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A new survey has found that one in five general practitioners (GPs) in the UK are using AI tools like ChatGPT to assist with daily tasks such as suggesting diagnoses and writing patient letters. 

The research, published in the journal BMJ Health and Care Informatics, surveyed 1,006 GPs across the about their use of AI chatbots in clinical practice. 

Some 20% reported using generative AI tools, with ChatGPT being the most popular. Of those using AI, 29% said they employed it to generate documentation after patient appointments, while 28% used it to suggest potential diagnoses.

“These findings signal that GPs may derive value from these tools, particularly with administrative tasks and to support clinical reasoning,” the study authors noted. 

We have no idea how many papers OpenAI used to train their models, but it’s certainly more than any doctor could have read. It gives quick, convincing answers and is very easy to use, unlike searching research papers manually. 

Does that mean ChatGPT is generally accurate for clinical advice? Absolutely not. Large language models (LLMs) like ChatGPT are pre-trained on massive amounts of general data, making them more flexible but dubiously accurate for specific medical tasks.

It’s easy to lead them on, with the AI model tending to side with your assumptions in problematically sycophantic behavior.

Moreover, some researchers state that ChatGPT can be conservative or prude when handling delicate topics like sexual health.

As Stephen Hughes from Anglia Ruskin University wrote in The Conservation, “I asked ChatGPT to diagnose pain when passing urine and a discharge from the male genitalia after unprotected sexual intercourse. I was intrigued to see that I received no response. It was as if ChatGPT blushed in some coy computerised way. Removing mentions of sexual intercourse resulted in ChatGPT giving a differential diagnosis that included gonorrhoea, which was the condition I had in mind.” 

As Dr. Charlotte Blease, lead author of the study, commented: “Despite a lack of guidance about these tools and unclear work policies, GPs report using them to assist with their job. The medical community will need to find ways to both educate physicians and trainees about the potential benefits of these tools in summarizing information but also the risks in terms of hallucinations, algorithmic biases and the potential to compromise patient privacy.”

That last point is key. Passing patient information into AI systems likely constitutes a breach of privacy and patient trust.

Dr. Ellie Mein, medico-legal adviser at the Medical Defence Union, agreed on the key issues: “Along with the uses identified in the BMJ paper, we’ve found that some doctors are turning to AI programs to help draft complaint responses for them. We have cautioned MDU members about the issues this raises, including inaccuracy and patient confidentiality. There are also data protection considerations.”

She added: “When dealing with patient complaints, AI drafted responses may sound plausible but can contain inaccuracies and reference incorrect guidelines which can be hard to spot when woven into very eloquent passages of text. It’s vital that doctors use AI in an ethical way and comply with relevant guidance and regulations.”

Probably the most critical questions amid all this are: How accurate is ChatGPT in a medical context? And how great might the risks of misdiagnosis or other issues be if this continues?

Generative AI in medical practice

As GPs increasingly experiment with AI tools, researchers are working to evaluate how they compare to traditional diagnostic methods. 

A study published in Expert Systems with Applications conducted a comparative analysis between ChatGPT, conventional machine learning models, and other AI systems for medical diagnoses.

The researchers found that while ChatGPT showed promise, it was often outperformed by traditional machine learning models specifically trained on medical datasets. For example, multi-layer perceptron neural networks achieved the highest accuracy in diagnosing diseases based on symptoms, with rates of 81% and 94% on two different datasets.

Researchers concluded that while ChatGPT and similar AI tools show potential, “their answers can be often ambiguous and out of context, so providing incorrect diagnoses, even if it is asked to provide an answer only considering a specific set of classes.”

This aligns with other recent studies examining AI’s potential in medical practice.

For example, research published in JAMA Network Open tested GPT-4’s ability to analyze complex patient cases. While it showed promising results in some areas, GPT-4 still made errors, some of which could be dangerous in real clinical scenarios.

There are some exceptions, though. One study conducted by the New York Eye and Ear Infirmary of Mount Sinai (NYEE) demonstrated how GPT-4 can meet or exceed human ophthalmologists in diagnosing and treating eye diseases.

For glaucoma, GPT-4 provided highly accurate and detailed responses that exceeded those of real eye specialists. 

AI developers such as OpenAI and NVIDIA are training purpose-built medical AI assistants to support clinicians, hopefully making up for shortfalls in base frontier models like GP-4.

OpenAI has already partnered with health tech company Color Health to create an AI “copilot” for cancer care, demonstrating how these tools are set to become more specific to clinical practice.  

Weighing up benefits and risks

There are countless studies comparing specially trained AI models to humans in identifying diseases from diagnostics images such as MRI and X-ray. 

AI techniques have outperformed doctors in everything from cancer and eye disease diagnosis to Alzheimer’s and Parkinson’s early detection. One, named “Mia,” proved effective in analyzing over 10,000 mammogram scans, flagging known cancer cases, and uncovering cancer in 11 women that doctors had missed. 

However, these purpose-built AI tools are certainly not the same as parsing notes and findings into a language model like ChatGPT and asking it to infer a diagnosis from that alone. 

Nevertheless, that’s a difficult temptation to resist. It’s no secret that healthcare services are overwhelmed. NHS waiting times continue to soar at all-time highs, and even obtaining GP appointments in some areas is a grim task. 

AI tools target time-consuming admin, such is their allure for overwhelmed doctors. We’ve seen this mirrored across numerous public sector fields, such as education, where teachers are widely using AI to create materials, mark work, and more. 

So, will your doctor parse your notes into ChatGPT and write you a prescription based on the results for your next doctor’s visit? Quite possibly. It’s just another frontier where the technology’s promise to save time is just so hard to deny. 

The best path forward may be to develop a code of use. The British Medical Association has called for clear policies on integrating AI into clinical practice.

“The medical community will need to find ways to both educate physicians and trainees and guide patients about the safe adoption of these tools,” the BMJ study authors concluded.

Aside from advice and education, ongoing research, clear guidelines, and a commitment to patient safety will be essential to realizing AI’s benefits while offsetting risks.

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Around 50% of Americans believe in conspiracy theories of one type or another, but MIT and Cornell University researchers think AI can fix that.

In their paper, the psychology researchers explained how they used a chatbot powered by GPT-4 Turbo to interact with participants to see if they could be persuaded to abandon their belief in a conspiracy theory.

The experiment involved 1,000 participants who were asked to describe a conspiracy theory they believed in and the evidence they felt underpinned their belief.

The paper noted that “Prominent psychological theories propose that many people want to adopt conspiracy theories (to satisfy underlying psychic “needs” or motivations), and thus, believers cannot be convinced to abandon these unfounded and implausible beliefs using facts and counterevidence.”

Could an AI chatbot be more persuasive where others failed? The researchers offered two reasons why they suspected LLMs could do a better job than you of convincing your colleague that the moon landing really happened.

LLMs have been trained on vast amounts of data and they’re really good at tailoring counterarguments to the specifics of a person’s beliefs.

After describing the conspiracy theory and evidence, the participants engaged in back-and-forth interactions with the chatbot. The chatbot was prompted to “very effectively persuade” the participants to change their belief in their chosen conspiracy.

The result was that on average the participants experienced a 21.43% decrease in their belief in the conspiracy, which they formerly considered to be true. The persistence of the effect was also interesting. Up to two months later, participants retained their new beliefs about the conspiracy they previously believed.

The researchers concluded that “many conspiracists—including those strongly committed to their beliefs—updated their views when confronted with an AI that argued compellingly against their positions.”

Our new paper, out on (the cover of!) Science is now live! https://t.co/VBfC5eoMQ2

— Tom Costello (@tomstello_) September 12, 2024

They suggest that AI could be used to counter conspiracy theories and fake news spread on social media by countering these with facts and well-reasoned arguments.

While the study focused on conspiracy theories, it noted that “Absent appropriate guardrails, however, it is entirely possible that such models could also convince people to adopt epistemically suspect beliefs—or be used as tools of large-scale persuasion more generally.”

In other words, AI is really good at convincing you to believe the things it is prompted to make you believe. An AI model also doesn’t inherently know what is ‘true’ and what isn’t. It depends on the content in its training data.

The researchers achieved their results using GPT-4 Turbo, but GPT-4o and the new o1 models are even more persuasive and deceptive.

The study was funded by the John Templeton Foundation. The irony of this is that the Templeton Freedom Awards are administered by the Atlas Economic Research Foundation. This group opposes taking action on climate change and defends the tobacco industry, which also gives it funding.

AI models are becoming very persuasive and the people who decide what constitutes truth hold the power.

The same AI models that could convince you to stop believing the earth is flat, could be used by lobbyists to convince you that anti-smoking laws are bad and climate change isn’t happening.

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Scientists have developed a new machine learning system that could help preserve vaccines, blood, and other medical treatments. 

The research, published in Nature Communications, was led by the University of Warwick and the University of Manchester.

The AI system helps identify molecules called cryoprotectants – compounds that prevent damage when freezing biological materials. 

Cryoprotectants are special substances that help protect living cells and tissues from damage when they’re frozen. They work by preventing the formation of harmful ice crystals, which essentially break tissue apart when you freeze it. They also help cells maintain their structure in extreme cold.

These compounds are fundamentally important for preserving things like vaccines, blood samples, and reproductive cells for long-term storage or transport.

Cryopresevants could one day be used to preserve organs, complex tissues, or even entire humans.

Currently, finding new cryoprotectants is a slow, trial-and-error process. This new ML-driven approach allows researchers to rapidly screen hundreds of potential molecules virtually.

Here are some key points of the study:

The team created a machine learning model trained on data from existing cryoprotectants.
This model can predict how well new molecules might work as cryoprotectants.
Researchers used the model to screen a library of about 500 amino acids.
The system identified several promising compounds, including an aminooxazole ester that outperformed many known cryoprotectants.
Lab tests confirmed the AI’s predictions, with the new compound showing strong ice crystal prevention.
The discovered molecule improved red blood cell preservation when combined with standard techniques.

The amino oxazole ester identified by the study demonstrated particularly remarkable ice recrystallization inhibition (IRI) qualities. It almost completely stopped ice crystals from growing larger during the freezing process.

The compound was effective even when researchers lowered its concentration. Plus, it also maintained its ice-inhibiting properties in phosphate-buffered saline (PBS), a solution that mimics the salt concentration in human bodies.

Dr. Matt Warren, the PhD student who spearheaded the project, described how the model accelerates efficiency: “After years of labour-intensive data collection in the lab, it’s incredibly exciting to now have a machine learning model that enables a data-driven approach to predicting cryoprotective activity.”

Professor Matthew Gibson from Manchester addeds, “The results of the computer model were astonishing, identifying active molecules I never would have chosen, even with my years of expertise.”

Professor Gabriele Sosso, who led the Warwick team, explained in a blog post that, while impressive, machine learning isn’t a cure-all for these types of research problems: “It’s important to understand that machine learning isn’t a magic solution for every scientific problem. In this work, we used it as one tool among many.”

The researchers combined the AI predictions with molecular simulations and lab experiments – a multi-pronged approach that helped validate results and refine the model.

This contributes to a range of AI-driven studies into drug discovery and material design. Researchers have built AI models to generate interesting medicinal compounds, one of which has been brought to clinical trial.

DeepMind also created a model named GNoME capable of automatically generating and synthesizing materials.

The new cryoprotectant compounds discovered could have broad real-world impacts.

For instance, the researchers describe how improving cryopreservation might extend the shelf life of vaccines and make it easier to transport sensitive medical treatments to remote areas. 

The technique could also speed up blood transfusions by reducing the time needed to process frozen blood.

While the results are promising, the team cautions that more work is needed to fully understand how these new compounds function and to ensure medical safety and stability. 

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Microsoft introduced “Wave 2” of its Copilot AI assistant, bringing a host of new features designed to transform how individuals and businesses interact with AI in their daily workflows.

At the core of Wave 2 is Copilot Pages, a novel digital workspace that Microsoft describes as “the first new digital artifact for the AI age.” 

In essence, the new Copilot announcements offer a fully AI-embedded workspace for media and content production, blending OpenAI’s GPT-4o model into a coherent digital workspace.

It allows teams to collaborate in real-time with AI assistance, turning AI-generated content into editable, shareable documents. 

The goal? To save business time and enhance productivity while driving paid adoption of AI tools. Wave 2 indicates Microsoft’s attempts to turn its multi-billion dollar OpenAI investment into revenue.

By promoting Copilot’s business appeal, the company aims to convert more companies into paying customers for its premium AI tools.

Jared Spataro, Microsoft’s Corporate VP for AI at Work, explains the new Copilot concept: “Pages takes ephemeral AI-generated content and makes it durable, so you can edit it, add to it, and share it with others. You and your team can work collaboratively in a page with Copilot, seeing everyone’s work in real time and iterating with Copilot like a partner.”

The rollout of these features varies, with some immediately available and others predicted to roll out in the coming weeks or months. 

Here’s a round-up of everything Microsoft announced at their live event:

Copilot Pages

Copilot Pages is the centerpiece of Microsoft’s Wave 2 update, offering a thoroughly AI-embedded approach to collaborative work. It works by transforming Copilot’s AI-generated responses from fleeting chat messages into lasting, editable documents.

To use it, you select an “Edit in Pages” button next to a Copilot response, opening a new window alongside the original chat thread. Users can work within that window to refine and build upon the AI’s output together.

Pages integrates with BizChat, Microsoft’s hub for combining web, corporate, and business data.

Like most of these features, Pages is currently exclusive to paid Copilot for Microsoft 365 subscribers.

PowerPoint powered by AI

The new PowerPoint Narrative Builder aims to streamline the presentation creation process. Users can input a topic, and the AI will generate an outline in minutes. 

Microsoft is also introducing a Brand Manager feature to ensure presentations align with company guidelines and maintain consistency across different channels.

That’s another business-centric feature designed to drive greater uptake among enterprise users.

In Microsoft’s words, “Accelerating every business process with Copilot—to grow revenue and reduce costs—is the best way to gain competitive advantage in the age of AI.”

Supporting businesses is a central theme here. Microsoft is pushing businesses to opt into its growing AI ecosystem, converting them into paying customers – customers who can pay much more than the average ChatGPT Plus subscriber at $20 a month. 

Smarter meetings with Teams

Copilot in Teams now offers more comprehensive meeting summaries by analyzing both spoken conversations and chat messages. 

As Microsoft puts it, “Now with Copilot in Teams, no question, idea, or contribution is left behind.”

Ending email overload in Outlook

Addressing the perennial challenge of email management, Microsoft introduces “Prioritize my inbox” for Outlook. 

This feature uses AI to identify important emails, provide concise summaries, and explain why certain messages were flagged as priorities. In the future, users will be able to teach Copilot their personal priorities to refine this feature. 

Copilot Agents

Perhaps the most intriguing addition is Copilot agents – customizable AI assistants that can perform specific tasks with varying degrees of autonomy. 

Microsoft is also launching an agent builder, which will make it easier for users to create and deploy AI helpers for different tasks, somewhat similar to Custom GPTs within ChatGPT.

Again, Agents are aimed at business users. In Microsoft’s words, “We’re introducing Copilot agents, making it easier and faster than ever to automate and execute business processes on your behalf—enabling you to scale your team like never before.

Excel evolves with Python integration

Copilot in Excel now includes Python integration, designed to accelerate data analysis. 

Users can perform advanced tasks like forecasting and risk analysis using natural language prompts without needing to write code. 

Microsoft’s AI productivity revolution kicks up a notch

Copilot Wave 2 represents a big jump forward for AI integration into the Microsoft ecosystem. 

However, amidst the deluge of announcements, some critical areas seem to have flown under the radar.

For one, security and privacy concerns are rife since many of these tools will interact with sensitive personal and business data. While Microsoft asserts that Pages has Enterprise Data Protection, details on how this works in practice are scarce.

Other features, such as the ability to analyze files without opening them, may be convenient, but they also mean Copilot has relatively permanent access to sensitive information. This level of AI involvement in the enterprise data ecosystem will breed some level of discomfort.

The same goes for AI integration into Outlook. Not everyone’s ready for an AI system to sift through personal and professional exchanges.

Microsoft must demonstrate that Copilot’s benefits outweigh the privacy concerns while being transparent about how user data is handled.

As Wave 2 rolls out, it’s clear that Microsoft is betting big on AI integration. Google is expected to respond with its own new wave of AI-driven features for Workspace.

Time will only tell how useful these tools are and the extent of business adoption, or whether we need a little longer for people to accept AI’s massive front-, center-, and behind-the-scenes role in our personal and business lives.

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