The Doctor-Patient Dynamic Redefined in Modern Healthcare

AI-driven preventive care is revolutionising healthcare by shifting the focus from treating diseases after they occur to predicting and preventing illnesses before they manifest. This transition is significantly altering the traditional doctor-patient relationship in several ways:

From Reactive to Proactive Care Traditionally, healthcare has been reactive. Patients seek medical attention only when symptoms arise. AI changes this by continuously monitoring health data, flagging potential risks, and enabling early interventions. This approach fosters a more dynamic, ongoing relationship between doctors and patients rather than episodic interactions.

Empowered Patients and Shared Decision-Making AI provides patients with real-time health data through wearable devices, mobile apps, and predictive analytics. This increases patient engagement and encourages shared decision-making, where patients take a more active role in their health rather than relying solely on physicians’ directives.

Personalised Treatment Plans AI can analyse large datasets to generate personalised health recommendations, tailoring preventive strategies based on an individual’s genetic profile, lifestyle, and medical history. This requires physicians to interpret and explain AI-driven insights to patients clearly.

As AI takes a more significant role in healthcare, clinicians must develop new communication skills, which includes:

Data Interpretation and Explanation Physicians must be able to translate complex AI-generated data into simple, actionable advice that patients can understand.

Digital Literacy and AI Proficiency Clinicians should develop a strong understanding of AI tools, ensuring they use them effectively while addressing concerns about accuracy, privacy, and reliability.

Empathy and Patient-Centred Communication Despite AI’s capabilities, maintaining the human touch is crucial. Physicians must reassure patients that AI is a tool for support, not a replacement for human expertise.

Trust and Ethical Conversations AI raises ethical concerns about data privacy and decision-making. Doctors must guide patients on how their data is used and ensure informed consent.

By adapting to these changes, clinicians can foster a more collaborative, patient-centric healthcare environment where AI enhances- rather than replaces the doctor-patient relationship.

AI Tools to Create Active Healthcare Participants

AI tools are transforming patients from passive recipients of healthcare to active participants in managing their health. This shift is crucial because an informed and engaged patient is more likely to adopt healthy behaviours, adhere to treatment plans, and seek early intervention when needed.

AI enhances patient engagement in several ways:

Continuous Health Monitoring Wearable devices (e.g., smartwatches, biosensors) track key health metrics like heart rate, glucose levels, and sleep patterns, alerting users and their doctors to potential risks before symptoms appear.

Personalised Health Recommendations AI-powered health apps analyse individual health data and provide tailored advice on diet, exercise, medication adherence, and mental well-being.

Telemedicine and AI Chatbots AI-driven chatbots and virtual assistants provide instant responses to health-related queries, offering 24/7 support for patients, especially those in remote areas.

Predictive Analytics for Preventive Care AI identifies patterns in patient data to predict disease risks and recommend timely interventions, preventing conditions such as diabetes, hypertension, and cardiovascular diseases.

Educating patients about AI-driven healthcare technologies is critical because:

Better Health Outcomes When patients understand AI-generated insights, they can take proactive steps to improve their health, such as adjusting their lifestyle, recognising early warning signs, and seeking medical attention at the right time.

Overcoming AI Hesitancy: Many patients may be sceptical or fearful of AI in healthcare. Education can demystify AI, addressing concerns about privacy, accuracy, and reliability.

Reducing Healthcare Costs and System Burden: When patients manage their health proactively with AI tools, it reduces unnecessary doctor visits, hospitalisations, and healthcare costs.

To maximise AI’s benefits, healthcare providers should integrate AI education into routine consultations, empowering patients to use these tools effectively for better health management.

The AI Detection Revolution

AI has the potential to revolutionise early disease detection by identifying subtle patterns in medical data that might be overlooked by traditional diagnostic methods. Some of the most promising areas where AI can significantly impact early screening and prevention include:

Oncology (Cancer Detection and Prevention)

• AI-powered imaging systems (e.g., mammography analysis for breast cancer, pap smears for cervical cancer) can detect early-stage malignancies with greater accuracy than human radiologists.
• AI-driven genomic analysis can identify genetic predispositions to cancers, enabling preventive measures such as lifestyle modifications or increased surveillance.

Cardiology (Heart Disease Prevention)

• AI can analyse ECGs, detect arrhythmias, and assess cardiovascular risk factors like cholesterol levels and blood pressure trends.
• AI-powered predictive models help identify individuals at high risk of heart attacks and strokes, prompting early lifestyle or medical interventions.

Infectious Diseases (HIV, Tuberculosis, Viral Outbreaks, etc.)

• AI-driven surveillance systems predict disease outbreaks by analysing epidemiological data.
• AI models assist in identifying high-risk individuals for infectious diseases, enabling targeted prevention and early treatment strategies.

Neurology (Alzheimer’s and Parkinson’s Disease)

• AI-based cognitive assessments and brain imaging can detect early signs of neurodegenerative diseases, allowing for early intervention strategies such as cognitive therapy or lifestyle changes.

Diabetes and Metabolic Disorders

• AI models predict diabetes risk based on lifestyle, genetics, and metabolic indicators, encouraging early dietary and exercise interventions.

By integrating AI into preventive healthcare, we can shift the paradigm from disease treatment to disease prevention, significantly improving public health outcomes.

Finding Harmony Between AI and Clinical Experience

AI’s data-driven recommendations sometimes differ from a physician’s clinical judgment, leading to potential conflicts. The best approach to reconciling AI insights with traditional medical expertise is through collaborative decision-making and evidence-based validation.

Key strategies to bridge the gap include:

Human Oversight and AI as a Support Tool: AI should augment not replace clinical judgment. Physicians should critically evaluate AI recommendations and use them as an additional layer of decision-making support.

Interdisciplinary Review and Validation: When AI suggests a novel preventive approach, it should be reviewed by experts across various medical specialties, ensuring that it aligns with established medical knowledge and ethical guidelines.

Continuous AI Learning and Model Improvement: AI models should be designed to incorporate real-world patient outcomes, continuously refining their recommendations based on clinical feedback.

Patient-Centred Decision-Making: Physicians should discuss AI-driven recommendations with patients, explaining potential benefits and risks. Patients should have a say in their care, fostering a balanced approach that considers both AI insights and human expertise.

Ethical and Regulatory Frameworks: AI integration into healthcare should be governed by ethical guidelines and medical regulations to ensure safety, transparency, and accountability.

By embracing AI while maintaining clinical oversight, healthcare professionals can leverage its potential to enhance, rather than disrupt, traditional medical practices.

How AI Will Redefine the Doctor’s Role in Healthcare

AI has already disrupted industries. Similarly to the industrial revolution that saw a huge paradigm shift from manual labor to relying to machines, AI will cause a shift from technical labor to machines. In healthcare, this is particularly important since medical education almost entirely focuses on diagnostics, pathophysiology and treatment as demonstrated by core skills exams. For now, adoption of AI is limited by government and medico-legal issues such as accuracy, ethics and concerns around patient privacy making it hard to implement en masse in hospital systems. However, new AI companies are targeting clinicians and frontline workers directly to help improve their own quality of life and make things such as admin tasks, scribing and documentation easier. In the future I believe the role of a doctor will shift from diagnostics and allow doctors to focus on things such as research, providing stellar patient-centered care, and creating more focused and customised treatment plans for patients.

Patient Education is Critical to AI Healthcare Success

It is very important because an AI is only as good as the input it is given. On top of that when it comes to patients the outcome is also dependent on the patient’s ability to understand and use the technology. There was a scene in the TV show House, MD where a patient was supposed to be taking a Ventolin puffer. When he asked her to show how it worked, instead of putting it into her mouth and inhaling she got the puffer and applied it to the side of her neck like a perfume. While it is clearly supposed to be humorous, you will be surprised by how little education is given to patients in a clinical setting with doctors simply assuming things. With AI, we are all starting on a similar learning curve which is a good thing but also I hope to see companies provide thorough education for both doctors and patients so they fully understand the tech they are using.

AI Screening is Changing the Diagnostician’s Role

The New Yorker did a fascinating piece titled “The Pastry AI That Learned to Fight Cancer” where a program designed to help bakeries check stock by taking a picture of the setup and scanning it was very similar to how cancer cells could be detected and picked up. The implications for diagnostics where traditionally a human would look at a scan and make conclusions (such as radiology and pathology) would be incredibly helpful to decreasing workload, with AI being able to flag potential issues in scans or slides. In this case, I believe the person doing the diagnostics would be able to come in and apply their own knowledge with better context and understanding of the situation.

The Critical Importance of Clinical Discernment

If it challenges traditional medical knowledge, it could signify the importance of diving deeper into the topic. It is important to remember that not all AI is entirely safe to use at the moment, so no medical professional should blindly trust it. In my day to day work I meet with doctors and other healthcare professionals to discuss the medico-legal implications of the content they produce as well as clinical decision making. In both settings, the onus is placed on the healthcare professional instead of the AI which means that your knowledge is key. In this day and age, discernment and curiosity are the most important soft skills when it comes to clinical decision making.

AI Tools Can Enhance Doctor-Patient Relationships

Physicians adopting AI can impact the doctor-patient relationship quite positively, this can take form in improved interpersonal communication, time availability for patients and improved access to information. Stanford has done a study on the second point that shows this phenomenon, doctors spend more time on data entry than with patients.

Take the example of note taking during consultation, who has not experienced his or her doctor taking notes while discussing symptoms during an appointment? Did you feel that the doctor was fully focused on you rather than data entry? AI note taking can have a positive impact on eye contact during consultation which is identified as a key trust builder between physician and patients.

Next, physicians are responding to a high volume of patient messages daily.  Considering the workload, fatigue and stress – empathy is often one of the first things to be lost. Text based AI tools can draft empathetic messages for physicians to review and modify, saving time and yet delivering superior communication impact to the patient.

Lastly, supporting AI tools are giving physicians access to more information during consultation and diagnosis. With rare diseases this can be helpful for physicians and patients to identify conditions earlier and start treatment. For more common diseases there will be development to predict progression more effectively, assisting physicians in making decisions earlier based on larger datasets of patients.

For readers interested in a deeper dive on AI in Healthcare I recommend Tom Lawry’s “Hacking Healthcare”. Tom has been a leading voice in the Healthcare space regarding AI, in his book he covers recent developments, how we need to change our approach to data and finally proposes how AI leadership and management could be organised.

The Prevention Imperative

Healthcare systems are under a tremendous amount of economic pressure due to aging populations, increasing healthcare costs, reducing number of healthcare professionals and the rise of chronic diseases.

It is paramount that we take our own health seriously to prevent illness rather than to rely on treatment solely. AI can play a role in managing and communicating real-time data sets with patients and physicians.

Wearable technology applications currently available or in development, centre around chronic disease management, sleep monitoring, drug adherence, emergency response and of course the big one – activity tracking and fitness.

The last point is one of the easiest and most inclusive ways to bolster our health – stay active.

How AI is Transforming Singapore’s Medical Screening

Singapore’s health start-up eco-system has approximately 200 companies in that field, most of them utilizing AI or machine learning in their offering.

AI has been actively adopted in diagnostic radiology, as an example, during Covid-19 Tan Tock Seng Hospital and the National Centre of Infectious Diseases utilized rapid screening for pneumonia to deliver a screening rate of one X-ray every three seconds. The country takes a systemic approach to adoption of AI tools in medical imaging with Synapxes’ AIM SG initiative to provide an open, vendor neutral platform that can implement AI models from various sources. The impact of the initiative could lead to adoption of algorithms to aid triaging patients in primary and emergency care settings.

I have followed AI in Endoscopy since 2019 and believe there is significant progress made in Colorectal Cancer Screening through a mixture of Public Policy (earlier screening age) and technology adoption in Endoscopy systems allowing for higher accuracy in polyp detection and characterization. There is clinical research ongoing on increased adenoma detection rates during colonoscopy procedures and this work could lead to earlier treatment and improved survival rates of a highly preventable disease.

Diabetes remains a major health concern for Asia, where more than 60% of patients are located and the disease continues to grow rapidly. AI can have an impact in prevention, diagnosis and treatment when implemented across the healthcare setting for the disease.

As we look at the adoption of AI in Healthcare, clinical care will experience significant progress, however we should also look at Healthcare system improvements that can be achieved through AI adoption such as Hospital process efficiencies in planning, budgeting and operations.

Navigating AI Integration in Clinical Decision-Making

This is the million-dollar question, and a satisfactory reply won’t exist for a while, as with every new technology being adopted. I believe that further discourse, research and experimentation are essential in advancing this question.

Physicians have the patients’ best interest as priority and are guided by their oath, high professionalism and their compassion to ensure that patient outcomes are delivered in the safest manner possible. In the end the responsibility lies with the physician and final decisions must be made by a human, the interesting question is at which point of the clinical process to bring in assistive technologies.

We also should consider that Medicine is still a developing field, we understand anatomy and physiology, yet the microbiome and disease mechanisms are still somewhat a frontier. So, providing physicians with advanced tools to care for patients is paramount. An important question to ask is, what is the current decision-making process today and where the gaps are. As example, clinical decision making is impacted by individual skill, experience and specific clinical context. Additionally, factors like cognitive biases, time constraints and system limitations can influence decision making by clinicians.  Research into Computerized Decision Support shows that technology has potential to enhance practitioner performance, for example in breast cancer identification, cardiovascular risks and diabetes diagnosis.

Clinicians are going to be faced with decisions during adoption of AI tools in practice, as with other fields we see varied adoption rates between early adopters and laggards. We need more discussion and experience in developing processes to safely adopt new technology – however, in the long run AI will benefit medicine tremendously.