ChatGPT Tag

Does AI Help You Reflect, or Just Write Faster?

By Nigel Hinchliffe Director of Education, CCH.

During a curriculum review workshop several years ago, a group of newly qualified nurses were asked to share a reflective account they were proud of. Most had written competent, structured pieces that moved neatly through Gibbs’ stages: description, feelings, evaluation, analysis, conclusion, action. When asked whether those accounts had changed anything about the way they practised, the room went quiet. They had learned to write reflection. They had not necessarily learned to reflect.

“They had learned to write reflection.
They had not necessarily learned to reflect.”

That gap, between the performance of reflection and reflection as a genuine habit of professional learning, is not a new problem. It is, however, an urgent one. Generative AI tools are now capable of producing plausible reflective writing at speed. Before the profession reaches for AI as a solution to reflective practice, it is worth being aware of what the problem actually is.

Has reflection been taught as a skill?

Healthcare professionals are required to reflect, assessed on their reflections, and held accountable for them at revalidation. The Nursing and Midwifery Council, for instance, requires five written reflective accounts as part of the revalidation process (NMC, 2019). 

What is less common is being explicitly taught how to reflect: how to move from description into analysis, how to connect personal experience to evidence, how to sit with uncertainty rather than resolve it prematurely into a tidy learning point.

Gibbs’ Reflective Cycle (1988) is the dominant framework, precisely because its six stages – description, feelings, evaluation, analysis, conclusion, action planning – provide a scaffold practitioners can follow without having been taught the underlying skill. That is both its strength and its limitation. Applied well, Gibbs takes a practitioner from raw experience to transferable learning. Applied as a form to be completed, it produces the kind of account that satisfies an assessor without troubling the practitioner who wrote it.

This is the baseline problem AI enters. Not a profession of skilled reflectors who lack time, but a profession in which written reflection may well be delivered, but practitioners may not have learned how to reflect

What AI offers, and what it does not do automatically

Generative AI tools – Claude, ChatGPT, Copilot and others – are capable of functioning as structured thinking partners in a way that a blank page cannot. Used well, they can prompt practitioners through Gibbs’ stages with tailored questions, surface relevant clinical frameworks, and help translate half-formed thoughts into coherent prose. 

For a nurse processing a safeguarding concern, or a GP working through a difficult prescribing decision, an AI interlocutor that asks “What assumptions were you making at that point, and what would challenge them?” can move the reflection further than a prompt to “complete your analysis section”.

But this is not automatic. A general AI model’s default behaviour is to affirm and reflect back.

Asked to help with a reflective account, it will typically smooth, summarise and endorse. Getting it to meaningfully challenge a clinician’s reasoning requires deliberate prompt design. That design is itself a competency, and one that many practitioners have yet to acquire. The AI needs to be asked to:

• Notice a self-serving account

• Probe an unexamined assumption

• Push back on a premature conclusion

There is also a legitimate concern about cognitive offloading. The effort of articulating an experience is an important aspect of reflective learning: finding the words, sitting with ambiguity, returning to an account and revising it. Moon’s theoretical work on reflection and experiential learning argues that this effortful processing is part of how meaning is constructed from experience, rather than a delivery mechanism for insight that exists independently of it (Moon, 2004).

The real risk: effortless performance

A 2024 systematic review of reflective writing as summative assessment found that power dynamics between students and markers can lead to ‘performative instead of genuine reflection’ and concluded that student voices in assessed work may not represent authentic participation ‘because students may lack agency as they are on the wrong side of a power imbalance and are motivated to pass’ (Ross, Bohlmann and Marren, 2024). The conventions of a passable reflective account, such as a growth narrative, appropriate emotional awareness, or a clear action point,  are learnable independently of the reflection itself.

AI makes producing that performance effortless. A practitioner who has absorbed the conventions can generate a convincing Gibbs account of a clinical encounter they have not meaningfully examined, in minutes, with minimal cognitive effort.

AI will be used in reflective writing; it already is. The real question for educators and professional bodies is whether the assessment frameworks and supervisory practices we have built are robust enough to distinguish genuine reflection from sophisticated production. Many are not, which is a problem for education design to solve, not for the technology.

Where AI can make a real difference

None of this means AI has no role. It means the role needs to be designed rather than assumed.

For student practitioners, AI can function as a responsive scaffold – asking the questions a supervisor would ask, at the moment when the experience is still live rather than weeks later in a tutorial. 

The immediacy matters: Mann, Gordon and MacLeod’s review identified proximity to the experience as one of the variables that influences the quality of reflective learning (2009). A tool that meets practitioners in that proximate moment, rather than waiting for a scheduled portfolio deadline, addresses a real structural gap. 

A systematic review of how health-professions students use generative AI found these tools most often supported learning through inquiry and in practice, rather than simple content acquisition (Pham et al., 2025).

For experienced clinicians preparing for supervision or appraisal, AI offers a way to organise thinking before the formal conversation rather than during it, reducing the time spent on description and increasing what is available for deeper analysis. The preparation is the reflection; the supervision becomes its extension.

For practitioners processing emotionally charged events – errors, patient deterioration, moral distress – the non-judgemental quality of an AI interlocutor is not trivial. The psychological safety required for honest reflection is not always available in formal supervisory relationships, and a first pass engagement with an AI tool may lower the threshold for authentic disclosure in a subsequent human conversation.

Prompting as a professional skill

The common thread across these applications is that the value of AI in reflective practice depends almost entirely on how it is used. A tool set up to challenge will challenge; a tool set up to affirm will affirm. The distinction is determined by the practitioner’s prompting, and prompting is a skill.

“A tool set up to challenge will challenge.
A tool set up to affirm will affirm.”

That skill can be taught directly. Practitioners can learn to build challenge into their prompts in the same way they learn any other clinical or communication skill: through worked examples, supervised practice, and feedback. This might mean asking the AI to argue the counter-case, name the assumption they have not examined, or hold back the tidy conclusion until the uncertainty has been explored.

Embedding this in professional development does not require waiting for system-wide change. It can sit inside existing CPD structures:

• A short module on prompt design for reflective practice
• Worked examples built into portfolio guidance
• Supervisors modelling the technique in appraisal conversations.

The frameworks for reflective practice already exist; what is needed is training practitioners to use AI within them deliberately, rather than assuming the tool will do the work on its own.

The Gibbs cycle has endured for nearly four decades because its structure is sound. What it has always needed is a workforce able to use it as a habit of thinking rather than a form to complete. AI does not change that requirement. It raises the stakes on meeting it. Whether AI ends up supporting genuine reflection or accelerating its performance depends on whether that training happens now.

What this means in practice

When AI makes reflective writing easier, it does not automatically make reflective learning more likely. The outcome depends on whether practitioners learn to prompt it to challenge their thinking, rather than simply affirm it.

Colleges and providers who educate healthcare professionals can address this directly, rather than wait for the wider profession to resolve it. Teaching prompt design as part of CPD, portfolio guidance, and supervision is not a future ambition. It is available now.

How to Take This Further

If this is a skill you want to build, our short course, Getting More from ChatGPT: Prompting Skills for Healthcare Professionals, includes a prompt template for reflective practice specifically, as well as the course’s wider training in getting reliable, useful output from any AI tool you use.

Click here to learn more.

About the author

This article was written by Nigel Hinchliffe, Director of Education at the College of Contemporary Health (CCH). Nigel has extensive experience in clinical education, with a particular focus on how healthcare professionals develop and demonstrate competence throughout their careers. At CCH, he leads the development of evidence-based training programmes and regularly provides feedback on learner’s reflective submissions as part of their professional development.

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People Like AI Mental Health Chatbots. Whether They Help Is Another Question

Key Takeaways: 

• Across 21 studies in 11 countries, people using generative AI mental health chatbots reported high satisfaction and found them convenient and accessible.
  
• Personalisation and empathy did not reliably translate into better clinical outcomes, and engagement often faded over time.
  
• The evidence base remains early-stage, leaving safety, equity and crisis response unresolved.
  
  
  

A treatment gap that digital tools are being asked to fill

Generative artificial intelligence (GenAI) chatbots designed to support mental health are winning people over on experience, but the research needed to establish whether they are safe and clinically effective has not kept pace. That is the central finding of a review, currently in press in the journal npj Digital Medicine, which examined the user experience (UX) and intervention design of GenAI mental health chatbots.

The context for this work is a substantial and persistent shortfall in care. Around 25% of people worldwide experience a mental health problem, yet approximately 85% do not receive adequate treatment. The reasons are varied and overlapping: stigma, cost, shortages of trained professionals, geographic distance from services and structural inequities, among others. As the prevalence of mental health conditions has grown while treatment gaps have remained, attention has turned towards innovative models of delivery – and digital tools, with their scalability and convenience, have become a focus of that search.

Digital mental health interventions deliver treatment or support through a range of channels, including chatbots, websites, mobile applications and wearables. Conversational agents, more commonly known as chatbots, are applications that simulate human dialogue using machine learning and natural language processing algorithms.

From scripted responses to open-ended conversation

Traditional mental health chatbots deliver pre-scripted therapeutic content through rules-based or retrieval-based systems. Their strength is predictability, but that same design limits their capacity to personalise support or to recognise what an individual actually needs in the moment.

Chatbots built on large language models (LLMs) work differently. They can simulate core aspects of a therapeutic encounter, including personalised suggestions and empathetic reflections. That flexibility comes with a trade-off. GenAI systems may produce responses that are incorrect or inappropriate, and their open-ended conversational capacity makes intervention design both more consequential and more complex than it is for rules-based systems. When a system can say almost anything, design decisions carry considerably more weight.

How the review was carried out

The researchers set out to map the design characteristics and UX outcomes of interventions involving GenAI mental health chatbots. They began with a systematic literature search to identify studies covering the design and deployment of such tools. Reviews, editorials, media articles and commentaries were excluded.

In total, 21 studies were selected, conducted across 11 countries between 2023 and 2025. The largest numbers came from China and the United Kingdom, followed by the United States. The included work spanned a wide range of maturity, from early-stage prototype evaluations through to clinical trials, with one real-world implementation study.

Most studies recruited general or clinical adult populations, including older people living with dementia. Others involved simulated users or university students. Sample sizes ranged from as few as five participants to as many as 527. Across the body of evidence, there was substantial heterogeneity in outcome measures, and most interventions remained at an early stage of development – two features that shape how much can reasonably be concluded from the literature as it stands.

What the interventions were designed to do

Chatbot interventions most often targeted depression and anxiety, and tended to adopt shared therapeutic mechanisms, including mindfulness, emotion regulation and cognitive restructuring. Some systems were oriented towards mental well-being, stress and loneliness, emphasising general support and preventive care rather than treatment for a specific condition. Others addressed eating disorders, post-traumatic stress disorder and dementia.

The dementia-focused interventions are worth distinguishing. Rather than attempting to address the central neurological features of the condition, they targeted its related psychological dimensions – carer burnout, psychological distress and loneliness among them.

Most interventions were grounded in cognitive-behavioural therapy principles. The specific techniques drawn upon included behavioural activation, psychoeducation, Socratic dialogue, acceptance and commitment therapy, cognitive restructuring and mindfulness.

How the tools were delivered

Interventions varied in frequency, delivery modality and duration. The majority were short-term, running from two to eight weeks. Most were deployed through web-based interfaces and mobile applications, while some were delivered via messaging or social media platforms – meeting people on services they already used rather than asking them to adopt something new.

About 67% of interventions were non-embodied, text-based chatbots. The remainder used voice, avatar-based, augmented reality or other multimodal forms of interaction, with the intention of improving engagement and realism.

What people made of them

All but two of the studies evaluated at least one UX domain. The majority relied on quantitative measures, typically Likert scales, while some gathered qualitative feedback through open-ended questions and semi-structured interviews.

User satisfaction and acceptability were the most commonly reported outcomes. Across studies, participants described the interventions as convenient and accessible, with acceptability generally rated moderate-to-high and reported satisfaction high.

Half of the studies examined usability, using qualitative feedback, the System Usability Scale or Likert scales. Interface design, interaction mode and deployment platform were all observed, alongside differences in usability between studies. A clear preference emerged for free-flowing chat interfaces and customisable features over predefined options. At the same time, some interventions had an unclear scope or limited functionality, leaving people uncertain about what the chatbot could actually do for them.

Usability, engagement and the drop-off problem

Only some studies reported objective utilisation and engagement metrics, such as session frequency, interaction duration, retention over time and task completion. Where these were captured, attrition patterns frequently emerged over time in repeated-measures designs. Uptake in multi-week interventions was often high at the outset before declining – a pattern familiar across digital health more broadly, and one that matters a great deal for interventions whose therapeutic logic depends on sustained practice.

Personalisation and perceived benefit

Most chatbots featured some form of personalisation, reflecting their capacity to adapt conversations and interfaces in response to previous interactions and a person’s emotional state. The most common approach was emotion detection paired with adaptive interaction, allowing people to receive tailored responses and empathetic reflections.

Perceived impact was not consistently measured as a standalone metric. It was more usually folded into qualitative feedback or broader UX evaluations. In the intervention that produced the most granular data, the most frequently reported benefit was improved clarity and awareness.

Where empathy stops being enough

The review’s more cautionary finding is that personalisation and empathy did not consistently translate into stronger clinical outcomes or sustained use. Feeling supported and being helped are not the same thing, and the studies reviewed do not yet demonstrate a reliable link between the two.

Some people reported responses that were repetitive, generic or contextually misaligned. Others raised concerns about over-reliance on chatbots, reduced human contact, data privacy and whether these systems can respond appropriately when someone is in crisis. Inaccurate or clinically misaligned outputs were also linked to an erosion of trust and to disengagement in several studies.

For healthcare professionals, the practical question is less whether these tools have promise than how to appraise them – knowing what a given system is grounded in, where its limits sit and when a conversation needs to move to a human. That judgement is increasingly treated as a core clinical competency, and it sits at the centre of CPD training on the everyday, ethical use of AI in practice.

Design features linked to a better experience

The authors identified several design features associated with better UX outcomes, while being careful to note that these were associations rather than demonstrated causes. They included:

• Deployment on platforms people already knew and used
• Richer interaction modalities beyond plain text
• Integration into existing care pathways
• Personalisation
• Grounding in domain knowledge
• Structured delivery
• Proactive outreach
• Co-design with both experts and end users

The predominance of early-stage studies, combined with limited direct comparative analyses, prevented firm conclusions about which of these features genuinely improved user experience.

What needs to happen next

Taken together, the review suggests that GenAI chatbots have meaningful potential to deliver tailored, empathetic mental health support, and that their acceptability among the people who use them is promising. That is a real finding, and not a small one given the scale of unmet need.

Significant challenges remain, however. Standardising how UX is assessed, grounding intervention design in the needs and preferences of the people who will use these tools, and sustaining engagement beyond the first few weeks all stand out as unresolved. Addressing them, the authors argue, will require co-design with experts and users, validated UX metrics applied in long-term studies, transparent reporting standards, independent evaluation, clearer reporting of model design and training data, and stronger attention to safety, equity and the limits of crisis response.

CCH insight

Generative AI tools are arriving in patient-facing care faster than the evidence base supporting them, which puts the burden of appraisal on clinicians. CCH’s CPD-accredited short course AI Essentials for Primary Care: Tools, Ethics and Everyday Applications covers the practical and ethical judgement this requires – what these tools can and cannot do, where the risks sit, and how to use them safely in day-to-day practice.

Find out more about AI Essentials for Primary Care →

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Large Language Models Show Promise in Detecting Drug Safety Signals from Clinical Notes

Key Takeaways:

• Large language models can identify immune-related adverse events in clinical notes without task-specific training, offering a potential alternative to labour-intensive manual review
  
• Performance remains below the threshold required for clinical decision support, with models tending to overpredict adverse events
  
• Despite limitations, this approach may support large-scale safety monitoring and accelerate research into cancer immunotherapies
  
  
  

The challenge of detecting drug safety signals

Drug safety signals are often embedded within unstructured clinical text, particularly in electronic health records. Identifying these signals has traditionally required either manual chart abstraction, which is resource-intensive, or natural language processing systems tailored to specific drugs and healthcare settings.

This challenge is particularly evident in the case of immune checkpoint inhibitors. These cancer therapies, first introduced in 2011, are associated with a broad range of immune-related adverse events. These events can affect multiple organ systems, including the colon, liver, lungs, heart, nervous system, skin, and endocrine system, making systematic detection complex and time-consuming.

Exploring large language models as a solution

Large language models are increasingly being explored as a way to streamline the identification of drug safety signals within clinical text. A multicentre study, published in eBioMedicine, evaluated whether these models could detect immune-related adverse events associated with immune checkpoint inhibitors.

The study focused on a zero-shot learning approach. In this setting, the model receives a single, detailed prompt without prior examples. The prompt used by the researchers began: “You are a clinical expert in identifying immune-related adverse events caused by immune checkpoint inhibitors …” and included a list of six immune checkpoint inhibitors alongside numerous associated adverse events.

This prompt was applied to clinical notes from multiple sources. These included records from 100 people treated at Vanderbilt Health, 70 people from the University of California, San Francisco, and 272 people enrolled in seven Roche-sponsored clinical trials.

Study design and model performance

The research team evaluated three models: GPT-3.5, GPT-4, and GPT-4o, with GPT-4o demonstrating the strongest overall performance.

To assess accuracy, the investigators used F1 scores, a metric that balances false positives and false negatives. Scores range from zero to one, with values above 90 percent considered excellent. A score of 80 percent or higher may be sufficient for use in automated clinical decision support systems.

At the patient level, GPT-4o achieved average F1 scores of 56 percent for Vanderbilt Health data, 66 percent for University of California, San Francisco data, and 62 percent for Roche clinical trial data. The models showed a consistent tendency to overpredict the presence of immune-related adverse events.

When analysing individual clinical notes, the model achieved an average F1 score of 57 percent across 667 notes from Vanderbilt Health, evaluating 17 different adverse events.

Implications for clinical practice and research

The findings suggest that large language models can play a role in identifying drug safety signals, even without task-specific training data.

“Manual patient chart abstraction for monitoring the safety and efficacy of drugs already at market requires tremendous resources and puts a drag on the pace of discovery in precision medicine. And that’s especially true with immune checkpoint inhibitors, where the adverse events are so varied. If zero-shot learning with LLMs could help with these notes, it could significantly reduce time and costs for all concerned,” said the report’s corresponding author, Cosmin Bejan, PhD, assistant professor of Biomedical Informatics at Vanderbilt Health.

However, the current level of performance falls short of what would be required for clinical decision support.

“These results show that zero-shot learning with a powerful LLM is useful for detecting these adverse events,” Bejan said. “This performance does not rise to the level required for clinical decision support, but the method could be valuable for automated irAE extraction across multiple sites, potentially speeding discovery and enhancing the safety and effectiveness of cancer immunotherapies.”

Wider research context

The study involved collaboration among multiple researchers at Vanderbilt Health, including Yaomin Xu, PhD, Eric Mukherjee, MD, PhD, Matthew Krantz, MD, Douglas Johnson, MD, MSCI, Elizabeth Phillips, MD, and Justin Balko, PhD. Funding support was provided in part by the National Institutes of Health.

Related research further highlights safety concerns associated with immune checkpoint inhibitors. In a research letter published in JAMA Oncology, Mukherjee, Phillips, and colleagues used logistic regression analysis of adverse event reports from the Food and Drug Administration. They confirmed that these therapies are independently associated with an increased risk of Stevens-Johnson syndrome and toxic epidermal necrolysis, which are severe and potentially life-threatening skin reactions. The study also found that this risk may be linked to exposure to human leukocyte antigen–restricted drugs.

Conclusion

Large language models represent a promising tool for extracting clinically meaningful insights from unstructured health data. While their current performance limits direct clinical application, their ability to operate across multiple datasets without task-specific training suggests potential for supporting large-scale pharmacovigilance efforts. As these models continue to improve, they may contribute to more efficient and comprehensive monitoring of drug safety in clinical practice.

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AI in Healthcare: Promise, Pitfalls, and the Risk of Misguided Medical Advice

Key Takeaways:

• People using AI for health advice often struggle to interpret and communicate symptoms effectively, leading to incorrect conclusions in many cases.
  
• Even when AI identifies a condition correctly, it may fail to recommend appropriate urgency, particularly in time-sensitive or complex scenarios.
  
• Clinicians see value in AI as a supportive tool, but stress that it should complement, not replace, professional medical care.
  
  
  

AI becomes a common source of health information

As technology companies continue to develop platforms tailored for healthcare consultation, artificial intelligence is becoming an increasingly influential part of how people make decisions about their health. According to OpenAI, more than 40 million people use ChatGPT each day to seek health-related information.

However, emerging research suggests that while these tools offer unprecedented access to medical knowledge, they may also mislead users in certain contexts.

Challenges in how people use AI for medical queries

One of the central issues identified by researchers is not only the capability of AI systems, but how individuals interact with them. Many people lack the knowledge required to communicate symptoms accurately or comprehensively.

A recent study published in Nature Medicine attempted to replicate real-world use of AI chatbots. Participants were given medical scenarios and asked to consult AI tools. The results highlighted notable limitations:

• Participants correctly identified the condition only about one-third of the time.
  
• Just 43% made the correct decision regarding next steps, such as whether to seek emergency care or remain at home.
  

“People don’t know what they are supposed to be telling the model,” says Andrew Bean, who studies AI systems at Oxford University and was one of the authors on this study.

Bean explains that effective use of AI often depends on precise wording. “Doctors are trained to ask you questions about symptoms you might not have realised you should have mentioned,” says Bean.

Small differences in input can lead to dangerous outcomes

The study demonstrated how subtle differences in language can significantly alter the advice provided by AI systems.

In one example, two individuals described the same clinical scenario slightly differently. One described experiencing “the worst headache I’ve ever had” and was advised to go to the emergency room immediately. The other, who did not include that specific phrasing, was advised to take aspirin and remain at home.

“Turns out this was actually a life-threatening condition,” says Bean.

This highlights a critical limitation: AI systems rely heavily on the information they are given, and may not prompt for missing but clinically important details in the way a trained clinician would.

When AI gets the diagnosis right but the advice wrong

Even when AI tools successfully identify a medical condition, they may still provide inappropriate guidance regarding urgency or next steps.

In a separate study, researchers evaluated how AI systems responded to a range of medical scenarios. They found that in 52% of emergency cases, the tools “under-triaged” – treating conditions as less serious than they actually were.

In one case, the AI failed to direct a hypothetical patient experiencing diabetic ketoacidosis and impending respiratory failure – both life-threatening conditions – to seek emergency care.

“When there was a textbook medical emergency, ChatGPT got it right,” said Girish Nadkarni, a doctor and AI researcher at Mount Sinai who is an author on the study. However, he noted that performance declined in more complex situations, particularly where timing was critical. In such cases, the system often misjudged how urgently care was required.

An OpenAI spokesperson responded by stating that the study did not reflect typical real-world usage and that it evaluated an older version of ChatGPT, which the company says has since been improved to address some of these concerns.

The role of AI in supporting patient understanding

Despite these concerns, many clinicians believe that AI tools can still play a constructive role in healthcare, particularly in improving patient understanding and engagement.

“I encourage patients to use these tools,” says Robert Wachter, a doctor at UC San Francisco and author of the recently published book, A Giant Leap: How AI Is Transforming Health Care and What That Means for Our Future.

Wachter points out that barriers to accessing healthcare – including cost and availability – mean that AI can sometimes provide a useful alternative source of information. “The advice you get from the tools is substantially better than nothing and better than what you would get from your second cousin,” says Wachter.

However, he emphasises that AI should never be viewed as a substitute for professional medical care.

Enhancing, not replacing, the doctor–patient relationship

Experts suggest that AI is most valuable when used alongside traditional healthcare, rather than in place of it.

Adam Rodman, a hospitalist and researcher at Harvard Medical School, advises against using AI tools to assess emergency situations. Instead, he sees their greatest benefit in preparing for or reflecting on medical consultations.

“A good time to use a large language model is when you’re about to go see a doctor – or after you see your doctor,” says Rodman.

He explains that AI can help people better understand their condition, ask more informed questions, and make more effective use of time during appointments. This can support a more collaborative relationship between patients and clinicians.

“There are no downsides to better understanding your health,” says Rodman.

The future of AI in healthcare

Healthcare professionals broadly agree that AI is now firmly embedded within modern medicine and will continue to evolve alongside clinical practice.

“ My hope is that you might see AI as an extension of a human relationship,” says Rodman. He envisions a future in which both clinicians and patients work with AI to improve communication and navigate healthcare systems more efficiently.

However, he also raises concerns about potential unintended consequences. One particular risk is the possibility that people may receive serious or distressing diagnoses – such as cancer – directly from an AI system, rather than from a clinician.

Research suggests that when healthcare becomes more transactional or resembles a marketplace, trust in clinicians may decline.

 ”What I hope is that this technology can be used in a way that enhances humanity in medicine,” says Rodman “and not in a way that cuts out the doctor-patient relationship.”

Conclusion

Artificial intelligence is rapidly transforming access to health information, offering both opportunities and risks. While these tools can enhance understanding and support more informed decision-making, their limitations – particularly in how they interpret incomplete or imprecise input – mean they must be used with caution.

Ultimately, AI has the potential to strengthen healthcare delivery, but only if it is integrated in a way that supports, rather than replaces, the human relationships at the heart of medicine.

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