Artificial Intelligence is revolutionising healthcare by offering solutions to issues of the industry. Despite the encouraging developments, significant challenges still impede the widespread development and adoption by healthcare professionals. One of the major challenges is data availability due to its ethical and privacy concerns, potential misuse, lack of standardisation and unorganised datasets. To empower medical AI, we must prioritise the organization of healthcare data. Robust data security measures, mitigating fears of misuse with clear ethical guidelines, innovative solutions that balance privacy, security, and accessibility, and nurturing a well-organized data ecosystem can be the measures to address these issues that can help convince healthcare professionals of its potential. Open discussions and collaboration among stakeholders can help navigate the ethical and regulatory complexities of AI in healthcare. Following are some of the measures to address data concerns in healthcare AI.        

• Institutions should take the responsibility for organising, storing and sharing institutional data to facilitate AI research and development. Data sharing initiatives by academia and hospitals on a secure platform, keeping patient privacy through anonymized data, can augment the research and development process.
• Synthetic data generation with the creation of realistic datasets would reduce dependence on real-world data.     
• Establishing standardised data formats, robust data cleaning and preprocessing tools, data provenance and lineage tracking preserve the quality of data.        
• Differential privacy, homomorphic encryption, and secure multi-party computation measures would address the data privacy and security concerns.        
• Infrastructure to develop secure, scalable and interoperable data would support storage, management and analysis of data.

Awareness initiatives to convince of the evidence supporting the effectiveness of AI will help mitigate the hesitation among healthcare professionals. Public-private partnerships are vital to invest in AI, adopting best practices and mitigating regulatory hurdles. Shared ownership and collective responsibility among various stakeholders including AI developers, healthcare institutions, hospitals, and the public is essential for fostering the ethical and responsible development and deployment of AI in healthcare.

Indian datasets empower tailored AI solutions that understand and address the specific needs and challenges of the Indian healthcare landscape. Indian datasets provide valuable resources for AI solutions which are finely tuned to the nuances of the Indian healthcare landscape, eventually leading to better-targeted interventions, improved patient outcomes, and enhanced healthcare delivery across the country.

Addressing data issues with the amalgamation of tech solutions, ethical considerations, and collaboration will transform healthcare AI from a promising concept to a powerful reality. With the pace with which AI is dominating, the risk of falling behind prevails, hence mechanisms to address issues and adopt become imperative.

1. https://bmcmededuc.biomedcentral.com/articles/10.1186/s12909-023-04698-z 2.https://www.questionpro.com/blog/synthetic-data-generation/