Expected Outcomes and Suggested Activity Charter of Advisory Board
May 16, 2025
Outcomes
Thought Leadership: Through thought leadership, we aim to amplify the industry's voice in support of responsible AI solutions' implementation. The board will share best practices, success stories, and opinions on the guiding principles and use cases of responsible AI in healthcare through articles, media interviews, and op-eds published in prominent media outlets.
Knowledge Sharing: We will publish quarterly white papers to delve into emerging trends, best practices, and challenges in responsible AI in healthcare. These white papers will be distributed to industry, academia, and government stakeholders, serving as reference documents to guide wider industry adoption of AI tools.
Honouring Ethical Excellence: The board will establish criteria and conduct evaluations for an annual award recognizing healthcare professionals leading responsible AI adoption in India.
Impact Assessment of Responsible AI in Healthcare: Over the long term, the board will design a study to explore how generative AI can enhance the capabilities and efficiency of healthcare professionals.
Overall, the advisory board is dedicated to advancing Responsible AI practices in healthcare, fostering innovation, and improving patient outcomes in the Indian healthcare industry.
Suggested activity charter: 1. Thought Leadership
Members of the board will contribute articles, opinion pieces (op-eds) in prominent media outlets, highlighting the significance of ethical AI implementation in healthcare.
Board members will participate in media interviews to disseminate insights, examples, and viewpoints on responsible AI adoption in healthcare.
Thought leadership content will be disseminated through top media outlets to reach a wide audience.
Shape policies that guide ethical AI deployment, ensuring alignment with societal needs and regulatory framework.
2. Knowledge sharing on implementation of Responsible AI
The board will publish quarterly white papers that explore emerging trends, best practices, and challenges in the field. Each white paper will be publicly accessible and distributed to stakeholders in industry, academia, and government policy makers.
3. Responsible AI in Healthcare Honour Responsible AI ensures that AI systems are developed and used ethically, transparently, and in a way that benefits society. To incentivize and recognize organizations and individuals who are at the forefront of responsible use of AI, the board will formalize criteria and perform evaluation for an annual award for healthcare professionals in India who are utilizing this technology responsibly to solve healthcare challenges in the country.
4. Impact assessment The board will plan and guide a study focused to demonstrate efficacy of GAI in clinical setting. The study will be published in a reputed journal.
Below are some suggested study objectives, outcomes and real-world implications. The board will decide will independently identify and decide the objectives, outcomes and real-world implications.
Suggested study objectives
The primary objective of this study would be to compare the accuracy, bias, efficiency, and ethical considerations of AI-driven decision-making with those made by clinical experts in diagnosis and treatment plans.
Secondary objectives would be to e
valuate the cost-effectiveness of using AI in clinical decision-making, considering factors such as resource utilization, staff burnout and potential cost savings.
Suggested expected Outcomes
Comparative Performance: A comprehensive comparison of the accuracy, bias, and efficiency of AI versus human decisions.
Clinician Decision: Clinicians will assess patients, review their medical records, and make decisions regarding diagnosis, treatment, and management.
AI Decision: The AI model will process patient data and generate potential diagnoses, treatment options, or risk assessments. These recommendations will be presented to clinicians for review and final decision-making.
Real world implications of the study
Practical guidelines for the integration of AI in decision-making processes, including areas where human oversight is critical.
Identification of potential workflows where AI can augment or complement clinical decision-making.