Artificial Intelligence is revolutionising consultant-led care, from augmenting clinical decisions to streamlining admin and enhancing patient outcomes. Mark Scrivens, FPT Software UK, CEO, explores five powerful ways next-generation AI tools are reshaping healthcare delivery, efficiency and the consultant’s evolving role.
As healthcare systems worldwide strive for improved patient outcomes, efficiency and equitable access to advanced treatments, Artificial Intelligence (AI) is set to be a transformative force.
Healthcare consultants are under increasing pressure to navigate a continuously evolving body of medical knowledge, handle increasingly complex patient populations, manage administrative burdens and deliver patient-centered care—all cost-effectively.
Generative AI (Gen AI) and advanced Machine Learning models have emerged as the key tools to empower consultants and their teams. Hospital and healthcare leaders have realised they must embrace AI technology for increased efficiency and improved patient care. In fact, the UK Government has launched a £150 million (US$185 million) procurement drive for AI solutions, putting out a request for information, indicating that AI is high on the agenda.
There are both opportunities and challenges of next-generation AI tooling and by exploring some of their potential application areas, healthcare leaders can start to build a roadmap for integration into consultant-led care.
How AI is impacting the role of healthcare consultants
Next-generation AI solutions are in place today supporting consultant-level care in many ways. This ranges from providing support in guiding junior doctors in complex decision-making, offering prognostic insights in oncology or cardiology and being tools to optimise orthopaedic recovery, pain management, rehabilitation and fall prevention.
The reality is that next-gen AI solutions are being technically implemented within clinical workflows. Seamless electronic health record (EHR) integration and user-friendly dashboards ensure that AI insights are accessible at the point-of-care. Interoperable data standards and robust API frameworks facilitate smooth adoption within existing clinical workflows.
AI solutions are also enhancing patient engagement, being integrated with wearables and smart environments for proactive interventions, and playing a key role in informing clinicians of emerging niche treatments.
Taking the example of orthopaedic care, a learning LLM can absorb the consultant’s expert nuances, ensure awareness of emerging therapies, integrate patient-specific biomechanical data and ultimately improve detection and treatment outcomes for patients with complex bone and musculoskeletal conditions.
AI technology can also provide more proactive diagnosis and treatment in the areas of pain and trauma management, physiotheraphy, rehabilitation and fall prevention. By integrating patient-reported outcomes, biometrics and system data, it can help consultants to forecast episodes, recommend interventions and modify care plans to improve pain control, prevent injuries and optimise patient care.
How AI can enhance core consultant responsibilities
There are five ways in which AI can support the role of healthcare consultants:
- Augmenting clinical decision-making
AI-driven decision support systems can provide junior doctors and consultants with real-time summaries of relevant guidelines, emerging therapies and complex clinical protocols. Gen AI models can distill huge volumes of literature, highlight dedicated treatments for rare diseases, and identify medication programmes tailored to the patient’s genomic and clinical profile.
- Prognostic modelling and specialist-level insights
Advanced AI models can forecast patient trajectories, aiding consultants in predicting adverse events, gauging treatment efficacy and planning long-term care. In cardiology, for instance, AI algorithms can pre-empt arrhythmic episodes or detect orthostatic hypotension risk factors, preventing dizziness-related falls. A notable example is FPT’s eCarePlus, an AI-powered solution that analyses ECG and patient activity data to detect early signs of syncope and fall risks. This proactive system enables timely interventions that improve patient safety and reduce complications from falls.
- Streamlining administrative burdens
AI tools can handle extensive documentation, from synthesising patient history into concise notes to summarising regulatory guidelines. This allows consultants to devote more time to complex decision-making and direct patient interaction. One such innovation is FPT’s AI Scribe, a generative AI-powered Speech-to-Text solution that transcribes spoken medical information into structured electronic health records (EHRs), fully compliant with HL7 FHI7, the global next-generation interoperability standard created by the standards development organisation Health Level 7. By automating documentation, AI Scribe frees up consultants to focus on complex decision-making and direct patient care, while ensuring compliance and data accessibility across the clinical workflow.
- Next-generation care for rare and complex therapies
AI systems can continuously monitor research, clinical trials and guidelines, alerting consultants to breakthrough treatments. These systems can highlight newly approved biologics for rare bone disorders and integrate personalised genomic data (including genes and DNA) for targeted therapies.
- Patient engagement and communication
Generative AI can produce patient-specific educational materials, simplifying treatment plans and increasing adherence. Chatbots handle routine queries and appointment reminders, allowing staff to focus on complex patient needs.
Considerations for ethical and successful AI
Employing trustworthy AI means adhering to ethical and regulatory considerations, such as the UK GDPR and Data Protection Act 2018. Transparent algorithms and interpretability features are important to maintain clinician trust and auditable decision-making paths will ensure that AI’s role remains advisory and accountable.
Human supervision and validation by consultants are essential to ensure system performance is enhanced over time. As consultants remain central to clinical judgment, AI recommendations must be validated and, when appropriate, challenged by human expertise. Continuous feedback loops, where clinicians endorse or reject AI suggestions, will ensure AI is optimised for the best patient care outcomes.
Successful adoption relies on clinician training to be able to effectively interpret AI outputs, manage device interfaces and discuss AI-guided recommendations with patients. Engaging stakeholders, forming strategic technology partnerships and testing solutions incrementally will also be essential to supporting a positive transition.
