Skip to main content
NCBI home page
Indian Journal of Community Medicine: Official Publication of Indian Association of Preventive & Social Medicine logoLink to Indian Journal of Community Medicine: Official Publication of Indian Association of Preventive & Social Medicine
. 2024 Dec 30;49(Suppl 2):S210–S216. doi: 10.4103/ijcm.ijcm_806_24

AI Horizons in Indian Healthcare: A Vision for Transformation and Equity

Neelesh Kapoor 1, S N Sanjana 1,✉, Shubha B Davalagi 1, P S Balu 2, Soumitra Sethia 3
PMCID: PMC11927818  PMID: 40124859

Abstract

Artificial intelligence (AI) is poised to revolutionize healthcare delivery in India, offering solutions to address the nation’s unique healthcare challenges. This position paper, presented by the Indian Association of Preventive and Social Medicine, examines the integration of AI in Indian healthcare, exploring its applications across diagnostic imaging, patient care, medical research, rehabilitation, and administrative processes. Notable implementations include AI-driven disease detection systems, telemedicine platforms, and public health surveillance tools, with successful applications in tuberculosis screening, breast cancer detection, and ophthalmological care. While these advancements show promise, significant challenges persist, related to data privacy concerns and interoperability issues, including the need for robust ethical frameworks. The paper highlights key stakeholder collaborations, including government initiatives and international partnerships, which are driving innovation in this space. Based on this analysis, we propose policy recommendations emphasizing research investment, professional training, and regulatory frameworks to ensure responsible AI adoption. Our vision advocates for an approach that balances technological advancement with accessibility and equity in healthcare delivery.

Keywords: Artificial intelligence, digital health, healthcare, India, medical innovation, public health

“The success in creating effective AI could be the greatest event in the history of our civilization, or the worst.”

- Stephen Hawking

INTRODUCTION

Artificial intelligence (AI) has been set to have a metamorphic force globally, revolutionizing various sectors, and the healthcare industry is no exception. The potential of AI in healthcare presents significant possibilities for enhancing patient and clinical team results, cutting down expenses, and impacting population health positively.[1] As we stand at the brink of a new era in medicine, it is vital to examine the role of AI in health and, more specifically, its potential impact on the healthcare landscape in India. This position paper, presented on behalf of the Indian Association of Preventive and Social Medicine, seeks to delve into the complexities of integrating AI into the Indian healthcare system, examining its potential benefits and challenges and proposing a roadmap for a harmonious coexistence.

Relevance of AI in the Indian healthcare context

India, with its vast and diverse population, faces unique challenges in healthcare delivery, including accessibility, affordability, and the burden of diseases. The integration of AI in the healthcare sector holds immense promise for addressing these challenges. As per a report by NITI Aayog, the government think tank of India, AI in healthcare can lead to a threefold increase in the gross domestic product (GDP) of the country by 2035.[2] The report also emphasizes the key role of AI in enhancing preventive care, diagnostics, and treatment outcomes, thereby contributing to the overall health and wellbeing of the population.

Purpose and scope of the position paper

The purpose of this position paper is to provide a comprehensive understanding of the current landscape of AI in healthcare, specifically tailored to the Indian context. By examining the global successes and challenges, we aim to derive insights that can help shape the development and implementation of AI strategies within the Indian healthcare system. The scope of this paper is not limited to the technological aspects of AI but also touches upon the socioeconomic, ethical, and regulatory dimensions which remain crucial for a holistic approach for integrating AI in healthcare.

HISTORICAL CONTEXT

Professor John McCarthy, regarded as the father of artificial intelligence, first used the term in the mid-1950s and defined it as “the science and engineering of making intelligent machines, especially intelligent computer programs.”[3] But historically, it was Alan Turing who first raised the possibility of machines being capable of simulating actual human thinking and behavior way back in 1950.[4] However, the advent of AI can be attributed to the famous work on neural networks by Warren McCulloch and Walter Pitts as early as 1943.[5]

Quest for intelligent systems in medicine gained momentum in the middle of the last century with potential applications being explored in every field of medicine.[6,7]

One of the early ventures into AI was by Stanford University through its SUMEX-AIM in 1973, which provided a collaborative computer system to enhance communication and research across clinical and biomedical fields.[8]

By 1976, Gunn had successfully explored acute abdominal pain diagnosis using computer analysis.[9] CASNET model was the first application of AI in medicine by consulting for glaucoma in 1978.[10] Another notable effort was put in by the University of Massachusetts by developing a decision support system named Dxplain, which used symptoms as inputs to generate differential diagnosis.[11]

In the past 2 decades, AI made exceptional progress first with the launch of Watson by IBM an NLP-based technology[12] and then the Chat GPT by Open AI, an LLM-based technology.[13]

SOME BASIC AI TERMS FOR COMMON UNDERSTANDING

Artificial Intelligence: “A.I. is an interdisciplinary field spanning computer science, psychology, linguistics, and philosophy, among others. According to its simplest definition, artificial intelligence (A.I.) is intelligence demonstrated by machines. It is sometimes also described as “machines that mimic cognitive functions that humans associate with the human mind, such as learning and problem solving”[14]

Machine Learning (ML): “Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.”[15]

Deep Learning (DL): “Deep learning is a method in artificial intelligence (AI) that teaches computers to process data in a way that is inspired by the human brain. Deep learning models can recognize complex patterns in pictures, text, sounds, and other data to produce accurate insights and predictions.”[16]

Natural Language Processing (NLP): “It is the discipline of building machines that can manipulate human language — or data that resembles human language — in the way that it is written, spoken, and organized.”[17]

Large Language Models (LLM): “A large language model (LLM) is a type of artificial intelligence (AI) algorithm that uses deep learning techniques and massively large data sets to understand, summarize, generate and predict new content.”[18]

Convolutional Neural Network (CNN): “It is a Deep Learning algorithm that can take in an input image, assign importance (learnable weights and biases) to various aspects/objects in the image, and be able to differentiate one from the other.”[19]

CURRENT LANDSCAPE OF AI IN INDIAN HEALTH CARE

As per Nasscom by 2025, AI and data have been expected to contribute 25–30 billion USD$ to India’s GDP.[20] Ayushmann Bharat Digital Mission has catapulted the trajectory of AI-based health innovations significantly in India. Initially, AI applications were limited, focusing on basic data analytics and administrative tasks.[21] However, with the rapid advancements in ML and DL algorithms, AI is making substantial contributions to disease prediction, personalized treatment plans, and medical imaging analysis. Telemedicine platforms, wearable devices, and AI-driven diagnostics are becoming integral components of the Indian healthcare ecosystem.[22]

OPPORTUNITIES AND INNOVATIONS IN HEALTH CARE

The applications of AI in healthcare can broadly be categorized as per Figure 1.

Figure 1.

Figure 1

Open in a new tab

Applications of AI in healthcare

  1. Imaging and Diagnostics

    The most promising applications of AI have shown to be in diagnostic imaging and early detection of disease, mounting attention is being directed at fine tuning varied AI or ML products to detect and quantify a wide array of clinical conditions.[23] To name a few, companies like Qure.ai, Niramai, and Artelus have made major strides in application of AI ML for specific diseases. Qure.ai for instance is an AI-based health application helping in diagnosing and identifying diseases such as tuberculosis, heart failure, or stroke using radiological images.[24] Niramai has broken barriers with its novel radiation-free, painless, touchless medical device for early detection of breast cancer.[25] Artelus and Remidio have democratized tertiary eye care and are pioneering in remote ophthalmic screening coupled with telemedicine.[26,27]

    The abovementioned applications are just a few examples of upcoming disease centric innovations in AI/ML which are only set to grow and expand further.

    The results seem to be encouraging when it comes to early identification and screening of diseases such as skin and breast cancer, pneumonia, disease of eye, psychotic disease, and neurological diseases like Parkinson’s disease, which largely pose a public health problem in our country.[28,29]

  2. Patient care and engagement

    While it is important to look at disease-specific solutions, it is equally important to bring together the iron triangle of healthcare – access, cost, and quality. India with its doctor patient ratio of more than 11,000 can accelerate Universal Health Coverage only when we start leveraging and adapting to the digital solutions at our disposal. The Ayushman Bharat Digital Mission is laying the foundation for the very future of a healthcare revolution or a UPI moment in healthcare by encouraging healthcare stakeholders to create more digital health transactions. This will not only ensure interoperability across various health systems but also greatly enhance reach to doctors via telemedicine and better patient engagement with vital or pill tracking on a patient’s mobile. There are already quite a few examples of such horizontal innovations looking at healthcare holistically. With the advent of more sophisticated wearable technologies and virtual care, standards of care for patient management are undergoing a huge transformation.[30]

  3. Medical Research

    Extracting meaning from big data is an essential and fundamental application of AI in medical research. Digital health data are practically a gold mine of medical science where ML can be used to derive key information on patterns of treatment, diagnoses, and disease progression which can enable identification of drug interactions and side effects. A clear example of this can be witnessed in United Kingdom, where their electronic health records databases have helped pinpoint comorbidity clusters in autism spectrum disorders and type 2 diabetes subgroups.[31]

    Pharmaceutical agencies are now shifting their focus on AI to optimize drug development processes and identify potential drug molecules. Predictive analytics can be used to recognize right aspirants for clinical trials.[30]

  4. Rehabilitation

    Integration of AI in rehabilitation has innumerable opportunities to improve the overall effectiveness, accessibility, and personalization of rehabilitation services. From simple AI-assisted chatbots to virtual reality-assisted cognitive rehabilitation to predictive analytics for recovery, AI is a promising tool to augment and fast track rehabilitation.

  5. Administrative

    Administrative overhead can be cut down to a large extent through automation by extracting data from therapeutic notes, pulling key vital data from past medical notes and gathering patient encounter information.[32]

As healthcare professionals across India increasingly embrace digital practices, the associated administrative burdens are experiencing a noteworthy reduction. For instance, Eka Care, an Electronic Medical Records (EMR) platform, goes beyond conventional EMRs by incorporating automated follow-up reminders for patients. It utilizes advanced technologies such as Optical Character Recognition (OCR) and Natural Language Processing (NLP) to interpret and link patients’ medical records to their unique ABHA ID. The platform features a Digilocker, providing patients with a secure repository for their longitudinal medical history. This comprehensive approach not only streamlines the operational aspects of medical practices but also signifies a significant stride toward modernized healthcare management in the digital age.

APPLICATION OF AI IN PUBLIC HEALTH

AI has wide applications in public health. Dinah V Parums in an extensive editorial outlined application of AI during COVID-19 pandemic in areas like identification of disease outbreaks, development of vaccines, and public health surveillance.[33] Sundermann et al.[34] demonstrated use of ML in hospital EMR systems in detection of multiple new outbreaks which could not be identified using traditional methods in hospital settings. Pascucci M et al.[35] through seminal work on computer vision technology developed a mobile app to conduct antibiogram analysis and hence identification of antimicrobial resistance with a high degree of sensitivity and specificity. By ML technologies and automated classification, AI researchers have used Internet media reports for global infectious disease monitoring.[36] Healthmap is an Internet-based infectious disease surveillance system which uses AI to extract geographical data to identify clusters of infectious disease cases[37] and was able to generate alerts about SARS CoV-2 just a few days after the first case of COVID-19.[38] Guo P et al.[39] developed a surveillance system based on Baidu search engine queries using an elastic net regression model to predict or track influenza epidemics. Chiu HR et al.[40] experimented with ML-based prediction models to identify decision rules which can guide health professionals to make optimum use of available medical resources and public health needs in different stages of an epidemic response. Jungwirth and Haluza experimented with GPT-3 to demonstrate that AI can successfully play the role of a public health researcher.[41] Chu KH leveraged ML to do a twitter sentiment analysis and identified target cohorts for antitobacco public health campaigns.[42] Khoury MJ proposed how AI-based advances in precision medicine can eventually lead to “Precision Public Health.”[43] In India too, the Ministry of Health and Family welfare is exploring the application of AI in public health in collaboration with NITI Ayog. In collaboration with Department of Biotechnology, NITI Aayog is building a comprehensive database of cancer-related pathology and radiology images of more than 20,000 patient profiles with focus on the major cancers prevalent in India. These will eventually be exposed to AI technologies to build sustainable solutions for local needs.[44] Wadhwani AI has pioneered an AI solution to predict the risk of “loss to follow-up” and mortality among TB patients when they start TB treatment.[45]

CHALLENGES OF AI IN HEALTHCARE

The adoption of AI in healthcare comes with various challenges that need addressing to ensure effective and responsible implementation. Some key challenges include:

  • Data Privacy and Security: AI systems often depend on sizeable datasets for their training and validation. Maintaining the privacy and security of sensitive patient data is critical for both preserving patient trust and complying with laws like HIPAA (Health Insurance Portability and Accountability Act).[46]

  • Interoperability and Standardization: Healthcare systems are built on diverse technologies complicating the integration of AI solutions. Ensuring there is standardization and interoperability is key for effective adoption.[47]

  • Ethical and Regulatory Compliance/Responsible AI: The ethical use of AI in healthcare is a significant concern. Establishing guidelines for responsible AI development, addressing biases in algorithms, and complying with ethical standards and regulations are critical to prevent unintended consequences.[48]

  • Limited Clinical Validation: AI algorithms often lack rigorous validation in real-world clinical settings. The transition from research and development to practical clinical application requires robust validation studies to ensure accuracy and effectiveness.[49]

  • Resistance to Adoption and Trust Issues: Healthcare professionals may be resistant to adopting AI due to a lack of understanding, fear of job displacement, or concerns pertaining to the reliability of AI-generated recommendations. Building trust with both the medical community and patients is key for successful uptake of new technologies.[50]

  • Algorithm Bias and Fairness: Biases in training data can be transferred to AI algorithms, leading to unreliable healthcare outcomes. Addressing algorithmic bias and ensuring fairness in AI applications is essential to avoid exacerbating existing health disparities.[51]

  • Explainability and Interpretability: Due to the sophisticated nature of their architectures, AI models, especially DL models, are often regarded as “black boxes”. Ensuring the explainability and interpretability of AI-driven decisions is crucial for gaining trust and understanding among healthcare professionals.[52]

  • Resource Constraints: Many healthcare institutions, particularly those with a resource crunch, may not have the infrastructure, expertise, or financial resources needed to implement and maintain AI systems. Bridging these resource gaps is a significant challenge.[53]

STAKEHOLDER ENGAGEMENT AND COLLABORATIONS

Public–Private Partnerships in AI Research and Implementation: Government of India is collaborating with private entities to pool resources, expertise, and data, enabling the development of robust AI algorithms and systems. These partnerships are crucial for addressing healthcare challenges, enhancing infrastructure, and ensuring the ethical deployment of AI technologies. Some eminent examples from India are:

  • National Institution for Transforming India (NITI Aayog) and Tech Industry Collaboration with entities like IBM and Microsoft for joint research, skill development programs, and the implementation of AI solutions to enhance healthcare delivery.[54]

  • Apollo Hospitals and Microsoft’s AI-driven Predictive Analytics for early detection of cardiac diseases.[55]

  • Various state health departments have collaborated with private tech providers to analyze diagnostic data and streamline patient records and enhance the efficiency of public health programs.[56]

  • Ayushman Bharat, India’s flagship healthcare scheme, partnered with private entities to implement AI-based tools for patient identification, fraud detection, and treatment optimization.[57]

International collaboration in AI for health

Collaborating with international organizations, research institutions, and technology companies facilitates the exchange of knowledge, best practices, and innovative solutions. Such collaborations position India as an active contributor to the global discourse on AI in healthcare while benefiting from shared insights and advancements.

US India Artificial Intelligence (USIAI) Initiative: To direct efforts toward AI collaboration in key sectors which hold significance for both countries, including health, agriculture, energy, manufacturing, or smart cities.[58]

India–Sweden Healthcare Innovation Centre: Is a tripartite collaboration between the Swedish Trade Commissioner’s Office, AIIMS, New Delhi and AIIMS, Jodhpur, ICMR, Ministry of Health and Family Welfare – India, and Ministry of Health and Social Affairs – Sweden, AstraZeneca, and NASSCOM. By providing clinical validation, access to funding and opportunities for International Expansion Centre promotes and accelerates growth of startups.[59]

WHO Collaborative Initiatives: India is actively participating in collaborative initiatives led by the World Health Organization (WHO), which focuses on utilizing AI for global health challenges.[60] Partnerships with Global Pharma Companies: Indian pharmaceutical companies like Cipla, Sun Pharma, and Dr. Reddy’s laboratories have collaborated with global partners to integrate AI into drug discovery and development processes.[61]

Global Partnership on AI (GPAI): India has collaborated with Global Partnership on Artificial Intelligence, which aims to ensure responsible development and implementation of AI based on human rights, inclusiveness, diversity, creativity, and economic prosperity.[62] These diverse international collaborations in AI for health in India emphasize the importance of global partnerships in advancing research, technology, and addressing healthcare challenges on a broader scale.

Future roadmap

The vision for the future of AI in Indian healthcare is one of profound optimism and innovation. As technology continues to evolve, AI stands poised to radically transform healthcare in India, making it more individual-centric, personalized, accessible, and efficient. In the coming years, we envision AI playing a pivotal role in preventive healthcare, with advanced analytics identifying potential health risks at an early stage and enabling timely interventions. Treatment strategies will become increasingly tailored to individual patient profiles, optimizing outcomes and alleviating the pressure on the existing healthcare system. Moreover, AI is anticipated to enhance diagnostic accuracy, particularly in resource-constrained regions, ensuring that even remote populations receive timely and accurate medical attention. AI’s integration will not only optimize administrative functions but also provide healthcare professionals with actionable insights, shaping a collaborative and patient-centered environment. Embracing a future where AI operates ethically and inclusively, the Indian healthcare sector can transform challenges into opportunities, ultimately realizing a vision of comprehensive, technologically advanced, and equitable healthcare for all.

POLICY RECOMMENDATIONS

Policy recommendations for the Government in the realm of AI in healthcare are paramount to fostering a robust and responsive healthcare ecosystem. First and foremost, there is a pressing need to invest in research and application initiatives that explore the full potential of AI in addressing healthcare challenges. Establishing dedicated research centers and collaborations with academic institutions can propel innovative solutions and pave the way for cutting-edge AI applications in diagnostics, treatment, and preventive healthcare. Work already begun at different IITs and AIIMS needs to be expanded to state level institutions. To make the most of AI advancements, healthcare professionals must be equipped with the right skills through prioritized reskilling and training programs. Accelerating the adoption of AI in health facilities nationwide is imperative, necessitating the development of clear guidelines and incentives for implementation. This involves creating a supportive regulatory framework that encourages collaboration between the public and private sectors. Moreover, the Government must emphasize responsible AI development by enforcing ethical standards and ensuring transparency in AI algorithms. By actively engaging in these areas, the Government can play a key role in steering India toward a future where AI is seamlessly integrated into the healthcare landscape, ensuring improved patient outcomes and a more resilient healthcare system.

CONCLUSION

In conclusion, this comprehensive position paper underscores the transformative potential of incorporating AI into the Indian healthcare system hold. As we navigate the complex landscape of healthcare challenges, AI emerges as a powerful ally capable of addressing disparities, improving patient outcomes, and enhancing overall health infrastructure. The findings and recommendations presented herein underscore the urgent need for a concerted effort by policymakers, healthcare professionals, and technology experts to harness the benefits of AI responsibly. By embracing a collaborative approach, fostering interdisciplinary partnerships, and prioritizing ethical considerations, the Indian healthcare sector can embark on a trajectory of innovation and resilience. The Indian Association of Preventive and Social Medicine advocates for a future where AI not only boosts clinical capabilities but also simultaneously contributes to a more equitable and accessible healthcare landscape. This position paper serves as a call to action, urging stakeholders to collectively seize the opportunities presented by AI to build a healthier and more prosperous India.

Conflicts of interest

There are no conflicts of interest.

Funding Statement

Nil.

REFERENCES

  • 1.Matheny ME, Whicher D, Thadaney Israni S. Artificial intelligence in health care: A report from the national academy of medicine. JAMA. 2020;323:509–10. doi: 10.1001/jama.2019.21579. [DOI] [PubMed] [Google Scholar]
  • 2.NITI Aayog, Government of India “National Strategy for Artificial Intelligence. 2018. Available from: https://niti.gov.in/writereaddata/files/document_publication/NationalStrategy-for-AIDiscussion-Paper.pdf . [Last accessed on 2023 Nov 28]
  • 3.Rajaraman V. John McCarthy — Father of artificial intelligence. Resonance. 2014;19:198–207. [Google Scholar]
  • 4.Turing AM. Computing machinery and intelligence. Mind. 1950;236:433–60. [Google Scholar]
  • 5.McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. Bull Math Biol. 1943;5:115–33. [PubMed] [Google Scholar]
  • 6.Lusted LB. Medical progress-medical electronics. N Engl J Med. 1955;252:580–5. doi: 10.1056/NEJM195504072521405. [DOI] [PubMed] [Google Scholar]
  • 7.Ledley RS, Lusted LB. Reasoning foundations of medical diagnosis. Science. 1959;130:9–21. doi: 10.1126/science.130.3366.9. [DOI] [PubMed] [Google Scholar]
  • 8.Kulikowski CA. An opening chapter of the first generation of artificial intelligence in medicine: The first Rutgers AIM Workshop, June 1975. Yearb Med Inform. 2015;10:227–33. doi: 10.15265/IY-2015-016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Gunn AA. The diagnosis of acute abdominal pain with computer analysis. J R Coll Surg Edinb. 1976;21:170–2. [PubMed] [Google Scholar]
  • 10.Weiss S, Kulikowski CA, Safir A. Glaucoma consultation by computer. Comput Biol Med. 1978;8:25–40. doi: 10.1016/0010-4825(78)90011-2. [DOI] [PubMed] [Google Scholar]
  • 11.The Massachusetts General Hospital Laboratory of Computer Science. Dxplain [Internet] 2017. Available from: http://www.mghlcs.org/projects/dxplain . [Last accessed on 2024 Jul 18]
  • 12.Ferrucci DL A, Bagchi S, Gondek D, Mueller ET. Watson: Beyond jeopardy! Artif Intell. 2013:199–200. 93–105. [Google Scholar]
  • 13.Brown T, Mann B, Ryder N, Subbiah M, Kaplan JD, Dhariwal P, et al. Language models are few-shot learners [Internet] arXiv. 2020. Available from: https://arxiv.org/abs/2005.14165 . [Last accessed on 2024 Jul 18]
  • 14.Russell S, Norvig P. 4th. Hoboken, NJ: Pearson; 2021. Artificial Intelligence: A Modern Approach. [Google Scholar]
  • 15.Mitchell TM. New York: McGraw-Hill; 1997. Machine Learning. [Google Scholar]
  • 16.Goodfellow I, Bengio Y, Courville A. Cambridge, MA: MIT Press; 2016. Deep Learning. [Google Scholar]
  • 17.Jurafsky D, Martin JH. 3rd. Hoboken, NJ: Pearson; 2023. Speech and Language Processing. [Google Scholar]
  • 18.Bommasani R, Hudson DA, Adeli E, Altman R, Arora S, von Arx S, et al. On the Opportunities and Risks of Foundation Models. arXiv. 2021. Available from: https://arxiv.org/abs/2108.07258 . [Last accessed on 2024 Nov 27]
  • 19.LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436–44. doi: 10.1038/nature14539. [DOI] [PubMed] [Google Scholar]
  • 20.NASSCOM How AI is transforming the future of healthcare in India. NASSCOM. 2023. Available from: https://nasscom.in/knowledge-center/publications/how-ai-transforming-future-healthcare-india . [Last accessed on 2024 Nov 27]
  • 21.Kumar A, Jain P, Kumar N. 1st. Routledge; 2021. Artificial Intelligence and Global Society: Impact and Practices. [Google Scholar]
  • 22.Roy M, Jamwal M. An overview of artificial intelligence (AI) intervention in Indian healthcare system. Int J Sci Res. 2020;9:1217–25. [Google Scholar]
  • 23.The Lancet Digital Health. [Internet] Available from: https://www.thelancet.com/journals/landig/home . [Last accessed on 2024 Jul 18]
  • 24.Engle E, Gabrielian A, Long A, Hurt DE, Rosenthal A. Performance of Qure.ai automatic classifiers against a large annotated database of patients with diverse forms of tuberculosis. PLoS One. 2020;15:e0224445. doi: 10.1371/journal.pone.0224445. doi: 10.1371/journal.pone.0224445. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Singh A, Bhat V, Sudhakar S, Namachivayam A, Gangadharan C, Pulchan C, et al. Multicentric study to evaluate the effectiveness of Thermalytix as compared with standard screening modalities in subjects who show possible symptoms of suspected breast cancer. BMJ Open. 2021;11:e052098. doi: 10.1136/bmjopen-2021-052098. doi: 10.1136/bmjopen-2021-052098. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Kesavadev J, Krishnan G, Mohan V. Digital health and diabetes: Experience from India. Ther Adv Endocrinol Metab. 2021;12:20420188211054676. doi: 10.1177/20420188211054676. doi: 10.1177/20420188211054676. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27. Available from: https://artelus.com/images/publications/papers/the-learning-computer-model.png .
  • 28.Bedi G, Carrillo F, Cecchi GA, Slezak DF, Sigman M, Mota NB, et al. Automated analysis of free speech predicts psychosis onset in high-risk youths. NPJ Schizophrenia. 2015;1:15030. doi: 10.1038/npjschz.2015.30. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.IBM Research IBM 5 in 5: With AI, Our Words Will Be a Window into Our Mental Health. 2017. Available from: https://www.ibm.com/blogs/research/2017/1/ibm-5-in-5-our-words-will-be-the-windows-to-our-mental-health/ . [Last accessed on 2022 Dec 25]
  • 30.Al Kuwaiti A, Nazer K, Al-Reedy A, Al-Shehri S, Al-Muhanna A, Subbarayalu AV, et al. A review of the role of artificial intelligence in healthcare. J Pers Med. 2023;13:951. doi: 10.3390/jpm13060951. doi: 10.3390/jpm13060951. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Wang Z, Shah AD, Tate AR, Denaxas S, Shawe-Taylor J, Hemingway H. Extracting diagnoses and investigation results from unstructured text in electronic health records by semi-supervised machine learning. PLoS One. 2012;7:e30412. doi: 10.1371/journal.pone.0030412. doi: 10.1371/journal.pone.0030412. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Wani SUD, Khan NA, Thakur G, Gautam SP, Ali M, Alam P, et al. Utilization of artificial intelligence in disease prevention: Diagnosis, treatment, and implications for the healthcare workforce. Healthcare. 2022;10:608. doi: 10.3390/healthcare10040608. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Parums DV. Editorial: Infectious disease surveillance using artificial intelligence (AI) and its role in epidemic and pandemic preparedness. Med Sci Monit. 2023;29:e941209. doi: 10.12659/MSM.941209. doi: 10.12659/MSM.941209. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Sundermann AJ, Chen J, Kumar P, Ayres AM, Cho ST, Ezeonwuka C, et al. Whole-genome sequencing surveillance and machine learning of the electronic health record for enhanced healthcare outbreak detection. Clin Infect Dis. 2022;75:476–82. doi: 10.1093/cid/ciab946. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Pascucci M, Royer G, Adamek J, Asmar MA, Aristizabal D, Blanche L, et al. AI-based mobile application to fight antibiotic resistance. Nat Commun. 2021;12:1173. doi: 10.1038/s41467-021-21187-3. doi: 10.1038/s41467-021-21187-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Freifeld CC, Mandl KD, Reis BY, Brownstein JS. HealthMap: Global infectious disease monitoring through automated classification and visualization of Internet media reports. J Am Med Inform Assoc. 2008;15:150–7. doi: 10.1197/jamia.M2544. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Brownstein JS, Freifeld CC, Madoff LC. Influenza A (H1N1) virus, 2009--online monitoring. N Engl J Med. 2009;360:2156. doi: 10.1056/NEJMp0904012. doi: 10.1056/NEJMp0904012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Bhatia S, Lassmann B, Cohn E, Desai AN, Carrion M, Kraemer MUG, et al. Using digital surveillance tools for near real-time mapping of the risk of infectious disease spread. NPJ Digit Med. 2021;4:73. doi: 10.1038/s41746-021-00442-3. doi: 10.1038/s41746-021-00442-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Guo P, Zhang J, Wang L, Yang S, Luo G, Deng C, et al. Monitoring seasonal influenza epidemics by using internet search data with an ensemble penalized regression model. Sci Rep. 2017;7:46469. doi: 10.1038/srep46469. doi: 10.1038/srep46469. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Chiu HR, Hwang CK, Chen SY, Shih FY, Han HC, King CC, et al. Machine learning for emerging infectious disease field responses. Sci Rep. 2022;12:328. doi: 10.1038/s41598-021-03687-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Jungwirth D, Haluza D. Artificial intelligence and public health: An exploratory study. Int J Environ Res Public Health. 2023;20:4541. doi: 10.3390/ijerph20054541. doi: 10.3390/ijerph20054541. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Chu KH, Colditz J, Malik M, Yates T, Primack B. Identifying key target audiences for public health campaigns: Leveraging machine learning in the case of hookah tobacco smoking. J Med Internet Res. 2019;21:e12443. doi: 10.2196/12443. doi: 10.2196/12443. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Khoury MJ, Iademarco MF, Riley WT. Precision public health for the era of precision medicine. Am J Prev Med. 2016;50:398–401. doi: 10.1016/j.amepre.2015.08.031. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.India AI. Available from: https://indiaai.gov.in/ . [Last accessed on 19 Jul 2024]
  • 45.Wadhwani Institute deploys AI solution for TB treatment at Haryana’s government health facilities. Available from: https://health.economictimes.indiatimes.com/news/health-it/wadhwani-institute-deploys-ai-solution-or-tb-treatment-at-haryanas-govt-health-facilities/99296899 . [Last accessed on 19 Aug 2024]
  • 46.Khalid N, Qayyum A, Bilal M, Al-Fuqaha A, Qadir J. Privacy-preserving artificial intelligence in healthcare: Techniques and applications. Comput Biol Med. 2023;158:106848. doi: 10.1016/j.compbiomed.2023.106848. doi: 10.1016/j.compbiomed. 2023.106848. [DOI] [PubMed] [Google Scholar]
  • 47.Baxter SL, Lee AY. Gaps in standards for integrating artificial intelligence technologies into ophthalmic practice. Curr Opin Ophthalmol. 2021;32:431–8. doi: 10.1097/ICU.0000000000000781. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Siala H, Wang Y. SHIFTing artificial intelligence to be responsible in healthcare: A systematic review. Soc Sci Med. 2022;296:114782. doi: 10.1016/j.socscimed.2022.114782. doi: 10.1016/j. socscimed.2022.114782. [DOI] [PubMed] [Google Scholar]
  • 49.Park SH, Choi J, Byeon JS. Key principles of clinical validation, device approval, and insurance coverage decisions of artificial intelligence. Korean J Radiol. 2021;22:442–53. doi: 10.3348/kjr.2021.0048. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Lambert SI, Madi M, Sopka S, Lenes A, Stange H, Buszello CP, et al. An integrative review on the acceptance of artificial intelligence among healthcare professionals in hospitals. NPJ Digit Med. 2023;6:111. doi: 10.1038/s41746-023-00852-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Norori N, Hu Q, Aellen FM, Faraci FD, Tzovara A. Addressing bias in big data and AI for health care: A call for open science. Patterns (N Y) 2021;2:100347. doi: 10.1016/j.patter.2021.100347. doi: 10.1016/j.patter.2021.100347. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Hassija V, Chamola V, Mahapatra A, Singal A, Goel D, Huang K, et al. Interpreting black-box models: A review on explainable artificial intelligence. Cogn Comput. 2024;16:45–74. [Google Scholar]
  • 53.Jia Z, Chen J, Xu X, Kheir J, Hu J, Xiao H, et al. The importance of resource awareness in artificial intelligence for healthcare. Nat Mach Intell. 2023;5:687–98. [Google Scholar]
  • 54.National Strategy for Artificial Intelligence, NITI Aayog. 2018. Available from: https://www.niti.gov.in/sites/default/files/2023-03/National-Strategy-for-Artificial-Intelligence.pdf . [Last accessed on 2023 Nov 29]
  • 55.Microsoft and Apollo Hospitals to use Artificial Intelligence for early detection of cardiac diseases. Microsoft India. Mar 8, 2018. Available from: https://news.microsoft.com/en-in/microsoft-apollo-hospitals-use-artificial-intelligence-early-detection-cardiac-diseases/ . [Last accessed on 2023 Nov 29]
  • 56.Accelerating Digitisation in Healthcare Delivery. National Health Authority. 2022. Available from: https://abdm.gov.in:8081/uploads/Accelerating_Digitisation_in_Healthcare_Delivery_v3_fad6fe700f.pdf . [Last accessed on 2023 Nov 29]
  • 57.Press Information Bureau, Ministry of Health and Family Welfare Use of AI for checking frauds under AB-PMJAY [Internet] 2023. Available from: https://pib.gov.in/PressReleasePage.aspx?PRID=1946706 . [Last accessed on 2024 Jul 18]
  • 58.US India Artificial Intelligence (USIAI) Initiative Department of Science and Technology. Available from: https://dst.gov.in/us-india-artificial-intelligence-usiai-initiative-launched . [Last accessed on 2023 Nov 29]
  • 59.India-Sweden Healthcare Innovation Centre. Available from: https://www.indiaswedenic.com/ . [Last accessed on 2023 Nov 29]
  • 60.World Health Organization; 2021. Global Strategy on Digital Health: 2020–2025. Available from: https://iris.who.int/bitstream/handle/10665/344249/9789240020924-eng.pdf . [Last accessed on 2023 Nov 29] [Google Scholar]
  • 61.Reimagining pharma and healthcare for India@100. 2023. Available from: https://assets.ey.com/content/dam/ey-sites/ey-com/en_in/news/2023/11/ey-re-imagining-pharma-and-healthcare-for-india-100-report.pdf . [Last accessed on 2023 Nov 29]
  • 62.Global Partnership on AI (GPAI) Available from: https://gpai.ai/ . [Last accessed on 2023 Nov 29]

Articles from Indian Journal of Community Medicine: Official Publication of Indian Association of Preventive & Social Medicine are provided here courtesy of Wolters Kluwer -- Medknow Publications

RESOURCES