Generative AI in healthcare: Diagnosing the future

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  Quantumrun Foresight
• April 2, 2025

Insight summary

Generative artificial intelligence (AI) is improving patient care, speeding up drug development, and reshaping how medical professionals perform their jobs. While patients and healthcare providers could gain from lower costs and more accurate diagnoses, the technology also poses challenges, including privacy risks, errors in medical information, and job displacement. Governments face increased pressure to regulate this technology carefully, balancing patient safety, innovation, and workforce retraining.

Generative AI in healthcare context

Generative AI has quickly become a central focus in healthcare due to its potential to transform clinical decisions, patient experience, and operational efficiency. Generative AI is a type of machine learning technology capable of creating entirely new digital content—such as text, images, audio, or video—based on data patterns learned from vast amounts of existing information. In healthcare, generative AI tools learn from extensive sets of medical data, such as patient records, clinical trial results, and diagnostic images, to produce valuable insights or new healthcare solutions. For instance, as of December 2023, approximately 70 drugs developed with the assistance of generative AI were already being tested in clinical trials, according to the US Government Accountability Office (GAO).

As healthcare organizations continue to explore generative AI, many have begun implementing or testing solutions in partnership with specialized technology vendors. A March 2024 survey by McKinsey found that more than 70 percent of healthcare leaders in the US are currently experimenting with or have fully adopted generative AI capabilities, often partnering with external vendors to build tailored applications rather than opting for pre-built software. For example, generative AI models are being developed to automate tasks like drafting clinical documentation, reducing administrative workload and addressing clinician burnout.

Despite significant enthusiasm, generative AI in healthcare faces substantial hurdles in data privacy concerns, accuracy of outputs, and regulatory uncertainties. GAO reported in September 2024 that generative AI tools often generate plausible yet incorrect results—known as "hallucinations"—posing serious risks in medical contexts. Additionally, the fragmented and sensitive nature of medical data creates practical difficulties in training reliable models. Recognizing these barriers, a 2024 study published in Implementation Science emphasized that successful adoption of generative AI requires careful management of expectations, robust governance frameworks, and proactive risk mitigation strategies to ensure patient safety and ethical application.

Disruptive impact

People may benefit from quicker and more accurate diagnoses, as generative AI assists doctors in reviewing scans and medical tests. Additionally, patients might see lower healthcare costs, since generative AI can reduce the number of unnecessary procedures or hospital visits through better predictive analytics. However, individuals might face increased privacy risks because generative AI relies on sensitive health data, prompting concerns about data protection. Furthermore, some patients might become overly reliant on automated health recommendations, which could reduce their trust in traditional doctor-patient relationships.

Healthcare providers could adjust their business models, potentially adopting subscription-based services for personalized health assessments or continuous health monitoring. In addition, pharmaceutical companies might use generative AI extensively for faster and cheaper drug discovery processes, requiring companies to rapidly adapt or fall behind. Smaller healthcare startups may find new opportunities in developing specialized services, such as virtual assistants tailored for niche healthcare markets like elder care or chronic disease management. However, businesses may face increased financial and reputational risks if generative AI systems lead to medical mistakes or inaccuracies.

Governments may need to develop new standards ensuring that generative AI products in healthcare are safe, unbiased, and transparent, especially since these systems influence critical medical decisions. They might also invest in national infrastructure to securely manage large-scale health data storage and analysis. Furthermore, countries could begin competing internationally, positioning themselves as leaders in generative AI-enabled healthcare innovation through strategic funding and partnerships with private companies and academic institutions. However, governments also face potential social challenges, such as job displacement among healthcare workers, leading to increased pressure to provide workforce retraining programs.

Implications of generative AI in healthcare

Wider implications of generative AI in healthcare may include: 

• Healthcare providers developing subscription-based personalized health monitoring plans, making preventive care accessible and affordable for consumers.
• Patients increasingly demanding control over their health data, prompting businesses to offer clear, user-friendly transparency settings to stay competitive.
• The government setting new rules for clinical AI tools to disclose clearly when medical advice comes from AI rather than a human professional.
• Better, faster, and more accurate virtual diagnostics, altering traditional hospital revenue streams.
• Reduction in healthcare-related waste and environmental footprint as AI-driven systems optimize supply chains and minimize unnecessary medical tests.
• A shift in medical training programs emphasizing skills for collaboration with generative AI systems rather than memorization of medical facts alone.
• Increased healthcare access for rural and underserved areas as generative AI allows less experienced practitioners to provide accurate diagnoses, reducing geographic healthcare disparities.
• Politicians becoming pressured by the public to expand healthcare coverage as generative AI makes effective medical interventions more affordable and accessible.
• Healthcare workers, especially administrative staff, facing job disruptions due to automation of routine tasks like billing and paperwork preparation.
• Pharmaceutical firms facing political and public pressure to make new drugs cheaper since generative AI significantly cuts research costs and time to market.

Questions to consider

• How could generative AI change your relationship with healthcare providers and your expectations about medical treatment?
• How could generative AI improve healthcare access or create new inequalities within your local community?