Healthcare Transformation using Artificial Intelligence

Book Description:

Explains recent Artificial Intelligence breakthroughs, critically evaluates how AI can be selectively and successfully deployed to alleviate current challenges to health and healthcare systems.

Key Features

Chronicles healthcare systems’ challenges and the most promising transformational techniques using AI Explains how AI works from scratch using only basic mathematics Takes a tour through 6 healthcare settings, explaining how AI can help transform each of them Explains safety, ethics and regulation and how AI is evolving to meet these requirements Healthcare Transformation Using Artificial Intelligence provides insights into why AI is becoming an essential tool to transform healthcare systems to achieve better outcomes and costs. Healthcare leaders and practitioners need to understand what kinds of improvements elementary AI, such as machine learning, can already deliver, as well as the further potential that is now emerging through Generative AI and Large Language Models (LLMs).

A clear tutorial exposition is given for AI tools from the most basic up to and including advanced LLMs and image processing. Examples of AI deployment are given in the following settings: the doctor’s office; patients at home and in the community; patients outside the system and mental health; post-discharge and recovery; radiology and medical imaging; and, in emerging and low-resource economies. Finally, we consider safety, regulation and ethics, and explain both the current limitations and the potential of AI as it transforms healthcare. Besides tutorial and explanatory content, the book also interviews leading researchers and practitioners to understand both current capabilities as well as how AI is expected to accelerate healthcare transformation.

1. You position AI as an “essential tool” rather than a silver bullet. What structural shortcomings in today’s healthcare systems make AI particularly well-suited - and where do you believe AI has limitations?

Decision makers in healthcare are short of time and resources, and overloaded with information. AI can help in the former by automating some tasks and in the latter by summarising information and, in some cases, suggesting next steps.

Established methods of AI are already becoming widely used, e.g., machine learning in medical imaging, where almost 1000 AI-based medical devices are already approved by the FDA. And transcribing/summarising medical encounters using “scribes” is saving clinicians hours per day.

But the most advanced techniques, e.g., chatbots that use GenAI, while enticing and impressive, can make mistakes. Most AI is still best used in partnership with humans and no technology can replace a human’s empathetic and caring judgment.

2. The book begins by demystifying how AI works using only basic mathematics. Why was it important for you to strip away the mystique, and how does this foundational understanding change how healthcare leaders should evaluate AI vendors?

AI is not magic, and (with a little bit of effort) most of how it works can be quite well learned and understood by its users. This will demystify AI and allow us to best apply and keep in mind its limitations.

3. You distinguish between elementary AI, machine learning, and emerging Generative AI. In practical healthcare terms, where should organisations start today, and where should they resist jumping too fast?

Some AI tools are more ready than others. Medical Imaging and transcription tools are here today and have already demonstrated their value, although they still require checking. Chatbots such as ChatGPT, Gemini, Claude, etc., are useful for question answering, and most of us are already using them, getting value, learning their limitations, and carefully adopting them into our workflows. Most organisations are encouraging some form of use, but educating their staff in appropriate use and safety. 

4. Across the six healthcare settings you explore, which setting has shown the highest readiness for AI transformation - and which faces the greatest cultural or systemic resistance?

Radiology and other forms of imaging are the leaders in adoption. Scribe tools in medical encounters are becoming prevalent. Clinical Decision Support (CDS) has some of the greatest potential, but is still relatively basic and mostly advisory.

The most impressive use might be in low-resource settings such as emerging economies, where AI is helping by quickly filling some basic gaps in information, access and care.

5. In the doctor’s office, AI is often framed as a productivity tool. How can AI be deployed without exacerbating clinician burnout or turning care into a purely transactional experience?

You’ve asked one of the most important questions. If the benefits of AI end up causing only shorter per-patient times, we will have “shot ourselves in the foot”. True breakthroughs will come when we share the productivity and quality improvement potential of AI with both improvement in patient outcomes as well as slowing cost growth.

And concerns about deskilling and “dethrilling” are real. We need to create new models of “human+AI teams” which avoid these traps on our journey to better and more affordable care.

6. Your discussion on patients at home and in the community highlights AI beyond hospital walls. How does this shift challenge traditional care models and accountability?

The book takes guidance from the 40/30/20/10 rule, which emphasises that 40% of health outcomes are due to how the patient leads his or her daily life, including diet, activity, stress, substance use, etc. AI can help the patient when deployed in a safe way, and it is already doing so in self-management, sensing and intervention, etc. AI offers the potential for the patient to take an even greater role in self-care and reap the benefits both in health and financially.

7. Mental health and patients outside the formal system represent some of healthcare’s hardest problems. What makes AI uniquely capable - or uniquely risky - in these contexts?

A report highlighted by the Harvard Business Review in May 2025 found that "therapy/companionship" topped the list of ChatGPT uses. Carefully deployed AI for mental health has already shown good success, but there have been tragic outcomes in rare cases when users have “spiralled downwards” in their use of chatbots. AI joins several other technologies, such as social media, in being double-edged swords: they serve useful purposes but can also create harm when misused.

8. Radiology and medical imaging are often seen as AI’s biggest success story. What lessons from imaging can realistically be transferred to other clinical domains, and which cannot?

Radiologists have done a great job of introducing AI into their practices as an aid or “team member”, not a replacement. We can learn from their experiences as we create AI+human teams that bring out the best capabilities of each. As we deploy AI into venues such as medical consultations, interactions that are more complex come into play.

9. You dedicate attention to emerging and low-resource economies. How can AI avoid reinforcing global healthcare inequities, and what design principles are essential for these settings?

Dr Skikoh Gitau of Qhala Health in Kenya is a role model. She shows that if you stay relentlessly focused on true patient needs, you can make a huge impact even with modest investments. Such fundamental needs include awareness, communications (including overcoming language and cultural sensitivities), and access (including transportation and obtaining medical supplies).

10. Safety, regulation, and ethics are recurring themes in the book. From your perspective, is regulation currently a brake on innovation - or a prerequisite for meaningful clinical trust?

If done right, regulation can be an enabler. Well thought out regulation is both supporting and informing innovation, even to the extent of providing guidelines, tools and sandboxes to help both the tech creators as well as adopters. Regulators around the world are listening, observing, and generally working towards a balance of protecting all parties while allowing for and encouraging appropriate experimentation and adoption.

11. Large Language Models are changing the conversation rapidly. What new categories of risk do LLMs introduce into healthcare compared to earlier AI systems?

LLMs are a massive leap forward in capability - they can tackle almost any question or need, based on their training on massive amounts of both general and health knowledge. And while they are already delivering value, we should keep in mind their limitations and potential for errors and hallucinations. With appropriate use and care, they are “net positive”.  With further development and improvements in safety, they will undoubtedly play a key role in the transformation of healthcare. And there’s no stopping them: according to some estimates, a majority of health workers are already using them!

12. You emphasise selective and successful deployment rather than blanket adoption. What decision framework should healthcare leaders use to decide where AI truly belongs - and where it doesn’t?

Read, study, attend workshops and conferences, talk to your colleagues, try out these tools, and if possible, track and measure their results in your environment. They are going to change practice, exactly how will depend on all of us. Some providers and clinics are forming reading and experience-sharing groups, and some are forming AI Deployment Offices. Get involved!

13. Through interviews with researchers and practitioners, what surprised you most about the gap between academic AI capabilities and real-world healthcare deployment?

It's more of a continuum than a gap. From large institutions to small practices of all kinds, an impressive amount of both bottom-up and top-down innovation is taking place. Curiosity, initiative and getting a competitive edge are the drivers, as they have always been in any transformation.

14. Looking ahead five to ten years, what would “successful healthcare transformation using AI” actually look like - not in terms of technology, but in patient outcomes, clinician experience, and system sustainability?

AI holds the potential to result in an improvement of patient outcomes and cost sustainability. It will play a key role in how we deal with cost drivers such as aging and should help to address overdue improvements for underserved communities. Of course, how AI ends up contributing on the balance sheet of benefits will depend on how and where it is deployed. As we each “place our bets”, we will be thinking through experience of all stakeholders: provider systems, healthcare workers, communities, and patients. AI can help us to weave all of them into a new model of shared care that improves the scorecard for us all.