AI in Healthcare: Saving Lives, Money, and Facing Challenges

Published Date : 20/07/2025 

Artificial intelligence (AI) has the potential to revolutionize healthcare by saving lives and reducing costs. However, its widespread adoption faces several technical and ethical challenges. 

Imagine walking into your doctor’s office feeling sick – and rather than flipping through pages of your medical history or running tests that take days, your doctor instantly pulls together data from your health records, genetic profile, and wearable devices to help decipher what’s wrong.

This kind of rapid diagnosis is one of the big promises of artificial intelligence for use in healthcare. Proponents of the technology say that over the coming decades, AI has the potential to save hundreds of thousands, even millions of lives.

What’s more, a 2023 study found that if the healthcare industry significantly increased its use of AI, up to US$360 billion annually could be saved. However, despite the widespread use of AI in other sectors, its impact on healthcare so far has been relatively low.

A 2024 American Medical Association survey found that 66% of U.S. physicians had used AI tools in some capacity, up from 38% in 2023. But most of it was for administrative or low-risk support. And although 43% of U.S. healthcare organizations had added or expanded AI use in 2024, many implementations are still exploratory, particularly when it comes to medical decisions and diagnoses.

Artificial intelligence excels at finding patterns in large sets of data. In medicine, these patterns could signal early signs of disease that a human physician might overlook – or indicate the best treatment option, based on how other patients with similar symptoms and backgrounds responded. Ultimately, this will lead to faster, more accurate diagnoses and more personalized care.

AI can also help hospitals run more efficiently by analyzing workflows, predicting staffing needs, and scheduling surgeries so that precious resources, such as operating rooms, are used most effectively. By streamlining tasks that take hours of human effort, AI can let healthcare professionals focus more on direct patient care.

But for all its power, AI can make mistakes. Although these systems are trained on data from real patients, they can struggle when encountering something unusual, or when data doesn’t perfectly match the patient in front of them. As a result, AI doesn’t always give an accurate diagnosis. This problem is called algorithmic drift – when AI systems perform well in controlled settings but lose accuracy in real-world situations.

Racial and ethnic bias is another issue. If data includes bias because it doesn’t include enough patients of certain racial or ethnic groups, then AI might give inaccurate recommendations for them, leading to misdiagnoses. Some evidence suggests this has already happened.

Healthcare systems are labyrinthine in their complexity. The prospect of integrating artificial intelligence into existing workflows is daunting; introducing a new technology like AI disrupts daily routines. Staff will need extra training to use AI tools effectively. Many hospitals, clinics, and doctor’s offices simply don’t have the time, personnel, money, or will to implement AI.

Also, many cutting-edge AI systems operate as opaque “black boxes.” They churn out recommendations, but even its developers might struggle to fully explain how. This opacity clashes with the needs of medicine, where decisions demand justification. Developers are often reluctant to disclose their proprietary algorithms or data sources, both to protect intellectual property and because the complexity can be hard to distill. The lack of transparency feeds skepticism among practitioners, which then slows regulatory approval and erodes trust in AI outputs.

There are also privacy concerns; data sharing could threaten patient confidentiality. To train algorithms or make predictions, medical AI systems often require huge amounts of patient data. If not handled properly, AI could expose sensitive health information, whether through data breaches or unintended use of patient records. U.S. regulations such as the HIPAA law impose strict rules on health data sharing, which means AI developers need robust safeguards.

Privacy concerns also extend to patients’ trust: If people fear their medical data might be misused by an algorithm, they may be less forthcoming or even refuse AI-guided care. The grand promise of AI is a formidable barrier in itself. Expectations are tremendous. AI is often portrayed as a magical solution that can diagnose any disease and revolutionize the healthcare industry overnight. Unrealistic assumptions like that often lead to disappointment. AI may not immediately deliver on its promises.

Finally, developing an AI system that works well involves a lot of trial and error. AI systems must go through rigorous testing to make certain they’re safe and effective. This takes years, and even after a system is approved, adjustments may be needed as it encounters new types of data and real-world situations.

Today, hospitals are rapidly adopting AI scribes that listen during patient visits and automatically draft clinical notes, reducing paperwork and letting physicians spend more time with patients. Surveys show over 20% of physicians now use AI for writing progress notes or discharge summaries. AI is also becoming a quiet force in administrative work. Hospitals deploy AI chatbots to handle appointment scheduling, triage common patient questions, and translate languages in real time.

Clinical uses of AI exist but are more limited. At some hospitals, AI is a second eye for radiologists looking for early signs of disease. But physicians are still reluctant to hand decisions over to machines; only about 12% of them currently rely on AI for diagnostic help.

Suffice to say that healthcare’s transition to AI will be incremental. Emerging technologies need time to mature, and the short-term needs of healthcare still outweigh long-term gains. In the meantime, AI’s potential to treat millions and save trillions awaits. 

Frequently Asked Questions (FAQS): 

Q: What is the potential impact of AI in healthcare?

A: AI has the potential to save hundreds of thousands, even millions of lives, and could save up to US$360 billion annually in healthcare costs.

Q: What are the main challenges in adopting AI in healthcare?

A: The main challenges include technical limitations, racial and ethnic bias, data privacy concerns, and the complexity of integrating AI into existing healthcare workflows.

Q: How does AI help in medical diagnosis?

A: AI can find patterns in large sets of data that might indicate early signs of disease, leading to faster and more accurate diagnoses and personalized care.

Q: What are some current uses of AI in healthcare?

A: Current uses of AI include AI scribes for drafting clinical notes, chatbots for administrative tasks, and AI as a second eye for radiologists looking for early signs of disease.

Q: Why is the transition to AI in healthcare gradual?

A: The transition is gradual due to the need for rigorous testing, the complexity of healthcare systems, and the need to address ethical and privacy concerns. 

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