With Ai being such a buzzword in health tech in 2025 I hark back to my days at Healthily where I quoted Garry Kasparov from his brilliant talk at WebSummit 2018 where he enthused about AI alternative definition as: ‘Augmented Intelligence’. I still stand by this – that the most effective outcomes in product arise from combining human intuition and creativity with AI’s speed and precision. Our symptom checker at the time used AI to power it but was backed up with human validated scientific rigour.
Health tech is always evolving rapidly, and while some of the core ideas—AI-driven personalisation, remote health monitoring, and preventative care—aren’t exactly new, the way they’re being executed is shifting dramatically. The latest advancements in AI, particularly large language models (LLMs), edge computing, and real-time biometric tracking, are making once-clunky features actually usable and effective.
But with all this innovation comes hype. Not all AI-driven personalisation is as intelligent as it sounds, and some so-called “cutting-edge” health apps may be repackaging old ideas with a fresh UI. So what’s truly new and what should we look out for this year?
Apologies for the earlier inaccuracies. Here’s the revised article, written in British English, with updated examples and correct citations.
The Top 5 Health Tech App Trends to Watch in 2025
The health tech landscape is evolving rapidly, with innovations transforming patient care, personal wellness, and medical diagnostics. As we navigate 2025, several key trends have emerged, driven by advancements in artificial intelligence (AI) and a focus on personalised, efficient healthcare solutions. Here are the top five trends to watch, highlighted by cutting-edge applications that exemplify each area.
1. AI-Driven Patient Prioritisation
Trend Overview: Hospitals are increasingly leveraging AI to manage patient care more effectively, especially in systems burdened with extensive waiting lists.
Example: The NHS has implemented AI software developed by Cambridge-based C2-Ai to analyse patient data—such as blood pressure, age, and respiratory rate—to assign risk scores. This system identifies high-risk patients, allowing for earlier interventions and optimised surgery scheduling. Early results have shown a significant reduction in post-surgery complications and shorter hospital stays.
Caveat: While AI enhances efficiency, it’s crucial to ensure that algorithms are transparent and free from biases that could affect patient outcomes.
2. Personalised Fitness and Health Coaching
Trend Overview: Combining AI with human expertise, personalised health coaching apps are providing tailored fitness and nutrition plans to users.
Example: Caliber offers a comprehensive programme that integrates strength training, cardio, and nutrition guidance, all overseen by a human coach. The app uses data analytics to track progress and adjust plans, resulting in improved fitness outcomes for users.
Caveat: The effectiveness of such programmes depends on user engagement and the quality of data collected; personalised plans must be adaptable to individual needs and limitations.
3. AI-Powered Mental Health Support
Trend Overview: AI is being utilised to provide accessible mental health support, offering interventions that are available around the clock.
Example: Wysa is an AI chatbot designed to assist users in managing mental health issues such as anxiety and depression. It employs natural language processing to engage in conversations, provide cognitive-behavioural therapy techniques, and suggest coping strategies.
Caveat: While AI can offer immediate support, it should complement, not replace, professional mental health care. Users must be aware of the limitations and seek human intervention when necessary.
4. Advanced Health Monitoring and Diagnostics
Trend Overview: Innovations in health monitoring are enabling early detection of conditions, improving patient outcomes through timely interventions.
Example: Swedish startup Neko Health has developed comprehensive body scanning technology that uses advanced sensors and AI to detect early signs of diseases, including cancer. These full-body scans provide detailed health data, facilitating proactive healthcare measures.
Caveat: The accuracy of such diagnostics is paramount; false positives or negatives can lead to unnecessary anxiety or missed treatments. Regulatory oversight is essential to ensure reliability.
5. AI Integration in Telehealth Services
Trend Overview: Telehealth platforms are incorporating AI to enhance service delivery, from patient triage to personalised care recommendations.
Example: Teladoc Health has been recognised as an “under-the-radar AI play,” effectively utilising artificial intelligence to improve virtual care services. The company has invested significantly in AI to predict health issues, reduce emergency visits, and lower costs, thereby enhancing patient care.
Caveat: The integration of AI in telehealth must be handled carefully to maintain patient trust, ensuring data privacy and the accuracy of AI-driven recommendations.
It’s essential to approach these health tech innovations with a critical eye, ensuring that they are implemented ethically, transparently, and with a focus on enhancing human decision-making rather than replacing it. At Healthily we spotted errors in basic rules-based systems in some competitor apps a few years ago – those made newspaper headlines at the time. Right now we need to make sure we’re safe in using the more powerful tools at our disposal!