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Is your approach to AI in healthcare responsible?

Is your approach to AI in healthcare responsible?

Is your approach to AI in healthcare responsible?

Is your approach to AI in healthcare responsible?

Is your approach to AI in healthcare responsible?

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AI in healthcare

GenAI

LLM

Responsible AI

Safe AI governance

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Nearly 80% of healthcare organizations are using AI, and it’s easy to see why. AI is improving every aspect of healthcare care, from making the emergency room intake process more efficient to improving clinical care. But integrating AI isn’t without its fair share of risks.

To fully realize the potential of AI in healthcare, healthcare providers must prioritize responsible AI practices.

AI makes healthcare better by improving care and simplifying how work gets done

AI is already making a difference in diagnosis, treatment, and patient management by collecting and processing enormous amounts of patient data.

AI can enable physicians to diagnose medical conditions more effectively and quickly. According to research from Nanyang Technological University in Singapore, AI-powered tools can diagnose cardiovascular diseases with over 98.5% accuracy, using electrocardiograms to detect conditions like coronary artery disease and congestive heart failure​.

AI overlooks details that physicians might miss

These tools help physicians by reducing the time spent on diagnosis, allowing them to focus on direct patient care.

Health tech initiatives like Foundation 29’s Dx29 use AI to interpret genetic tests, speeding up the identification of rare diseases​.

AI can sift through vast amounts of patient data, identifying connections that may be overlooked by human practitioners. Such applications are especially beneficial in fields where early detection can dramatically improve patient outcomes.

AI in healthcare improves workflows

AI also offers advantages in managing routine tasks and improving patient care delivery. For example, emergency room physicians at four HCA Healthcare hospitals are using GenAI to generate medical notes from conversations during patient visits.

HCA Healthcare is also investigating how GenAI can improve the patient handoff process between nurses. GenAI can automate and standardize handoffs, which is typically a time-consuming and manual task. This will improve continuity of care.

Be aware of the risks that AI poses to healthcare

AI has the potential to transform healthcare, but it comes with serious risks. The massive data needs of GenAI models make patient information a prime target for cyberattacks. Plus, biased training data and privacy challenges can lead to flawed care and costly regulatory issues, creating major ethical and legal pitfalls.

AI poses cybersecurity risks

GenAI models require vast amounts of data, including sensitive patient information, for training. This makes them prime targets for cyberattacks, which can have severe consequences. For example, bad actors might hack into a hospital’s AI system to steal data or corrupt models, potentially leading to incorrect diagnoses or treatment recommendations​. In addition, cybersecurity breaches can be financially costly. The average cost of a cybersecurity breach in healthcare is nearly $10 million.

AI in healthcare can be biased

AI systems are only as good as the data on which they are trained. If the training data lacks diversity or contains biases, AI tools may produce inaccurate results, disproportionately affecting certain patient groups. A notable case is Google’s AI-powered dermatology assistant, which faced criticism for not accounting for people with darker skin tones​.

Such biases can result in misdiagnosis and inequitable care, raising ethical concerns about fairness and inclusivity in AI applications.

Healthcare organizations can unwittingly violate patient privacy laws

Another risk is the potential for healthcare organizations to unknowingly violate HIPAA privacy laws when using patient data to train large language models.

Training AI models often requires vast datasets, including sensitive patient information, to achieve accurate results. If data is not properly anonymized or if privacy protocols are not strictly followed, there is a risk of exposing identifiable patient information, leading to compliance breaches.

This can result in legal consequences, hefty fines, and a loss of trust from patients. As AI becomes more embedded in healthcare, providers must be diligent in maintaining compliance with privacy regulations to protect patient data.

Manage AI risks through responsible AI practices

To benefit from AI’s potential while mitigating its risks, your healthcare organization should practice responsible AI. This involves addressing the concerns mentioned above through specific strategies, such as ensuring data inclusivity, keeping humans in the loop, and implementing robust cybersecurity measures.

AI models must be trained with diverse data sets to be effective

Ensuring that AI models are trained with diverse datasets is essential for treating a wide range of populations effectively. For example, Foundation 29’s Dx29 uses a broad array of genetic data to improve the detection of rare diseases​.

By incorporating data from underrepresented groups, AI models can better identify conditions across diverse patient demographics. This inclusivity not only improves the accuracy of diagnoses but also helps ensure that AI serves all patients equitably, reducing disparities in care.

In practice, this means working with data from different ethnic groups, age ranges, and socioeconomic backgrounds. It also involves collaborating with underrepresented communities to ensure that their specific health needs are considered during AI development.

This approach helps to create AI tools that are capable of delivering more personalized and effective care, ultimately leading to better health outcomes for all.

Keep humans in the loop

AI has the potential to create job loss by replacing people. But AI should augment, not replace, human decision-making in healthcare. Keeping healthcare professionals in the loop is crucial to ensuring that AI recommendations are validated before being applied in clinical settings.

For instance, at HCA Healthcare, ER physicians use GenAI to draft medical notes during patient visits. But the notes are reviewed and approved by doctors before being added to patients’ records. This human oversight helps ensure that AI tools do not introduce errors or overlook important clinical details.

A human-in-the-loop (HITL) approach also helps build trust among patients, who can be assured that their care is guided by human judgment and empathy. Keeping humans involved ensures that any potential biases or anomalies in AI recommendations can be identified and corrected before they affect patient care.

Practice strong cybersecurity and compliance

Healthcare providers must implement robust cybersecurity protocols to prevent data breaches. For example, adopting a zero trust architecture (ZTA) can help ensure that only authorized personnel have access to sensitive information​.

Additionally, hospitals should consider tools like Google Magika, which uses AI to detect and prevent malware, providing an added layer of protection against cyber threats​.

You can safeguard against committing data security violations and running afoul of HIPAA regulations by practicing strong governance, risk, and compliance. This includes conducting regular security audits and ensuring that AI models are trained using anonymized data wherever possible.

Such practices not only safeguard patient information but also help maintain public trust in AI applications.

Continuously monitor and improve AI in healthcare

Your organization should continuously update and improve all AI models based on new medical research and feedback from healthcare professionals. This helps ensure that the models stay relevant and effective as new diseases and treatment methods emerge.

By adopting a cycle of improvement, you can ensure that your AI applications evolve to meet the changing needs of the medical field. For example, since 2021, the AI tool developed by Nanyang Technological University for diagnosing cardiovascular diseases has been tested in collaboration with hospitals to expand its data set and validate its clinical use​.

Centific can help you responsibly adopt AI in healthcare

By adopting a framework of responsible AI practices—training models with diverse data, keeping humans in the loop, maintaining strong cybersecurity measures, and continually improving AI tools—you can ensure that AI serves as a trusted ally in improving patient care.

A frontier AI data foundry can help you deploy AI in healthcare responsibly and effectively through techniques such as red teaming, reinforcement learning from human feedback, and adversarial testing.

Visit our website to learn how we can help you apply AI responsibly.

Deliver modular, secure, and scalable AI solutions

Centific offers a plugin-based architecture built to scale your AI with your business, supporting end-to-end reliability and security. Streamline and accelerate deployment—whether on the cloud or at the edge—with a leading frontier AI data foundry.

Deliver modular, secure, and scalable AI solutions

Centific offers a plugin-based architecture built to scale your AI with your business, supporting end-to-end reliability and security. Streamline and accelerate deployment—whether on the cloud or at the edge—with a leading frontier AI data foundry.

Deliver modular, secure, and scalable AI solutions

Centific offers a plugin-based architecture built to scale your AI with your business, supporting end-to-end reliability and security. Streamline and accelerate deployment—whether on the cloud or at the edge—with a leading frontier AI data foundry.