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AI healthcare

Improvement in the Future As more AI is embedded into the healthcare system, it's necessary to address the ethical, technical, and practical problems that come with it. Algorithmic bias is perhaps one of the largest concerns, since it may result in unequal care outcomes. For example, in 2019, a study in Science found that a popular AI system used in U.S. hospitals consistently underestimated the medical needs of Black patients by almost 50% because it had been trained on biased data (Science, 2019). Data privacy is another main issue — medical AI systems are based on huge volumes of patient data, so there must be strict data protection legislation like HIPAA or GDPR. 71% of patients, according to a report by McKinsey published in 2021, would be more confident in trusting AI in medicine if their providers were transparent regarding how their data is being used (McKinsey, 2021). Furthermore, while AI can be utilized to advise on decisions, it should not replace human intelligence. This is where explainable AI becomes vital — physicians need to understand how and why an algorithm ended up with a conclusion before they use it. According to Deloitte's 2024 Consumer Health Care survey, nearly 65% of individuals are at ease when doctors make use of generative AI to assist with interpreting diagnostic test results, provided the technology is employed openly and their personal details are safeguarded properly. Globally, however, accessibility is still a concern. The majority of effective uses of AI are found in high-income nations, with developing areas lagging behind because of insufficient digital infrastructure. The World Health Organization emphasized in its 2021 report that if AI is to be impactful across the globe, countries must invest in digital literacy, data infrastructure, and equitable access to AI tools (WHO, 2021). In the future, developing responsible and inclusive AI is not just a technical problem — it is a moral imperative.

Future Improvement in AI Healthcare: Overcoming Bias, Privacy Risks, and Accessibility Challenges

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