AI in Precision Medicine Market To Cross USD 24.4 Billion by 2033

Trishita Deb
Trishita Deb

Updated · Mar 25, 2024

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Introduction

The AI in Precision Medicine Market is projected to witness significant growth in the coming years, expanding from USD 2.4 Billion in 2023 to approximately USD 24.4 Billion by 2033, with a CAGR of 26.1% during the forecast period. This growth is driven by several pivotal factors, including technological advancements in AI, an increasing prevalence of cancer requiring precision medicine, and the rise of personalized treatment modalities. Machine Learning (ML), Deep Learning, and Natural Language Processing (NLP) are key technologies driving this expansion, enabling the integration and modeling of diverse patient data to provide more personalized and precise medical interventions.

Strategic collaborations and innovation among market players are pivotal in advancing AI applications in precision medicine. For instance, Google’s partnership with iCAD to integrate AI in mammography and the collaboration between Absci and AstraZeneca have contributed to expanding the market’s reach and impact, fostering technological advancements.

Sano Genetics is a company at the forefront of the precision medicine revolution, which recently secured $11.4 million in new funding to further develop its innovative software solutions. The funding will enable Sano Genetics to enhance its platform, which combines genetic testing, recruitment, and engagement to streamline precision medicine trials. With a fivefold increase in Annual Recurring Revenue (ARR) in 2023, a doubled headcount, and expansion into the pharmaceutical market, Sano Genetics is poised to accelerate its contributions to precision medicine, particularly in rare and neurodegenerative diseases.

A significant development in the AI in Precision Medicine sector is the acquisition of MIM Software by GE HealthCare, announced on January 8, 2024. MIM Software specializes in AI-based solutions for radiation oncology, molecular radiotherapy, diagnostic imaging, and urology, catering to a global clientele that includes imaging centers, hospitals, and research organizations. GE HealthCare aims to enhance its imaging analytics and digital workflow capabilities across multiple care areas through this acquisition, integrating MIM’s technological advancements into its existing offerings to advance precision care.

Key Takeaways

  • The AI in Precision Medicine market is projected to grow from USD 2.4 Billion in 2023 to approximately USD 24.4 Billion by 2033, indicating a significant CAGR of 26.1% during the forecast period.
  • Chronic obstructive pulmonary disease (COPD) accounted for 3.23 million deaths globally in 2019, making it the third leading cause of death worldwide.
  • The elderly population, particularly those over 70 years old in low and middle-income countries, represents 90% of COPD cases.
  • Deep learning holds a notable market share of 36.2% in the AI in precision medicine market, showcasing its effectiveness in diagnostic procedures.
  • Oncology leads the therapeutic area segment with a market share of 47.6% in 2023, driven by the growing prevalence of cancer cases globally.
  • Neurology anticipates significant growth with a projected CAGR of 34.8%, attributed to the increasing incidence of neurological disorders such as epilepsy and Alzheimer’s disease.
  • By 2030, it is estimated that one out of every seven individuals will be aged over 60 years, with this demographic group doubling by 2050 to approximately 2.3 billion people.
  • The number of Americans with at least one chronic condition is expected to increase by 99.5%, from 71.522 million in 2020 to 142.66 million in 2050.
  • North America dominates the market, capturing a notable market share of 32.4% in 2023, with companies like Mead Johnson and Abbott Laboratories contributing to its growth.
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AI in Precision Medicine Statistics

  • Precision medicine, assisted by AI, is transforming healthcare, leading to numerous advancements.
    • AI helps in identifying new treatment targets and understanding disease better.
    • Machine learning predicts new treatment targets and evaluates drug effectiveness using computer simulations.
  • AI and ML improve healthcare by analyzing medical images, predicting disease risk, and understanding genetic patterns.
    • They help personalize treatment plans based on patient characteristics like genetics and lifestyle.
  • Precision medicine tailors treatments to specific patient groups based on factors like genetics and lifestyle.
    • Deep phenotyping, using multiple data types, predicts treatment outcomes and risks.
  • Promising results in precision medicine have been seen in diseases like cancer, neurological disorders, and heart diseases.
  • Challenges for AI in healthcare include handling large and diverse datasets and ensuring data accuracy and value.
  • The integration of nanomaterials with AI in precision medicine enhances drug effectiveness.
  • Big data plays a crucial role in precision medicine by collecting individualized data for tailored treatments.
  • AI adoption in healthcare faces challenges like data security and bias, requiring advancements in technology and privacy measures.
  • Precision medicine aims to personalize diagnosis, treatment, and prognosis using large biological datasets.
  • AI algorithms show promise in predicting disease risks and outcomes, particularly in cancer and heart diseases.
  • However, predicting risks and prognosis for neurodevelopmental disorders (NDDs) like autism and epilepsy remains challenging.
  • AI helps analyze brain imaging and genetic markers but needs further development for better accuracy.
  • Precision medicine aims to identify different patient groups based on biological differences for personalized treatments.
  • AI algorithms like support vector and neural networks help predict disease diagnosis and staging.
  • Large biological datasets benefit AI applications in healthcare, improving diagnostic accuracy.
  • AI aids in DNA sequencing and variant classification, enhancing diagnostic accuracy.
  • Clinical genetic diagnosis for severe NDDs associated with intellectual disabilities has a success rate of about 40%.
  • AI approaches help uncover hidden patterns in NDD data, improving understanding and diagnosis.
  • Hundreds of genetic variants pose challenges, but AI helps identify patterns and improve diagnostic models.
  • Transparency about AI limitations is essential for accurate NDD diagnosis and treatment.

Use Cases

  • Cancer Care Enhancement: AI platforms like Phathom Analytics and algorithms developed by entities such as Qure AI have significantly improved oncology care. For instance, AI has been used to assess liver lesions with 95% accuracy and auto-generate radiology report impressions, leading to a 20% increase in oncologist productivity. These advancements aid in speeding up critical decision-making and enhancing cancer care while boosting clinician efficiency.
  • Heart Failure and Arrhythmia Management: AI algorithms are proving invaluable in predicting congestive heart failure readmission risks and analyzing electrocardiograms (ECGs) for arrhythmia detection. This technology has led to improvements such as 93% recall and 90% precision in heart failure readmission risk scoring and matching cardiologist accuracy in detecting arrhythmias. Such applications demonstrate AI’s capacity to support preventive interventions and ensure rapid, accurate diagnoses).
  • Advancements in Imaging and Disease Progression Analysis: In tackling diseases like multiple sclerosis and Alzheimer’s, AI has been instrumental in quantifying disease progression through imaging analysis. By generating precise measurements from MRI scans, AI assists in correlating brain lesions to physical symptoms and assessing disease trajectory over time with high accuracy, thus facilitating earlier and more targeted interventions.
  • Optimizing Hospital Operations: Beyond direct patient care, AI is enhancing hospital efficiency, particularly in nursing staff models and sepsis early warning systems. For example, AI models have been employed to provide early warnings for sepsis, allowing for rapid intervention, and optimizing nurse staffing, thus improving patient care quality and reducing costs.
  • AI in Research and Development: The Center for Artificial Intelligence Research in Therapeutics (CAIRT) at Chiba University highlights AI’s role in medical research by using data-driven approaches to stratify disease and develop novel treatment strategies. This encompasses the collection and analysis of genomic, clinical, and ‘omics’ data to predict disease and treatment outcomes, illustrating AI’s potential in advancing personalized treatment options.
  • AI-Aided Diagnosis and Treatment: AI is assisting in the early detection of diseases, enhancing treatment delivery, and providing clinical decision support. Innovative applications include AI models that can swiftly estimate feasible dose objectives in cancer radiotherapy and deep learning frameworks for planning in stereotactic body radiation therapy, showcasing AI’s ability to refine treatment planning and execution.
  • Precision Medicine: AI’s role in precision medicine extends to creating personalized treatment plans that consider a patient’s medical history, genetic makeup, and lifestyle factors. This application of AI ensures tailored patient care that significantly enhances treatment effectiveness and patient satisfaction.

Recent Developments

Strategic Partnerships and Acquisitions

  • Absci and AstraZeneca’s collaboration exemplifies the potential of AI in developing cancer treatments.
  • ConcertAI’s acquisition of CancerLinQ’s oncology data platform underscores the industry’s collaborative spirit towards innovation.
  • In a major move, AbbVie acquired Cerevel Therapeutics for $8.7 billion, and Merck ventured into a $610 million Parkinson’s project, highlighting the sector’s bullish market sentiment.

Funding and Investment Rounds

  • Generate:Biomedicines closed a series C financing of $273 million to advance its generative AI pipeline of preclinical and clinical protein candidates.
  • Iambic Therapeutics secured an oversubscribed $100 million series B financing to advance AI-discovered therapeutics.
  • Genesis Therapeutics received a $200 million investment led by Andreessen Horowitz to progress AI-based drug candidates into the clinic.

Technological Advancements

  • The application of AlphaFold in drug discovery is anticipated to be transformational, with the potential to generate time and cost savings of at least 25–50% in the drug discovery phase.
  • Canadian startup Valence Discovery develops novel algorithms for drug discovery, utilizing machine learning approaches to optimize potential drug candidates.
  • Baixing AI Lab, a US-based startup, has created AI Pharma Bx, a SaaS medical AI research platform that eliminates data silos in pharmaceutical research.

Key Players Analysis

Intel Corporation

Intel Corporation is actively involved in advancing artificial intelligence in the precision medicine sector. An important collaboration between Intel and Penn Medicine resulted in the largest global machine learning effort to date, aimed at improving the identification and prediction of tumor boundaries in glioblastoma patients. This effort involved securely aggregating knowledge from brain scans of over 6,000 patients across 71 sites worldwide, demonstrating the potential of federated learning in healthcare research. The project was conducted with utmost care to ensure patient privacy was not compromised.

NVIDIA Corporation

NVIDIA Corporation is using generative AI to advance the AI in precision medicine sector. Their NVIDIA Clara platform offers AI-powered solutions that accelerate developments in medical imaging, genomics, drug discovery, and medical devices. This initiative enables healthcare professionals and researchers to build and deploy scalable, software-defined medical devices that can process streaming data in real time and utilize AI for genomics analysis, providing unprecedented speed, accuracy, and cost-effectiveness.

Recently, NVIDIA launched a new suite of healthcare microservices, including the NVIDIA NIM™ AI models and workflows. These microservices aim to transform healthcare companies by offering capabilities such as advanced imaging, natural language and speech recognition, and digital biology generation. NVIDIA provides tools like Parabricks®, MONAI, NeMo™, Riva, and Metropolis as microservices, accelerating healthcare workflows for drug discovery, medical imaging, and genomics analysis. NVIDIA’s launch reflects their commitment to enabling healthcare companies worldwide to harness the full potential of generative AI, revolutionizing patient data analysis, disease detection, and the efficiency of digital assistants in healthcare.

NVIDIA BioNeMo empowers the computer-aided drug discovery ecosystem with its generative AI models and cloud services, providing tools for protein structure prediction, molecular optimization, and generative chemistry, among others. This is enabling researchers and pharmaceutical companies to generate novel molecules with desired properties, streamlining the drug discovery process. By integrating these capabilities with platforms like Cadence’s Orion® molecular design platform, NVIDIA is facilitating a more precise and accelerated approach to drug discovery, highlighting the transformative impact of AI in revolutionizing precision medicine.

Microsoft Corporation

Microsoft Corporation is actively involved in advancing AI in the precision medicine sector through various collaborations and technological innovations. By partnering with NVIDIA, Microsoft is taking advantage of the combined strengths of Microsoft Azure’s cloud and computing capabilities and NVIDIA’s advanced AI technologies. The primary focus of this collaboration is to accelerate healthcare and life sciences innovation, especially in clinical research, drug discovery, and patient care, with the aim of making medical care more precise, accessible, and effective.

Furthermore, Microsoft’s partnership with Epic aims to drive the impact of generative AI in healthcare. By integrating conversational, ambient, and generative AI technologies across Epic’s electronic health record ecosystem, this initiative aims to improve patient care, enhance operational efficiency, and support the financial integrity of health systems worldwide. This collaboration is designed to tackle pressing needs in the healthcare industry, such as workforce burnout and staffing shortages, by deploying AI-powered clinical insights and administrative tools within various Epic modules.

In addition, Microsoft Research is actively engaged in making healthcare more data-driven, predictive, and precise. Their work focuses on enabling precision medicine and connected care through the convergence of medicine, biology, and technology. This endeavor aims to use AI to aggregate complex inputs from multiple data sources, driving deeper discovery, faster innovation, and delivering on the goal of precision medicine, which emphasizes prevention and individualized care.

AstraZeneca

AstraZeneca is utilizing the power of Artificial Intelligence (AI) in the precision medicine sector to discover and develop life-changing medicines. By embedding data science and AI in its R&D operations, AstraZeneca’s scientists can push the boundaries of science even further. The integration of AI into R&D processes is revolutionizing the company’s approach by improving its ability to interpret complex data, streamline drug development, and customize treatments for individual patients more efficiently. AstraZeneca is focusing on optimizing data and AI foundations to fully realize the potential of AI in developing innovative treatments for cancer and other diseases. This strategy demonstrates AstraZeneca’s commitment to leveraging cutting-edge technology to drive advancements in healthcare and improve patient outcomes.

Atomwise

Atomwise Inc leverages AI to revolutionize drug discovery, focusing on developing new small molecule medicines. Their innovative approach combines convolutional neural networks with vast chemical libraries, enhancing the efficiency and speed of identifying potential drugs. This integration of machine learning into the drug discovery process exemplifies Atomwise’s commitment to addressing complex challenges in medicine with cutting-edge technology.

Insilico Medicines

Insilico Medicine is utilizing generative AI to fast-track drug discovery, significantly reducing both the cost and time traditionally required for preclinical drug discovery processes. By employing its AI platform, the company has advanced a drug candidate to Phase 2 clinical trials for treating idiopathic pulmonary fibrosis, showcasing the potential of AI to accelerate the development of therapies for complex diseases.

Conclusion

The AI in Precision Medicine Market is set to expand significantly, driven by technological advancements, a growing elderly population, and an increasing focus on personalized medicine. Despite challenges related to data security, ongoing developments and strategic collaborations among key players are expected to continue propelling the market forward.

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Trishita Deb

Trishita Deb

Trishita has more than 7 years of experience in market research and consulting industry. She has worked in various domains including healthcare, consumer goods, and materials. Her expertise lies majorly in healthcare and has worked on more than 400 healthcare reports throughout her career.