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Artificial Intelligence and healthcare

Your partner in harnessing the power of Artificial Intelligence and Machine Learning for healthcare to improve efficiency and create value for your organization.

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Supercharge your healthcare data with medical artificial intelligence

With nearly a decade of experience helping healthcare teams optimize software systems, our AI Practice experts can guide stakeholders through the process of planning, implementing, and analyzing cutting-edge technologies that enhance patient care, optimize operations, and drive groundbreaking advancements. Examples include:

  • Natural language processing-powered chatbots, virtual assistants, and AI augmented health coaches, streamline patient interactions, provide medical information, and offer appointment scheduling assistance.

  • Analyze patient data to create personalized treatment plans, taking into account genetic information, medical history, current health status, and any other relevant structured or unstructured data.

  • Leverage AI-driven algorithms to enhance diagnostic accuracy, identify patterns, and provide doctors with invaluable insights for more informed decisions.

  • Anticipate disease outbreaks, patient admission rates, patient acuity, and resource utilization through predictive models, enabling proactive planning and resource allocation.

  • Enhance patient care with wearable devices and AI analytics, allowing real-time monitoring and early intervention for high-risk patients.

  • AI optimizes the recruitment process for clinical trials by identifying suitable candidates from large patient databases, expediting research timelines.

  • Implement digital diagnostics or other AI based Software as Medical Device (SaMD) based on analysis of images or other medical device data.

Medical AI: HTD’s vision

  • Human-centered AI: AI healthcare solutions should exist to improve the lives of patients and to make clinical work easier
  • Deep domain knowledge: Successful implementation depends on a deep understanding of the clinical workflows and health IT systems
  • Careful evaluation: AI systems need to be carefully evaluated in multiple ways. Computing performance metrics on test data are necessary but not sufficient.
  • Rigorous testing: When moving models to the bedside, the underlying AI infrastructure needs to be diligently tested and instrumented. Monitoring care processes is the way to assess if these models are making a positive impact.

AI in healthcare: Examples

Learn more about the use cases, opportunities, and risks for medical AI. HTD's AI Practice Lead Erkin Otles describes the challenges facing AI/ML implementation including lack of technical and people infrastructure in legacy care delivery organizations, and the high-risk nature of AI-assisted diagnosis and treatment.

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Jul 25, 2023

11 min read


AI and healthcare: Big opportunities, bigger risks?

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