1. Over the last nine months, what AI development do you believe has had the biggest impact on healthcare communications and why?
The biggest shift has been the move from “search” to “answer” with patients and HCPs increasingly starting with a large language model rather than a journal or search engine. This new dynamic means that people are being given interpreted answers, and for healthcare communications, that has huge implications.
It raises the bar on accuracy, it’s no longer simply whether information exists, but how it’s summarised, framed and understood. Communicators must pay attention to how science is represented in AI environments, and where errors or outdated information could distort understanding.
That’s why at Real Chemistry, we invested in HealthGEO to give us insight into how generative AI is portraying a brand’s science in the wild - what models are saying, where inaccuracies appear, and how to respond proactively.
2. Gen AI is evolving rapidly. What are the most important AI literacy skills healthcare leaders need to develop today to stay relevant and effective?
First, prompting, and I mean that in the same way I’d talk about writing a strong brief. The leaders getting the most from AI know how to ask the right question, provide the right context, and push the tool to challenge their thinking rather than just validate it.
Second, critical evaluation - to assess, interrogate, and improve what you’re given. In healthcare, especially, you cannot accept a polished output at face value.
And third, governance literacy. Leaders need enough understanding of privacy, bias, IP and regulatory risk to create clear guardrails so teams can experiment safely and confidently.
You don’t need to code, but you need to direct AI well, question it intelligently, and use sound judgement about its limitations.
3. As Head of Global MedComms, how are you seeing AI reshape the relationship between scientific storytelling, data, and patient engagement?
AI is bringing those three areas much closer together. What used to be a linear process -data, then narrative, then dissemination - is becoming far more connected and dynamic.
We can now move quickly from insight to story to engagement, tailoring content for different audiences with relevance and precision – whether to a rare disease patient, a caregiver or a time-poor specialist.
The feedback loop is also changing. AI helps us understand how and what people search, and where gaps in communication exist. That has the potential to make scientific storytelling not just efficient but more responsive.
But scientific integrity must remain centre stage. AI can accelerate storytelling and improve relevance, but it should never reshape the evidence to fit the story.
4. Many healthcare organisations are still cautious about AI adoption. What practical steps can teams take to move from experimentation to confident implementation?
It starts with leadership. If leaders want responsible adoption, they need to use it themselves - openly demonstrating where it adds value, where human judgement still matters, and being honest about both the wins and limitations. People take their cue from what leaders do far more than what they say.
We are also at the doorstep of an exciting transformation in the future of work. We’re all learning in real time, and there’s no perfect blueprint, but the best organisations will embrace reality, encourage curiosity and learn from one another.
You have to create permission to experiment and to fail. People won’t build confidence if every attempt has to be perfect. The key is to make experimentation safe: clear policies on data, privacy, and human review so people can move with confidence inside defined guardrails.
5. Women in AI Healthcare is focused on empowering female executives. In your view, why is female leadership especially important in shaping the future of AI in healthcare?
Because AI reflects the people who build, train and govern it. If women are underrepresented, we risk carrying existing blind spots into the next generation of healthcare tools, at scale.
That’s critical in healthcare, where women are both a major part of the workforce and often the primary health decision-makers in families. Female leadership is essential if we want AI systems that reflect the realities of the whole population, not just part of it.
Women bring thoughtfulness, healthy scepticism, empathy and evidence-based thinking - strengths that are incredibly valuable in a field where trust, safety and judgement matter. So let’s bring those strengths into the room and help shape what responsible progress looks like.
And while the rules are still being written, diverse leadership really matters. It can help shape how AI is used in healthcare from the outset - and that will be fairer and more effective if women are actively helping define the standards, strategy and ambition.
6. What barriers still exist for women looking to lead AI-driven transformation initiatives within life sciences organisations?
Some barriers are structural: fewer women in technical and data-focused roles, less access to sponsorship, and fewer opportunities to lead high-visibility transformation projects that often become career-defining.
There’s also a confidence and permission gap - women are less likely to put themselves forward, and when they do, they’re not always taken as seriously as they should be, particularly in highly technical spaces. AI can amplify that, but no one really has universal ownership of AI right now. AI leadership comes from curiosity, initiative and a willingness to shape what comes next. That can be as daunting as it is exciting.
Women in life sciences often bring enormous value in judgment, ethics, communication, audience understanding and governance – and AI needs those skills too.
So what do we do? We need to make AI leadership feel claimable to give women permission to lead, to sponsor them and take their contributions seriously. It is not just about building the technology, it’s about knowing where to apply it and how to govern it to make it useful, safe and trusted. Organisations have a role to play, but women need to put their hand up and recognise that this space is theirs to shape too.
7. Have you noticed any differences in how women leaders approach AI adoption, governance, or communication strategies compared to traditional approaches?
The important distinction isn’t gender itself, but the leadership mindset being brought to AI. In healthcare, the most effective leaders balance ambition with responsibility, looking to apply AI safely, credibly and in ways people trust.
That said, many women leaders I know bring a strong focus on inclusion, communication and human impact – and as AI reshapes workflows, decision-making and relationships, the ability to listen well, build trust and bring people with you matters enormously. I’d be careful about overgeneralising, but I do think those qualities are exactly the ones this moment demands.
8. With the rise of AI-generated content, how can medical communications teams maintain scientific accuracy, trust, and compliance?
Human accountability has to remain non-negotiable, especially for clinical claims, medical education or regulated content - a named expert should always remain responsible for final review and sign-off.
Practically, that means grounding AI outputs in validated, referenced source material rather than relying on the open web; building medical, legal and regulatory review into the workflow, not bolting it on at the end; and maintaining a clear audit trail of where AI was used and how outputs were checked.
AI can be incredibly useful for drafting, summarising and accelerating workflows. But it should never be treated as the final arbiter of medical truth. That responsibility stays with people.
9. What are some misconceptions healthcare professionals still have about Gen AI that need to be addressed?
The two biggest misconceptions tend to sit at opposite extremes. One is that AI is somehow inherently correct, a kind of infallible oracle. The other is that it is just hype and has no serious role in healthcare.
In reality, AI is a powerful tool, but it’s also fallible. It can accelerate thinking, support analysis, and improve efficiency, but it still sounds confident when it’s wrong. That’s why expertise matters.
Another misconception is that AI will replace professionals. I don’t believe that. But it does amplify good judgement and expose weak judgement, so, used well, it gives experts more time for the thinking, context and decision-making that only humans can provide.
10. How do you balance innovation with ethical responsibility when integrating AI into healthcare communication workflows?
I do not see them as opposing forces. In healthcare, ethical responsibility is what makes innovation viable, because without trust nothing useful gets adopted.
In practice, ethics has to be integrated from the beginning: privacy-safe data use, transparency about where AI is being used, active consideration of bias, and clear human accountability at every important decision point.
But people need room to experiment, so the goal is to create clear guardrails so teams can explore confidently without compromising patient safety, compliance or scientific integrity. For me, the test is simple: would I be comfortable explaining how this was created to the patient or healthcare professional? If the answer is no, we need to rethink it.
11. Looking ahead, which AI trends or technologies do you think healthcare executives should pay closest attention to over the next 12–18 months?
I would focus on three things.
First, the continued rise of AI copilots and workflow tools that are becoming genuinely useful in day-to-day work. We are moving beyond experimentation into tools that can help teams draft, analyse, synthesise and automate.
Second, automation. Not by replacing expertise, but removing repetitive, low-value tasks so people can spend more time on strategy, judgement and high-quality scientific work - where AI will create meaningful impact.
And third, the growing importance of human expertise that AI can’t replicate: judgement, context, creativity, emotional intelligence and trust. As AI becomes more capable, those differentiators become more valuable, not less.
So my advice would be: pay attention not only to what AI can do, but to what only your people can do, and design workflows that strengthen both.
12. For women attending the event who are just beginning their AI journey, what advice would you give them to build confidence and become active participants in AI conversations?
Start now, start small, and do not wait until you feel fully qualified. Confidence rarely comes before action. It usually comes from doing.
Use the tools on something familiar first. It needn’t be work, just start somewhere you already have context so you can compare the output with your own judgement and learn how the tool thinks.
Find people who know more than you and learn alongside them. Ask questions. Talk about it at dinner parties, events, barbecues, wherever you can. Ask what’s working, what’s not, and share your own learning too - especially with other women.
Most importantly, remember that your expertise isn’t secondary to the technology – it’s what gives the technology value. The conversations shaping AI in healthcare need your voice in them, so don’t wait to be invited to contribute.
13. Finally, what do you hope attendees will take away from your “Last 9 Months in 9 Minutes” session at the Women in AI Healthcare meet-up?
Two things, really.
First, I hope they leave feeling inspired and reassured that AI is far more accessible than it can sometimes seem. There’s a lot of noise and jargon in this space, and I want people to come away thinking: I can engage with this, and I can start now.
Second, I hope they connect with someone, whether that’s someone they can learn from, collaborate with, or simply continue the conversation with afterwards. So much confidence in AI comes from community, shared learning and real conversations.
If people leave feeling energised, less intimidated, and more connected, I will feel the session has done its job.
