AI in Healthcare: Challenges & Best Practices in 2025

Although AI holds great promise for advancing healthcare systems, its real-world impact has often fallen short of expectations. For instance, less than 1% of AI tools developed during the COVID-19 pandemic were successfully deployed in clinical settings.¹

This highlights a persistent challenge: while AI can enhance diagnostics, treatment, and efficiency, implementing these solutions at scale remains difficult due to barriers like system integration, regulatory hurdles, and limited clinical validation.

Benefits of AI in healthcare

Enhanced diagnostic accuracy

AI tools have demonstrated improved accuracy in diagnosing various conditions. For example, AI systems analyzing medical images can detect diseases such as breast cancer and lung nodules more effectively, aiding healthcare professionals in early diagnosis and treatment planning.

Real-life example:

AI systems used in radiology have shown measurable improvements in detecting diseases from medical images. In recent clinical evaluations, deep learning algorithms outperformed radiologists in identifying subtle patterns in mammograms and CT scans.

For instance, one AI model trained on thousands of annotated mammograms detected early-stage breast cancer with higher sensitivity and fewer false positives than human experts.²

Similarly, AI tools scanning lung images were able to flag small nodules suggestive of malignancy that might have been missed in routine examinations. These advancements have significant implications for early diagnosis and improved patient outcomes.

Personalized treatment strategies

Artificial intelligence enables the development of personalized treatment plans by analyzing patient data, including electronic health records and genetic information.

Real-life example:

Researchers in the UK developed an AI test to analyze genomic and clinical data from men diagnosed with prostate cancer. The goal was to determine who would benefit from abiraterone, a commonly prescribed drug.

The AI model predicted treatment responses with over 85% accuracy, enabling healthcare providers to avoid prescribing the drug to patients unlikely to respond, thereby reducing exposure to side effects and lowering costs. This development supports more efficient, data-driven precision medicine practices in oncology.³

Operational efficiency

AI applications can automate administrative tasks, such as scheduling and documentation, allowing healthcare providers to focus more on patient care. AI-generated post-operative reports are more accurate than those written by surgeons, potentially reducing errors and saving time.

Real-life example:

A comparative study assessed AI-generated post-operative reports against those written by surgeons. Using structured inputs from surgical procedures and outcomes, AI systems generated summaries that scored higher in peer reviews on completeness, accuracy, and clarity.

In one extensive hospital system, this automation reduced average documentation time by 40%, freeing up surgeons for more patient-centered tasks and reducing burnout. The findings underline AI’s potential in handling administrative tasks in clinical practice.⁴

Accelerated drug discovery

The drug development process benefits from AI by identifying potential compounds and predicting their efficacy, thereby shortening development timelines. Machine learning models have been used to expedite the search for treatments for neurological diseases, significantly reducing research costs and time.

Check out AI pharma to learn more about developments in drug discovery with AI.

Real-life example:

In 2024, researchers used AI tools to accelerate the drug discovery process for Parkinson’s disease. A machine learning system trained on known drug-target interactions scanned millions of compounds in silico, identifying several with high binding affinity for dopamine receptors.

One compound progressed to pre-clinical trials within six months, a process that traditionally takes 2–3 years. This demonstrates the potential of AI models to reduce development timelines and costs in the healthcare sector.⁵

Improved access to care

AI-powered virtual health assistants, chatbots, and remote monitoring tools can extend healthcare services to underserved or remote populations. These technologies facilitate continuous patient engagement and support, ultimately contributing to improved health outcomes and enhanced population health management.

Virtual health assistants powered by AI are increasingly used for chronic disease management and mental health support with therapist chatbots. These systems use natural language processing and real-time patient data to offer reminders, monitor symptoms, and suggest interventions.

Real-life example:

In one study involving rural patients with depression, AI-powered chatbots provided daily interaction and coping strategies. AI-powered chatbots are promising tools to deliver mental health support in rural areas. They effectively reduce symptoms of depression and anxiety, lower perceived stigma, and offer accessible, cost-effective solutions.⁶

Challenges of AI in healthcare

Data privacy and security concerns

The integration of AI in healthcare raises significant concerns regarding the protection of sensitive patient data. Ensuring compliance with data protection regulations and maintaining patient confidentiality are critical challenges that healthcare organizations must address.

Real-life example:

A 2025 study published on arXiv analyzed vulnerabilities in AI systems that process electronic health data. Researchers found that generative AI models trained on clinical data could unintentionally reproduce identifiable fragments of health records.

In one test, a model revealed parts of patient notes containing real names and diagnoses. This raises serious concerns regarding compliance with privacy laws, such as HIPAA and GDPR. The researchers emphasized the need for differential privacy techniques and secure training pipelines to protect sensitive medical data.⁷

Algorithmic bias and fairness

AI models trained on non-representative datasets can perpetuate existing disparities in healthcare delivery. For example, AI systems may underperform in diagnosing conditions in minority populations if the training data lacks diversity, leading to unequal patient outcomes.

Check out AI ethics to learn more about ethical dilemmas in AI systems and how to handle them.

Real-life example:

One study evaluated the use of AI tools in disease diagnosis across multiple hospital systems in the United States. The researchers discovered that an AI model trained predominantly on data from white patients underperformed when applied to Black and Hispanic populations.

For instance, diagnostic accuracy for diabetic retinopathy was 91% for white patients but dropped to 76% for Black patients. The disparity was traced to underrepresentation in training datasets.

This type of bias can lead to misdiagnosis and unequal access to quality care, making fairness a central issue in the development and deployment of AI applications.⁸

Lack of transparency

Many AI systems operate as “black boxes,” making it difficult for healthcare professionals to understand how decisions are made. This opacity can hinder clinical decision-making and reduce trust in AI applications among medical professionals.

Real-life example:

In a peer-reviewed article, clinicians expressed concern over the use of “black box” AI models in clinical decision making.

A highlighted case involved a deep learning model that recommended specific chemotherapy protocols without providing understandable explanations. When oncologists attempted to verify the rationale, the system offered only abstract probability scores.

This lack of interpretability led to hesitation among medical professionals, who feared clinical liability in the absence of human oversight. The study recommended the use of explainable AI techniques to enhance clinician trust and patient safety.⁹

Integration into clinical workflows

Incorporating AI tools into existing clinical workflows poses challenges, including the need for training healthcare workers and adapting to new technologies. Resistance to change and the complexity of healthcare systems can impede the effective adoption of AI solutions.

Real-life example:

A global survey of healthcare leaders found that fewer than 30% of healthcare organizations had successfully integrated AI tools into everyday clinical workflows.¹⁰

Despite the AI system being technically sound, operational disruptions and limited buy-in from clinicians made full adoption difficult. This illustrates that integrating AI requires more than technical readiness; instead, it demands organizational change and workforce engagement.

Regulatory and ethical considerations

The rapid advancement of AI in healthcare outpaces the development of regulatory frameworks, leading to uncertainties regarding accountability and ethical use.

Establishing clear guidelines and standards is essential to ensure the responsible deployment of AI technologies in clinical practice.

Real-life example:

A study analyzed the regulatory landscape for artificial intelligence in healthcare, highlighting gaps in global oversight. A new AI system used for predicting adverse drug events entered clinical trials without standardized reporting requirements. Regulatory agencies lacked a clear framework for evaluating their safety, resulting in inconsistent approvals across regions.

Additionally, the study highlighted that ethical reviews often lag behind technical developments, leaving healthcare systems vulnerable to unintended consequences. The researchers called for harmonized policies and ethical frameworks tailored to AI in clinical practice.¹¹

Best practices to implement AI in healthcare systems

Data quality and management

• Ensure high-quality, diverse data: AI models must be trained on accurate, comprehensive, and demographically diverse health data to avoid bias and ensure equitable healthcare delivery.
• Use structured and standardized data formats: Employ interoperable standards (e.g., HL7 FHIR) for electronic health records to improve data exchange and model training.
• Maintain data privacy and compliance: Implement encryption, access controls, and audit logs to comply with data protection regulations (e.g., HIPAA, GDPR).

Model development and validation

• Use clinically relevant outcomes: Train and evaluate AI models on endpoints meaningful to clinical practice, such as clinical outcomes, patient safety, or diagnostic accuracy.
• Conduct external validation: Test AI tools on independent datasets from different healthcare systems to ensure generalizability.
• Monitor performance post-deployment: Continuously assess model drift, false positives, and predictive accuracy in live clinical environments.

Clinical integration

• Embed AI into existing clinical workflows: AI solutions should be designed to complement, rather than replace, healthcare professionals, integrating into current clinical decision-making and documentation processes.
• Provide interpretability and transparency: Utilize explainable AI methods to enable healthcare providers to understand how predictions are made and justify their decisions to patients.
• Include human oversight: Ensure final clinical decisions remain under the control of qualified medical professionals.

Governance and ethics

• Establish clear accountability: Define the legal and clinical responsibilities among developers, healthcare organizations, and providers in the event of system failure or harm.
• Engage in ethical review: Conduct ongoing assessments of ethical risks, particularly in sensitive areas such as mental health, predictive analytics, and medical diagnosis.
• Develop inclusive AI: Involve stakeholders from diverse backgrounds, such as patients, clinicians, and ethicists, when designing AI applications.

Workforce preparation and training

• Educate healthcare workers: Provide structured training on how AI tools function, their limitations, and their role in clinical workflows.
• Encourage interdisciplinary collaboration: Promote partnerships between medical professionals, computer science experts, and data scientists to ensure clinically grounded development.

Evaluation and benchmarking

• Utilize clinical trials: Evaluate AI systems using randomized controlled trials or prospective cohort studies when appropriate, particularly for AI models employed in disease diagnosis or treatment planning.
• Report performance transparently: Follow reporting guidelines, such as CONSORT-AI and SPIRIT-AI, to effectively communicate model performance, limitations, and associated risk factors.
• Track impact on patient outcomes: Assess whether AI use contributes to better patient outcomes, reduced healthcare costs, or improved public health.

Technology and security

• Maintain auditability: Enable system logging and retrospective analysis to trace decision pathways and support compliance.
• Harden AI systems against cyber threats: Implement cybersecurity best practices to protect medical data and prevent manipulation of AI tools.
• Design for interoperability: Ensure that AI applications can be integrated with various electronic health record systems and medical devices.

External Links

• 1. Hundreds of AI tools have been built to catch covid. None of them helped. | MIT Technology Review. MIT Technology Review
• 2. Tech goal is for devices that help general practitioners spot skin cancer - The Washington Post. The Washington Post
• 3. New AI test can predict which men will benefit from prostate cancer drug | Prostate cancer | The Guardian. The Guardian
• 4. reuters.com.
• 5. AI speeds up drug design for Parkinson’s ten-fold | University of Cambridge.
• 6. View of The Influence of AI-Powered Chatbots on Reducing Mental Health Care Barriers in Rural Areas.
• 7. [2501.01639] Implications of Artificial Intelligence on Health Data Privacy and Confidentiality.
• 8. [2407.19655] AI-Driven Healthcare: A Review on Ensuring Fairness and Mitigating Bias.
• 9. ScienceDirect.
• 10. https://reports.weforum.org/docs/WEF_The_Future_of_AI_Enabled_Health_2025.pdf
• 11. [2412.03576] Ethical Challenges and Evolving Strategies in the Integration of Artificial Intelligence into Clinical Practice.