AI in healthcare: what are the risks for the NHS? (2024)

AI in healthcare: what are the risks for the NHS? (1)AI in healthcare: what are the risks for the NHS? (2)BBC

At a time when there are more than seven million patients on the NHS waiting list in England and around 100,000 staff vacancies, artificial intelligence could revolutionise the health service by improving patient care and freeing up staff time.

Its uses are varied - from spotting risk factors in a bid to help prevent chronic conditions such as heart attacks, strokes and diabetes - to assisting clinicians by analysing scans and x-rays to speed up diagnosis.

The technology is also maximising productivity by carrying out routine administrative tasks from automated voice assistants to scheduling appointments and capturing doctors' consultation notes.

'Transformative'

Generative AI - a type of artificial intelligence that can produce various types of content, including text and images - will be transformative for patient outcomes, according to Sir John Bell, a senior government advisor on life sciences.

Sir John is president of the Ellison Institute of Technology in Oxford - a major new research and development facility investigating global issues, including the use of AI in healthcare.

He says generative AI will improve the accuracy of diagnostic scans and generate forecasts of patient outcomes under different medical interventions, leading to more informed, personalised treatment decisions.

But he warns researchers should not work in isolation, instead innovation should be shared fairly around the country to avoid some communities missing out.

"To achieve these benefits the NHS must unlock the enormous value currently trapped within data silos, to do good while safeguarding against harm," Sir John says.

"Allowing AI access to all the data, within safe and secure research environments, will improve the representativeness, accuracy and equality of AI tools to benefit all walks of society, reducing the financial and economic burden of running a world-leading National Health Service and leading to a healthier nation."

AI in healthcare: what are the risks for the NHS? (3)AI in healthcare: what are the risks for the NHS? (4)Ellison Institute of Technology

'Mitigate risks'

AIopensupaworldofpossibilities,butitbringsrisksand challenges too, likemaintainingaccuracy.Results still need to be verified by trained staff.

The government is currently evaluating generative AI for use in the NHS - one issue is that it can sometimes "hallucinate" and generate content that is not substantiated.

DrCarolineGreen,fromtheInstituteforEthicsinAIattheUniversityofOxford, is aware of some health and care staff using models like ChatGPT to search for advice.

"It is important that people using these tools are properly trained in doing so, meaning they understand and know how to mitigate risks from technological limitations... such as the possibility for wrong information being given," she says.

She feels it is important to engage people working in health and social care, patients and other organisations early in the development of generative AI and to keep on assessing any impacts with them to build trust.

Dr Green says some patients have decided to deregister from their GPs over the fear of how AI may be used in their healthcare and how their private information may be shared.

"This of course means that these individuals may not receive the healthcare they may need in the future and fall through the cracks," she says.

AI in healthcare: what are the risks for the NHS? (5)AI in healthcare: what are the risks for the NHS? (6)

Then there is the risk of bias. AI models may be trained on datasets that might not reflect the populations they will be applied to, exacerbating health inequalities based on things like gender or ethnicity.

Therefore,regulationiskey.It needs to keep patients safe and protect their personal data, whilst at the same time increasing capacity to keep up with developments and allow AI to evolve and learn on the job.

AI-powered medical devices are tightly regulated by the Medicines and Healthcare products Regulatory Agency (MHRA).

The Health Foundation think tank recently published a six-point national strategy to ensure AI tools are rolled out fairly and regulation is updated.

NellThornton,improvement fellow at the Health Foundation, says: "There are so many of these models coming through the system that it's difficult to assess them quickly enough.

"That's where we need support around the capacity of the system to regulate these things and we also need some clarity on some of the challenges that will come from the quirkiness of generative AI systems and what additional regulation they might need."

Dr Paul Campbell, MHRA Head of Software and AI, says: "As a regulator, we must balance appropriate oversight to protect patient safety with the agility needed to respond to the particular challenges presented by these products to ensure we continue to be an enabler for innovation.”

The Department of Health and Social Care says the new Labour government will "harness the power of AI" by purchasing new AI-enabled scanners to diagnose patients earlier and treat them faster.

While few can deny the transformative effect AI is having within healthcare, there are challenges to overcome, not least that NHS staff need the confidence to use it and patients must be able to trust it.

Follow BBC South on Facebook, X (Twitter), or Instagram. Send your story ideas to south.newsonline@bbc.co.uk or via WhatsApp on 0808 100 2240.

Health

Artificial intelligence

Oxford

University of Oxford

AI in healthcare: what are the risks for the NHS? (2024)

FAQs

AI in healthcare: what are the risks for the NHS? ›

This study identified and clarifies seven main risks of AI in medicine and healthcare: 1) patient harm due to AI errors, 2) the misuse of medical AI tools, 3) bias in AI and the perpetuation of existing inequities, 4) lack of transparency, 5) privacy and security issues, 6) gaps in accountability, and 7) obstacles in ...

What are the risks of AI in healthcare? ›

Ethical concerns arising from AI-generated decisions that may conflict with patient or family preferences. Data quality issues related to incomplete or inaccurate data. Potential cybersecurity risks such as ransomware, malware, data breaches, and privacy violations.

What is the dark side of AI in healthcare? ›

AI algorithms are only as good as the data they're trained on, and if that data reflects existing societal biases, AI can inherit and amplify those biases. This can lead to unfair and inaccurate diagnoses, treatment recommendations, and resource allocation.

What are the challenges of AI in healthcare? ›

Lack of Quality Medical Data

Another obstacle is that the medical data from one organization may not be compatible with other platforms due to interoperability problems. To increase the amount of data available for testing AI systems, the healthcare sector must concentrate on techniques for standardizing medical data.

Why AI is failing in healthcare? ›

The absence of a clear regulatory structure creates two core challenges for the health care sector. First, there is no well-articulated testing process for these new technologies. While drugs go through FDA approval, for instance, AI tools are simply tested by the companies and developers that create them.

What are 5 disadvantages of AI? ›

Frequently cited drawbacks of AI include the following:
  • A lack of creativity. ...
  • The absence of empathy. ...
  • Skill loss in humans. ...
  • Possible overreliance on the technology and increased laziness in humans. ...
  • Job loss and displacement.
Jun 16, 2023

How AI is disrupting healthcare? ›

Automation Of Labor-Intensive Tasks

The more practitioners can push labor-intensive or administrative tasks to AI solutions, the more time they can spend with patients. AI can complete those labor-intensive tasks with less (if any) human assistance and even uncover inefficiencies in current practices.

Can we trust AI in healthcare? ›

Artificial intelligence (AI) affects 100% of physicians and other health care providers, but three out of four patients do not trust AI in a health care setting. AI has become ubiquitous in health care, but a new survey found nearly 80% of patients don't know if their doctor is using it or not.

How AI can be used unethically in healthcare? ›

Data Bias and Fairness

Data used to train AI algorithms may result in biased healthcare decisions. This can lead to ethical dilemmas where AI systems possibly perpetuate or exacerbate disparities in healthcare outcomes among different demographic groups.

What is an example of AI affecting healthcare? ›

AI is used daily in many areas of modern healthcare, from the online scheduling service for appointments to drug interaction warnings when physicians prescribe multiple medications to research development. The most widely known and accepted evidence-based medicine used today are flowcharts and database research.

What are the inaccuracies of AI in healthcare? ›

Mistakes can include biased outcomes, “hallucinations” and AI drift, which may seriously harm patients and therefore demand measures and increased awareness to counter these unwanted effects.

What is the biggest problem in AI? ›

Top Challenges in Artificial Intelligence You Need to Know
  • Understanding the complexity of AI algorithms. ...
  • Mitigating bias and discrimination. ...
  • Safeguarding privacy and data security. ...
  • Ensuring ethical decision-making. ...
  • Addressing security risks. ...
  • Overcoming technical difficulties. ...
  • Promoting transparency and explainability.
May 2, 2024

What is the biggest risk of AI? ›

Dangers of Artificial Intelligence
  • Automation-spurred job loss.
  • Deepfakes.
  • Privacy violations.
  • Algorithmic bias caused by bad data.
  • Socioeconomic inequality.
  • Market volatility.
  • Weapons automatization.
  • Uncontrollable self-aware AI.

How AI will disrupt healthcare? ›

Automation Of Labor-Intensive Tasks

The more practitioners can push labor-intensive or administrative tasks to AI solutions, the more time they can spend with patients. AI can complete those labor-intensive tasks with less (if any) human assistance and even uncover inefficiencies in current practices.

Is artificial intelligence harmful to human health? ›

If companies rely too much on AI predictions for when maintenance will be done without other checks, it could lead to machinery malfunctions that injure workers. Models used in healthcare could cause misdiagnoses. And there are further, non-physical ways AI can harm humans if not carefully regulated.

What are the consequences of AI bias in healthcare? ›

“When a dataset used to train an AI system lacks diversity, that can result in misdiagnoses, disparities in healthcare, and unequal insurance decisions on premiums or coverage," explains Tom Hittinger, healthcare applied AI leader at Deloitte Consulting.

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