The healthtech revolution is still being spearheaded by San Francisco with more than 500 healthtech startups based in the Bay Area and over $300 billion in global AI investments anticipated by 2025. Custom large language models (LLMs) are being used by healthtech startups more and more to address the difficulties in healthcare.
The healthtech revolution is still being spearheaded by San Francisco, a centre of technological innovation. The field is ready for ground-breaking developments, with more than 500 healthtech startups based in the Bay Area and over $300 billion in global AI investments anticipated by 2025.
Custom large language models (LLMs) are being used by healthtech startups more and more to address the particular difficulties of the healthcare industry, such as patient care customization and regulatory compliance. As 2025 unfolds, startups aiming to remain competitive and transform patient outcomes must consider the unparalleled advantages offered by custom LLMs.
San Francisco is well-known for its groundbreaking innovation. The last ten years have witnessed an exponential growth in the city’s healthtech scene. With multiple healthtech startups operating in the region, global AI investments are expected to exceed $300 billion by 2025 (Statista, 2023).
Startups like Carbon Health and HealthTap have harnessed AI to offer remote patient monitoring and personalised medicine. This is something that has revolutionized the way healthcare is delivered, making it more efficient and accessible to a wider population. The integration of AI in healthtech has also paved the way for advancements in predictive analytics and precision medicine, leading to better patient outcomes.
However, as the healthtech industry matures, the demand for highly specialized solutions has surged. Generic AI models are no longer sufficient to tackle the intricate, domain-specific challenges of healthcare. Here comes custom LLMs, AI solutions that are specifically designed to meet the demands of the healthcare industry. These models set the stage for future innovation by enabling startups to precisely tackle complex issues.
Large language models, such as OpenAI’s GPT and Meta’s Llama, are AI systems designed to understand and generate human-like text. Although general-purpose LLMs have proven useful in a variety of applications, it is the custom LLMs that are tailored to a domain’s unique requirements.
Custom LLMs are built by training a foundational model on curated datasets that align with the unique requirements of a particular field. For healthtech startups, this means creating models that understand medical terminology, regulatory requirements, and patient-specific contexts, enabling them to deliver highly relevant and actionable insights.
Healthcare is one of the most stringently regulated industries. HIPAA in the United States, GDPR in Europe, and other regional mandates govern permissible uses and sharing protocols for sensitive patient data. Navigating the complex web of legal requirements while protecting data privacy and security is a daunting task for startups.
Custom large language models (LLMs) can be fine-tuned to understand the complexities of these regulatory frameworks. They are able to detect possible hazards like inappropriate data sharing procedures, parse compliance documentation, and automatically produce reports that are ready for an audit. In order to guarantee compliance with changing regulations and lessen the possibility of expensive fines and harm to one’s reputation, LLMs can also incorporate real-time updates.
Modern healthcare prioritises personalized treatment plans based on individual patient data. In order to suggest individualized treatment options, custom LLMs are able to evaluate a great deal of patient data, including genetic information, medical histories, and lifestyle factors. There are companies like Tempus Labs that use AI to analyse genomic and clinical data to guide cancer treatment decisions.
Studies have shown that personalised medicine approaches can increase treatment efficacy by up to 50% (NIH, 2023), significantly improving outcomes and patient satisfaction. By comparing patient data with pharmaceutical databases, custom LLMs also aid in the early detection of adverse drug reactions.
According to the Centre for American Progress (2022), the yearly cost of administrative inefficiencies to the U.S. healthcare system exceeds $250 billion. Healthcare providers may become overburdened with tasks like appointment scheduling and medical record management.
Custom LLMs can automate these processes, freeing up valuable time for clinicians to focus on patient care. Startups like Suki AI have developed virtual assistants that transcribe physician notes in real-time, reducing documentation time by 76%. Similarly, Olive AI automates revenue cycle management, enabling hospitals to cut down on billing errors and save millions annually.
Diagnostic errors affect an estimated 12 million adults annually in the U.S. alone (Johns Hopkins University, 2022). Specialised LLMs that have been trained on domain-specific datasets help physicians decipher test results, spot trends in patient symptoms, and make recommendations for possible diagnoses.
With a 95% accuracy rate in detecting anomalies like strokes and embolisms, Aidoc employs AI to evaluate medical imaging. Custom LLMs ensure timely interventions by significantly reducing diagnostic errors and delays through the use of AI precision to augment human expertise.
The integration of healthtech startups’ solutions with current healthcare systems is frequently fraught with difficulties. By making interoperability and data sharing easy, custom LLMs can close these gaps. Redox offers a single API platform that uses artificial intelligence (AI) to standardize data exchange between more than 85 EHR systems. Such advancements not only enhance collaboration among healthcare providers but also improve continuity of care for patients.
Healthcare solutions that are multilingual are necessary due to San Francisco’s diverse population. A language other than English is spoken at home by around 44% of the population (U.S. Census Bureau, 2022).
Custom LLMs can help with multilingual communication by offering culturally appropriate content and precise translations. To facilitate effective communication between patients and providers in more than ten languages, Babylon Health integrates AI-driven translation tools into its telehealth platform. This makes healthcare more inclusive and lessens inequalities in access to care.
Startups seeking to revolutionize healthcare innovation are embracing custom large language models (LLMs) in impressive numbers within San Francisco’s healthtech ecosystem.
Companies that have successfully tapped into the potential of custom LLMs include the following noteworthy success stories:
Leading precision medicine startup Tempus is at the forefront of using AI to enhance patient outcomes. Drug discovery is accelerated and clinical trial design is optimized by Tempus through the integration of custom LLMs trained on genomic, clinical, and molecular data.
Its in-house platform has made it possible to find individualized treatment options, which has greatly shortened the time needed for cancer diagnosis. Over $1 billion in funding has been raised by Tempus as of 2023, highlighting its critical role in healthtech innovation.
Aidoc, an AI-powered radiology startup, employs custom LLMs to streamline the detection of anomalies in medical imaging. They have trained LLMs on millions of radiology scans. By doing this Aidoc has enhanced diagnostic precision, reducing false negatives and assisting radiologists in delivering quicker, more accurate interpretations. Aidoc’s solutions are now deployed in over 1,000 hospitals worldwide, helping save critical time in emergency cases like stroke or pulmonary embolism.
Ophthalmology, urology, and neurology are among the specialty medical specialties for which Verana Health specializes in data curation and analysis.
By developing unique LLMs for these fields, Verana Health helps medical professionals provide better patient care by drawing useful conclusions from enormous datasets. The company’s VeraQTM platform is a noteworthy illustration of how structured datasets combined with specially designed AI models can enhance the creation of tangible evidence.
Custom LLMs are being used by San Francisco-based Invitae to transform genetic testing and interpretation. Through the use of genetic data to train its models, Invitae offers patients and physicians precise, useful information about inherited illnesses.
Its AI-powered reports have improved turnaround times for important diagnoses, decreased analysis costs, and increased accessibility to genetic testing.
Deepcell enables revolutionary breakthroughs in single-cell analysis by fusing AI and cell biology. Deepcell helps with research on immunology, cancer, and drug discovery by using custom LLMs trained on biological data to identify patterns at the cellular level. Its AI-powered platform has already aided academic institutions and pharmaceutical corporations in boosting personalized medicine initiatives.
Qventus, a healthcare startup that focuses on operational efficiency, optimizes hospital workflows using custom LLMs. Its AI technologies are specifically designed to predict patient flow, reduce emergency department congestion, and improve operational decision-making. Qventus has collaborated with major healthcare organizations such as Mercy and New York-Presbyterian to demonstrate the scalability and effectiveness of its LLM-powered platform.
The FDA has approved Viz.ai‘s AI solutions, demonstrating the company’s dedication to patient safety and regulatory compliance. Viz.ai is a leader in AI-powered stroke detection, using custom LLMs to analyze CT scans and identify potential blockages in blood vessels. The company’s platform, which is integrated with hospital networks, alerts doctors of critical cases in real-time, accelerating life-saving interventions.
While custom LLMs offer transformative potential, their development comes with intricate challenges that demand expertise, resources, and a nuanced understanding of both AI and healthcare domains.
Training a high-performing LLM is dependent on having access to large, high-quality, domain-specific datasets. In the healthtech space, this involves accessing Electronic Health Records (EHRs), medical imaging data, genetic information, and clinical trial datasets. However, healthcare data is frequently siloed across institutions, fragmented in inconsistent formats, and subject to stringent regulations such as HIPAA and GDPR.
By using federated learning strategies, which enable decentralised data training without sacrificing privacy, startups can overcome these obstacles. The data is also structured for the best training results through data preprocessing, such as standardising medical terms using SNOMED CT or LOINC codes.
The development and upkeep of custom LLMs requires a substantial financial commitment. Millions of dollars in compute resources are needed to train state-of-the-art models, such as GPT-4 or its equivalent, especially when using high-performance GPUs for lengthy training cycles.
Operating expenses go beyond infrastructure and include putting together interdisciplinary teams of data scientists, machine learning engineers, and medical specialists with specialised knowledge.
Startups can reduce expenses by using cloud-based platforms like Google Vertex AI or AWS SageMaker for scalable solutions, as well as by implementing transfer learning. This involves adapting pre-trained foundational models to their unique requirements rather than training from scratch.
Healthcare AI models need to be carefully examined to prevent biases from being reinforced, which could make health disparities worse. Minority groups may not receive accurate diagnoses if they are under-represented in training datasets, for example.
Adherence to frameworks such as the AI Ethics Guidelines for Trustworthy AI, bias detection algorithms, and meticulous dataset curation are necessary to mitigate this. Methods that mimic under-represented patient profiles, like synthetic data augmentation and adversarial debiasing, can aid in the development of a more equitable model.
Developing custom LLMs requires a unique confluence of skills, including proficiency in natural language processing (NLP), neural network architectures (e.g., transformers), and domain-specific knowledge of healthcare workflows.
Furthermore, understanding concepts like sequence-to-sequence learning for medical text summarisation and the use of self-supervised learning for contextual embeddings in clinical data is critical.
Many startups overcome talent shortages by partnering with research institutions or utilising third-party vendors such as Hugging Face or OpenAI’s enterprise solutions. Rapid experimentation and prototyping are also made possible by open-source libraries like PyTorch and TensorFlow.
Through proactive investments and collaborations, healthtech startups can fully realize the potential of customised LLMs to transform patient care and operational effectiveness.
For healthtech startups ready to embrace custom LLMs, here are the steps to get started:
Define Objectives
Clearly state the issues you hope to resolve with a personalised LLM. Whether the goal is to streamline processes or improve diagnostics, a clear objective will direct the development process.
Curate High-Quality Data
Make an investment in procedures for gathering and cleaning data to guarantee that your LLM is trained on precise and pertinent datasets.
Collaborate with Experts
To create and train the model, collaborate with AI experts and medical practitioners. Utilising outside skills can boost results and speed up progress.
Test and Validate
To assess the LLM’s dependability and performance, thoroughly test it in real-world situations. To improve the model, get end-user input.
Ensure Compliance
Ensure that your LLM complies with all relevant laws and regulations by collaborating closely with legal and regulatory professionals.
Large language models that are specially tailored for a given industry, like healthcare, are known as custom LLMs. GPT-4 and LLaMA are examples of general-purpose LLMs that are trained on a variety of datasets, whereas custom LLMs are customized using specialized datasets to satisfy the requirements of a particular use case.
Custom LLMs allow healthtech startups to address unique challenges in healthcare, such as:
These specialised needs cannot be satisfied by general LLMs without considerable modification, which makes custom LLMs essential for innovation and competitive advantage in the healthtech industry.
Some of the key challenges include:
Custom LLMs that have been trained on medical datasets (such as pathology reports and imaging records) are able to analyse patient symptoms, spot trends, and help interpret test results. Aidoc’s models, for instance, assist radiologists in identifying anomalies in imaging data, which lowers diagnostic errors and increases the likelihood of early disease identification.
Custom LLMs can suggest specialized treatments and interventions by examining patient data, such as genetics, medical history, and lifestyle factors. Better health outcomes, more patient happiness, and the shift to precision medicine are all facilitated by this degree of personalization.
It is possible to program custom LLMs to translate and standardize data formats, allowing for smooth integration between various electronic health record (EHR) systems. They can fill in the gaps between proprietary platforms, guaranteeing data accessibility throughout the IT ecosystem of a hospital.
Top quality ensured or we work for free
