11 real ways AI is transforming healthcare

Healthcare is on course to become more personalized and effective than ever, thanks to advances around artificial intelligence (AI). In a recent report, the World Health Organization outlined the transformative potential of AI in global health, citing the many ways it will change drug development, administration, diagnosis, treatment, and patient care. If governed and implemented effectively, WHO said, AI will not only improve access to higher quality services for all, but also address workforce shortages and reduce health system costs in the process.

In the not-too-distant future, then, we can expect to see treatment plans that are tailored precisely to each patient to determine the most successful interventions, taking into account everything from genetics and medical history to lifestyle choices. Because of AI’s ability to mine vast amounts of data, medical professionals will be given the insights they need to accurately detect, diagnose and develop bespoke treatments for critical health issues and diseases to save more lives. In clinics, they’ll be empowered with real-time support to improve decision-making. In surgery, smart robot systems (like F.MED’s microsurgery robot) will assist with intricate procedures and perform minimally invasive techniques, resulting in better outcomes and faster recovery times. Through AI-powered remote monitoring, patients will receive ongoing care and therapy from the comfort of their own homes. And by streamlining processes and easing administrative burdens, AI will free up healthcare professionals to focus on what truly matters – patient care.

In this new AI-driven world, healthcare systems will become more proactive and deliver patient-centric experiences that reach more people. Medical breakthroughs will be reached faster, and treatments and therapies will dramatically improve patient outcomes. And, through personalized insights and tools for wellness management, more people will be empowered to live healthier lives. With all this in mind, here are some of the remarkable ways AI in the healthcare industry is already making a difference on a global level:

How is AI being used in healthcare?

1. Research and development: AI is transforming product and drug discovery. In silico compound screening accelerates the development process with foundational chemistry models that can map millions of chemical compounds by structure and function – it works like generative AI content tools, but instead of words it predicts the next part in the molecular structure. Deep learning algorithms also assist in virtual screening by analyzing large datasets of chemical compounds to determine how different drugs might interact with specific proteins or molecules. From here, scientists can focus on the most promising drug candidates. Additionally, generative models, paired with detailed data analysis, can help identify existing drugs that might be suitable for use in other, new therapeutic applications.
   
2. Clinical trials: AI is proving promising when it comes to optimizing clinical trials, helping to identify the most suitable and eligible participants faster and predict trial outcomes to reduce the time and cost of clinical research. Biomarkers help to assemble diverse and representative populations while digital medical writing assistants, powered by natural language generation, streamline regulatory filings and reporting, and analyze vast datasets quickly, uncovering key insights and patterns to accelerate decision-making. By boosting efficiency across the entire clinical development process, pharmaceutical companies could achieve 50% cost reductions from streamlined clinical trial processes and auto-drafting trial documents; faster clinical trials by more than 12 months; and at least a 20% increase in net present value.
   
3. Manufacturing: Pharmaceutical companies like GlaxoSmithKline and Sanofi have embraced AI to enhance the efficiency and reliability of drug production. AI models support predictive maintenance, which allows them to fix and replace equipment before it disrupts manufacturing, and optimize stock management to avoid waste. Using AI and machine learning tools to analyze quality control issues (also known as deviations) at its production sites and automate the review process for minor deviations, Sanofi has reduced closure times by 60%, resulting in shorter cycle times and enhanced quality and reliability across the supply chain.
   
4. Safety and quality regulations: AI is enhancing safety and quality in healthcare and pharmaceuticals by automating tasks like compliance checks and reporting, and streamlining the regulatory submission process by generating all required documents, tracking changes, and verifying data. In drug manufacturing settings, AI can detect anomalies in production data to resolve potential issues quickly and reduce the risk of non-compliance. And for ongoing drug safety, AI is being used to analyze clinical data, proactively identify potential adverse effects and safety risks, and continually monitor information from healthcare providers and patients.
   
5. Commercialization: McKinsey estimates that AI could generate US$60 billion to US$110 billion a year in economic value for the pharmaceutical and medical-product industries by accelerating the process of identifying compounds for possible new drugs, accelerating their development and approval, and improving the way they are marketed. In particular, marketers can use AI’s advanced search and data analysis capabilities to extract deeper insights from customer research, information on physicians, as well as updates on policy changes, legal developments, and formulary considerations. With all this information, they can get a better understanding of the markets they’re targeting and fine tune their campaign strategies.
   
6. Consultation: Now, before seeing a medical professional, patients might instead interact with an AI virtual assistant, which gathers their medical records, assesses symptoms, and triages them based on their condition. From here, doctors benefit from more focused consultations, backed by valuable insights to help with diagnosis and treatment. They can also take advantage of natural language processing (NLP) tools, which relieve the administrative burden and save them valuable time by transcribing and summarizing all clinical engagements​.
   
7. Diagnostics: By training AI algorithms to analyze medical images and detect patterns from symptoms and other factors, it’s becoming possible to better identify things like cancerous lesions and tumors and detect diseases earlier, with greater accuracy. For example, the American Cancer Society reported that many mammograms produce false positives, leading to one in two healthy women being misinformed about having cancer. However, it discovered that AI can review and interpret mammograms 30 times faster, with 99% accuracy, significantly reducing the number of unnecessary biopsies.
   
8. Medical decision-making: AI is helping medical professionals diagnose medical conditions, build the most effective treatment plans and better predict patient outcomes by consolidating and drawing insights from all manner of data including medical records, lab results and imaging data. Machine learning models are proving particularly valuable for helping to detect serious conditions like sepsis, meningitis and heart disease, which might get overlooked in initial consultations​. AI is also helping to build a holistic 360-degree patient view, sometimes called patient 360 records, by drawing together data from electronic health records, lab results, wearable devices and more.
   
9. Treatment: In the UK alone, more than 14,000 hospital beds are occupied every day by patients who are well enough to be discharged. Busy hospitals and bed shortages have a significant impact on patient safety. AI is helping by predicting patient admission rates, identifying seasonal peaks, and optimizing everything from staffing and resource allocation to bed management. NLP tools generate discharge summaries in a fraction of the time, reducing the burden on healthcare staff and expediting the overall discharge process. AI is also helping to improve communication between different hospital departments – vital for effectively coordinating care in complex cases that involve multiple specialists.
   
10. Follow-up: AI is transforming patient follow-up care through effective remote monitoring and personalized patient experiences. AI is now capable of doing things like sending personalized follow-up messages and medication reminders to reduce readmission rates, as well as flagging abnormalities to help with timely interventions. Developments like personalized diabetes management tools help to keep track of patients and make sure they’re adhering to their treatment plans, making recommendations based on real-time patient data.
    
11. Health assessment/self-monitoring: The rise of consumer wearables and medical devices, enhanced by AI technology, is revolutionizing the management of chronic illnesses including heart disease. Through better monitoring, healthcare professionals can keep a closer eye on patient symptoms and identify potentially life-threatening episodes earlier when they’re more treatable. Smart watches now include features like heart rate monitoring and use AI algorithms to analyze heart rhythms, helping users detect irregularities such as atrial fibrillation.

Dassault Systèmes and the future of AI in healthcare

Much like in our everyday lives and across most industries, AI is set to become an integral part of the global healthcare system. More than 70% of healthcare organizations are already testing and implementing AI capabilities to improve the patient experience and streamline operations. Many expect AI, combined with other technological advances, to reshape the industry entirely – driving the shift to a new era of efficient, personalized and proactive care.

In many ways, this vision aligns with Dassault Systèmes’ own mission to drive innovation and efficiency in life sciences and healthcare – a future where patients become consumers, in control of their healthcare, and an industry where virtual twin experiences are the catalyst for sustainable innovation and efficient healthcare systems.

Through its virtual twin capabilities and powerful AI tools, Dassault Systèmes strives to empower healthcare organizations, pharmaceutical companies and consumers with new, data-driven ways of visualizing, predicting and managing responses to treatments and interventions. This approach involves the creation of dynamic, highly detailed digital replicas (virtual twins) of patients, considering their individual anatomy, genetics, and real-world medical data. In clinical trials, virtual twins will replace traditional placebo groups, using synthetic patient data to speed up research and broaden access to innovative therapies. And when it comes scaling up precision medicine production, virtual twins and AI will make it possible to manufacture new biologics efficiently and deliver them globally.

“Just imagine… if you’re able to understand, represent, test and predict what can’t be seen – from the way a drug affects a disease to the outcome of a surgical intervention,” said Claire Biot, our VP of Life Sciences and Healthcare, introducing Dassault Systèmes’ vision for the industry earlier this year. “That’s really what we are trying to achieve and we want to position the virtual twin as a way to propose a platform for medical practice excellence and value-based care.”