methods 2018;151:34-40.
16. Intelligent technology for reducing pregnancy risks.
Available online: https://www.matra.tech/. Accessed Nov
7, 2020.
17. Maternal, Newborn, and Child Health. Mumbai:
Wadhwani Institute of Articial Intelligence (India).
Available online: https://www.wadhwaniai.org/work/
maternal-newborn-child-health/. Accessed Nov 7, 2020.
18. Niramai – A Novel Breast Cancer Screening Solution
[Internet] Available online: https://niramai.com/. Accessed
Nov 7, 2020.
19. Bhattacharya S, Pradhan KB, Bashar MA, et al. Articial
Intelligence Enabled Healthcare: A Hype, Hope or Harm.
J Family Med Prim Care 2019;8:3461-4.
20. Tandon A, Kautilya H. Case study: paperless labour
monitoring system in low resource healthcare settings."
Proceedings of the 10th Indian Conference on Human-
Computer Interaction; 2019:1-5.
21. Rinkoo AV, Panjiyar AK, Songara D, et al. From
Guidelines to Practice: Assessing the Implementation
Fidelity of Partograph Use for Monitoring Labour in
Primary Healthcare Settings in India Using a Tablet-Based
Mobile Application. J Health Med Informat 2019;10:334.
22. Thippaiah SM, Nanjappa MS, Math SB. Suicide in India:
A preventable epidemic. Indian J Med Res 2019;150:324.
23. Chattopadhyay S, Sahu SK. A predictive stressor-
integrated model of suicide right from one's birth:
a Bayesian approach. J Med Imaging Health Infor
2012;2:125-31.
24. Prakash AV, Das S. Would you Trust a Bot for Healthcare
Advice? An Empirical Investigation. Proceedings of 24th
Pacic Asia Conference on Information Systems; 2020;
Dubai, UAE.
25. Singh J, Shah D. Employment in the Healthcare Industry
in India. The Arthniti 2020;1:86-94.
26. Mahajan V, Venugopal V, Gupta S. The Secret Seven: Free
tools that every ‘next-gen’ practicing radiologist should
know. European Congress of Radiology 2020. Available
online: https://epos.myesr.org/poster/esr/ecr2019/C-2858
27. Rajan S, Mahajan V, Mahajan H, et al. Evaluating
radiologists’ knowledge of MRI safety–a questionnaire-
based survey of practicing radiologists. European Congress
of Radiology 2020. Available online: https://epos.myesr.
org/poster/esr/ecr2020/C-04729
28. Rajalakshmi R, Subashini R, Anjana RM, et al. Automated
diabetic retinopathy detection in smartphone-based

Journal of Hospital Management and Health Policy, 2021 Page 9 of 10
© Journal of Hospital Management and Health Policy. All rights reserved. J Hosp Manag Health Policy 2021;5:28 | http://dx.doi.org/10.21037/jhmhp-20-126
fundus photography using articial intelligence. Eye
2018;32:1138-44.
29. Belsare AD, Mushrif MM. Histopathological image
analysis using image processing techniques: An overview.
Signal & Image Processing 2012;3:23-36.
30. Raju M, Aswath A, Kadam A, et al. Advances in Analytics
and Applications (Singapore): Springer; Automatic
detection of tuberculosis using deep learning methods;
2019:119-29.
31. Kadam, D. Articial intelligence (AI)—An Inection Point
In the Global History and its Signicance For a National
Strategy. Indian J Plast Surg 2019;52:145-7.
32. Chattopadhyay AK, Nag A, Ghosh D, et al. A Secure
Framework for IoT-Based Healthcare System. Proceedings
of International Ethical Hacking Conference; Singapore:
Springer; 2018:383-93.
33. Ramnath VR, Malhotra A. Telemedicine in the ICU
(Cham): Springer; Remote Proactive Physiologic
Monitoring in the ICU; 2019:21-44.
34. Aruna C, Parameswari AD, Malini M, et al. Voice
recognition and touch screen control based wheel chair
for paraplegic persons. Proceedings of 2014 International
Conference on Green Computing Communication and
Electrical Engineering; 2014:1-5.
35. Jain P, Anand A, Saria M, et al. A Prototype Proposal
for AI based Smart Integrated Platform for Doctors and
Patients. Proceedings of 8th International Conference
on Reliability, Infocom Technologies and Optimization
(Trends and Future Directions); 2020:998-1003.
36. EzeRx (easy for prescription). Available online: https://
www.ezerx.co.in. Accessed Nov 7, 2020.
37. Burande AM, Nimse SB. Fuzzy Measure of Multiple Risk
Factors in the Prediction of Osteoporotic Fractures in
Indian Women. Bull Marathwada Math Soc 2012;13:9-19.
38. Sharma L, Garg PK, Agrawal NK, et al. From Visual
Surveillance to Internet of Things: Technology and
Applications: CRC Press: 2019. A Foresight on
E-Healthcare Trailblazers; p 235.
39. BAGMO. Available online: https://www.bagmo.in/.
Accessed Nov 7, 2020.
40. Prantaeis. Available online: https://www.prantae.solutions/
contact_prantae. Accessed Nov 7, 2020.
41. Available online: https://mybiocalculus.com/. Accessed
Nov 7, 2020.
42. Gopura RC, Bandara SV, Kiguchi K, et al. Developments
in hardware systems of active upper-limb exoskeleton
robots: A review. Robot Auton Syst 2016;75:203-20.
43. Vaishya R, Javaid M, Khan IH, et al. Articial intelligence
(AI) applications for COVID-19 pandemic. Diabetes
Metab Syndr 2020;14:337-9.
44. Mohapatra I, Giri P. Articial Intelligence in Healthcare
and Application in the Fight against Current Pandemic-
COVID-19. National Journal of Research in Community
Medicine 2020;9:81-4.
45. Verma A, Rao K, Eluri V, et al. Regulating AI in Public
Health: Systems Challenges and Perspectives. ORF
Occasional Paper No 261, Observer Research Foundation.
2020 July.
46. Kalyanakrishnan S, Panicker RA, Natarajan S, et al.
Opportunities and Challenges for Articial Intelligence in
India. Proceedings of the 2018 AAAI/ACM Conference
on AI, Ethics, and Society; 2018:164-70. Available online:
https://www.aies-conference.com/2018/contents/papers/
main/AIES_2018_paper_52.pdf
47. Bali J, Bali RT. India and the Fourth Industrial Revolution:
How we should approach Articial Intelligence in
Healthcare and Biomedical Research? J Assoc Physicians
India 2020;68:72-74
48. Patil, A. Time for Articial Intelligence to Meet
Healthcare Costs. Healthcare Innovation 2018.
Available online: https://www.hcinnovationgroup.com/
population-health-management/article/13030832/time-
for-articial-intelligence-to-meet-healthcare-costs.
Accessed Nov 7 2020.
49. Articial Intelligence in Global Health: Dening a
Collective Path Forward. Washington: The Rockefeller
Foundation 2019. Available online: https://www.usaid.gov/
sites/default/les/documents/1864/AI-in-Global-Health_
webFinal_508.pdf
50. Panch T, Pearson-Stuttard J, Greaves F, et al. Articial
intelligence: opportunities and risks for public health.
Lancet Digit Health 2019;1:e13-e14.
51. Pinninti R, Rajappa S. Articial intelligence in healthcare:
How long to go? Cancer Research, Statistics and
Treatment 2020;3:133-4. Available online: http://www.
crstonline.com/downloadpdf.asp
52. Wahl B, Cossy-Gantner A, Germann S, et al. Articial
intelligence (AI) and global health: how can AI contribute
to health in resource-poor settings? BMJ Glob Health
2018;3:e000798.
53. Mohandas S. AI and healthcare in India: Looking forward.
Roundtable Report. The Centre for Internet and Society,
India. (2017). Available online: https://cis-india.org/
internet-governance/les/ai-and-healthcare-report
54. Kamble SS, Gunasekaran A, Sharma R. Analysis of
the driving and dependence power of barriers to adopt

Journal of Hospital Management and Health Policy, 2021Page 10 of 10
© Journal of Hospital Management and Health Policy. All rights reserved. J Hosp Manag Health Policy 2021;5:28 | http://dx.doi.org/10.21037/jhmhp-20-126
industry 4.0 in Indian manufacturing industry. Computers
in Industry 2018;101:107-19.
55. Dixon P. Failure to "Do No Harm" -- India's Aadhaar
biometric ID program and its inability to protect privacy
in relation to measures in Europe and the U.S. Health
Technol (Berl) 2017;7:539-67.
56. Indian healthcare is all set to be transformed. Marketwatch
2019 5 March. Available online: https://www.marketwatch.
com/press-release/indian-healthcare-is-all-set-to-be-
transformed-by-ai-2019-03-05. Accessed Nov 7 2020.
57. India’s bid to harness AI for healthcare. Factor Daily 2019
April 4. Available online: https://factordaily.com/ai-for-
healthcare-in-india/. Accessed Nov 7 2020.
58. CMEE Newsletter: A Special Issue On Understanding
Digital Health: Challenges and Opportunities. Noida:
Centre for Marketing in Emerging Economies (India).
Available online: http://iimlcmee.org/wp-content/
uploads/2020/07/Volume-5-Issue-2-Special-Issue-on-
Digital-Health-Care-Delivery-in-India.pdf. Accessed Nov
7, 2020.
59. Raja R, Mukherjee I, Sarkar BK. A Systematic Review of
Healthcare Big Data. Sci Program 2020. Available online:
https://www.hindawi.com/journals/sp/2020/5471849/.
Accessed Nov 7 2020.
60. Kunnavil R, Murthy NS. Healthcare Data Utilization for
the Betterment of Mankind-An Overview of Big Data
Concept in Healthcare. Int J Healthcare Edu Med Inform
2018;5:14-7.
doi: 10.21037/jhmhp-20-126
Cite this article as: Pradhan K, John P, Sandhu N. Use of
artificial intelligence in healthcare delivery in India. J Hosp
Manag Health Policy 2021;5:28.