Use of artificial intelligence in healthcare delivery in India

Pradhan, Keerti Bhusan; John, Preethi; Sandhu, Namrata

doi:10.21037/jhmhp-20-126

Cited by 15 publications

(7 citation statements)

References 28 publications

(43 reference statements)

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“…For example, in a country like India with multiple ethnic groups, AI tools need to take into account characteristics of patient ethnicity in the design and development of the tools. 50 Trust in recommendations made by AI is another important factor that may influence adoption. This factor appeared important at ICC where physicians expressed reservations about building trust in technology, especially if used for cancer care.…”

Section: Discussionmentioning

confidence: 99%

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Physicians' Perceptions and Expectations of an Artificial Intelligence-Based Clinical Decision Support System in Cancer Care in an Underserved Setting

et al. 2022

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Objectives Artificial intelligence (AI) tools are being increasingly incorporated into health care. However, few studies have evaluated users' expectations of such tools, prior to implementation, specifically in an underserved setting. Methods We conducted a qualitative research study employing semistructured interviews of physicians at The Instituto do Câncer do Ceará, Fortaleza, Brazil. The interview guide focused on anticipated, perceived benefits and challenges of using an AI-based clinical decision support system tool, Watson for Oncology. We recruited physician oncologists, working full or part-time, without prior experience with any AI-based tool. The interviews were taped and transcribed in Portuguese and then translated into English. Thematic analysis using the constant comparative approach was performed. Results Eleven oncologists participated in the study. The following overarching themes and subthemes emerged from the analysis of interview transcripts: theme-1, “general context” including (1) current setting, workload, and patient population and (2) existing challenges in cancer treatment, and theme-2, “perceptions around the potential use of an AI-based tool,” including (1) perceived benefits and (2) perceived challenges. Physicians expected that the implementation of an AI-based tool would result in easy access to the latest clinical recommendations, facilitate standardized cancer care, and allow it to be delivered with greater confidence and efficiency. Participants had several concerns such as availability of innovative treatments in resource-poor settings, treatment acceptance, trust, physician autonomy, and workflow disruptions. Conclusion This study provides physicians' anticipated perspectives, both benefits and challenges, about the use of an AI-based tool in cancer treatment in a resource-limited setting.

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Section: Discussionmentioning

confidence: 99%

“…For example, in a country like India with multiple ethnic groups, AI tools need to take into account characteristics of patient ethnicity in the design and development of the tools. 50…”

Section: Discussionmentioning

confidence: 99%

Physicians' Perceptions and Expectations of an Artificial Intelligence-Based Clinical Decision Support System in Cancer Care in an Underserved Setting

et al. 2022

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“…A lack of available specialized medical personnel in LMICs, especially in rural regions, is one of the factors contributing to poor cancer outcomes in LMICs [81]. The ability of AI tools, such as WfO, to provide subspecialty treatment information makes such tools a much-needed resource that existing physicians can use to meet population demands, especially in rural areas, as envisioned for the use of AI in an LMIC like India [4,82]. Approximately 60 oncologists serve over 300 million people in West Africa, and only 2000 oncologists are available for 10 million patients in India [83][84][85].…”

Section: Implications For Lmicsmentioning

confidence: 99%

“…Additionally, there is concern about whether AI tools would exacerbate the divide in health care access and use, especially with respect to socioeconomic status. There is a fear that AI would recommend treatment options that patients cannot afford or that only high-income and educated patients who are aware of AI tools may benefit from the use of AI in LMICs [4,19,28] Another important concern-one that applies to a tool such as WfO-is that the data and training of AI tools may not incorporate patient characteristics into treatment recommendations. For example, in China, local patient characteristics such as gene mutations and the weaker physiques of Chinese patients, which can influence treatment recommendations, were not accounted for in WfO recommendations [20].…”

Section: Implications For Lmicsmentioning

confidence: 99%

“…This market growth has been accompanied by both national initiatives for AI and the rapid growth of academic literature on the use of AI in health care. For example, in India, an "AI for All" policy was established along with NITI (National Institution for Transforming India) Aayog-a Government of India think tank for formulating a national strategy for AI [4]. A bibliometric analysis of the literature reported in the Journal of Medical Internet Research found a growth rate of 45.15% in publications from 2014 to 2019, with 70.67% of all publications occurring in the same period [5].…”

Section: Introductionmentioning

confidence: 99%

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Physicians’ Perceptions of and Satisfaction With Artificial Intelligence in Cancer Treatment: A Clinical Decision Support System Experience and Implications for Low-Middle–Income Countries

Care¹,

Care²,

Rocha³

et al. 2022

JMIR Cancer

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As technology continues to improve, health care systems have the opportunity to use a variety of innovative tools for decision-making, including artificial intelligence (AI) applications. However, there has been little research on the feasibility and efficacy of integrating AI systems into real-world clinical practice, especially from the perspectives of clinicians who use such tools. In this paper, we review physicians’ perceptions of and satisfaction with an AI tool, Watson for Oncology, which is used for the treatment of cancer. Watson for Oncology has been implemented in several different settings, including Brazil, China, India, South Korea, and Mexico. By focusing on the implementation of an AI-based clinical decision support system for oncology, we aim to demonstrate how AI can be both beneficial and challenging for cancer management globally and particularly for low-middle–income countries. By doing so, we hope to highlight the need for additional research on user experience and the unique social, cultural, and political barriers to the successful implementation of AI in low-middle–income countries for cancer care.

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