AI in Health Care

Key Points

• This is an exciting time for using AI in the medical field

• Both the Canadian and the American Medical Associations have provided guiding principles for use of AI by physicians

• Some of the main use cases used in the medical field involve research, medical education, administration assistance for medical professionals, diagnosis, treatment, monitoring, and more. The use cases that are predicted to be especially useful in 2025 are striking

AI is becoming pervasive in medicine, and many in the health care field predict that it is going to continue to proliferate this realm well into the future.

Input from Canadian and American Medical Associations

The Canadian Medical Association (CMA) notes that the rapid evolution of AI technologies is expected to improve health care and change the way it is delivered. In fact, the CMA states that  AI is being explored, along with other tools, as a means of increasing diagnostic accuracy, improving treatment planning, and forecasting outcomes of care. There has been promise for the following:

• clinical application in image-intensive fields, including radiology, pathology, ophthalmology, dermatology, and image-guided surgery
  

• broader public health purposes, such as disease surveillance

Interestingly, Health Canada has already approved several AI applications, but it is worth noting that that the CMA advises doctors that:

“Before deciding to use an AI-based technology in your medical practice, it is important to evaluate any findings, recommendations, or diagnoses suggested by the tool. Most AI applications are designed to be clinical aids used by clinicians as appropriate to complement other relevant and reliable clinical information and tools. Medical care provided to the patient should continue to reflect your own recommendations based on objective evidence and sound medical judgment”

Moreover, the CMA stresses that physicians do the following:

• Ensure that AI is used to compliment clinical care. Medical care should reflect doctors’ own recommendations based on objective evidence and sound medical judgment
  

• Crucially review and assess whether the AI tool is suited for its intended use and the nature of your practice
  

• Consider the measures that are in place to ensure the AI tool’s continued effectiveness and reliability
  

• Be mindful of legal and medical professional obligations, including privacy, confidentiality, and how patient data is transferred, stored, and used (and whether reasonable safeguards are in place)
  

• Be aware if bias and try to mitigate it as much as possible
  

• Have regard to the best interests of the patient

The American Medical Association (AMA) similarly recognizes the immense potential of AI in health care in enhancing diagnostic accuracy, treatment outcomes, and patient care; simultaneously, it appreciates that there are ethical considerations and potential risks that demand a proactive and principled approach to the oversight and governance of health care AI.

To that end, the AMA created principles that call for comprehensive policies that mitigate risks to patients and physicians, ensuring that the benefits of AI in health care are maximized while potential harms are minimized.

These key principles include:

• Oversight: the AMA encourages a whole of government approach to implement governance policies to mitigate risks associated with health care AI, but also acknowledges that non-government entities have a role in appropriate oversight and governance of health care AI

• Transparency: The AMA emphasizes that transparency is essential for the use of AI in health care to establish trust among patients and physicians. Key characteristics and information regarding the design, development, and deployment processes should be mandated by law where possible, including potential sources of inequity in problem formulation, inputs, and implementation

• Disclosure and Documentation: The AMA calls for appropriate disclosure and documentation when AI directly impacts patient care, access to care, medical decision making, communications, or the medical record

• Generative AI: To manage risk, the AMA calls on health care organizations to develop and adopt appropriate polices that anticipate and minimize negative impacts associated with generative AI. Governance policies should be in place prior to its adoption and use

• Privacy and Security: Built upon the AMA’s Privacy Principles, the AMA prioritizes robust measures to protect patient privacy and data security. AI developers have a responsibility to design their systems from the ground up with privacy in mind. Developers and health care organizations must implement safeguards to instill confidence in patients that personal information is handled responsibly. Strengthening AI systems against cybersecurity threats is crucial to their reliability, resiliency, and safety

• Bias Mitigation: To promote equitable health care outcomes, the AMA advocates for the proactive identification and mitigation of bias in AI algorithms to promote a health care system that is fair, inclusive, and free from discrimination

• Liability: The AMA will continue to advocate to ensure that physician liability for the use of AI-enabled technologies is limited and adheres to current legal approaches to medical liability

  
  

Furthermore, the AMA principles address when payors use AI and algorithm-based decision-making to determine coverage limits, make claim determinations, and engage in benefit design. The AMA urges that payors’ use of automated decision-making systems do not reduce access to needed care, nor systematically withhold care from specific groups. It states that steps should be taken to ensure that these systems are not overriding clinical judgement and do not eliminate human review of individual circumstances. There should be stronger regulatory oversight, transparency, and audits when payors use these systems for coverage, claim determinations, and benefit design.

Another thing to consider is that the AMA has released Principles for Augmented Intelligence Development, Deployment, and Use, which provides explanatory information that elaborates on the above principles.

Examples of Use Cases

There are several examples of AI use in health care. Here are some of the main ones we came across:

• Early warning systems: This AI tool has reduced unexpected deaths in hospital by 26 percent. An AI-based early warning system flagged incoming results showing that the patient's white blood cell count was very high and caught an instance of cellulitis (a bacterial skin infection that can cause extensive tissue damage). Another example has been seen in detecting instances of breast cancer—AI is becoming a member of the medical team

• Optimizing chemotherapy treatment plans and monitoring treatment response: Oncologists rely on imprecise methods to design chemotherapy regimens, leading to suboptimal medication choices. AI models that assess clinical data, genomic biomarkers, and population outcomes help determine optimal treatment plans for patients. Also, cancer treatment plans require frequent adjustment, but quantifying how patients respond to interventions remains challenging. AI imaging algorithms track meaningful changes in tumors over the course of therapy to determine next steps

• Robotic surgery: AI is enabling surgical robots to perform complex operations with greater precision and control, resulting in reduced recovery times, fewer complications, and better patient outcomes. These AI systems are used for minimally invasive surgeries as well

• Medical research and training: AI is being used for new and repurposed drug discovery and clinical trials. Additionally, medical students are receiving some feedback from AI tutors as they learn to remove brain tumors and practice skills on AI-based virtual patients

• Improving precision for Computed Tomography (CT) and Magnetic resonance (MR) image reconstruction: radiology departments are not being replaced—they are being improved by bolstering precision and speed. That is, AI-enabled camera technology can automatically detect anatomical landmarks in a patient to enable fast, accurate and consistent patient positioning. Also, AI-enabled image reconstruction can help to reduce radiation dose and improve CT image quality, thereby supporting diagnostic confidence. This all helps radiologists read images faster and more accurately. For instance, AI assessed Alzheimer’s disease brain atrophy rates with 99% accuracy using longitudinal MRI scans

• Precision oncology: AI allows for the development of highly personalized treatment plans based on a patient’s individual health data, including their genetics, lifestyle, and treatment history

• Remote medicine: With wearable devices and mobile health applications, AI can continuously monitor patients remotely. The data collected is analyzed in real time to provide updates on the patient’s condition, making it easier for healthcare providers to intervene early if something goes wrong

• Administration, professional support, and patient engagement: AI can help professionals identify and reduce fraud, receive necessary supports, and support patients

What is in Store for 2025?

Indeed, it is an exciting time for medical professionals. AI is fundamentally reimagining our approach to human health. Here are some AI trends that are expected to dominate the medial field in 2025:

• Predictive healthcare: Machine learning algorithms now analyze complex datasets from genetic profiles, wearable devices, electronic health records, and environmental factors to create comprehensive health risk assessments. There are platforms that can predict disease onset and recommend preventative interventions and treatment plans

• Advanced precision medicine and genomic engineering: Driven by remarkable advances in genomic engineering and CRISPR technologies, this is becoming standard practice. The ability to precisely edit genetic codes has opened up revolutionary treatment possibilities for previously untreatable genetic disorders, including correcting genetic mutations, developing targeted therapies, and making customized treatment plans

• Immersive telemedicine and extended reality healthcare: Extended reality (XR) technologies, including augmented reality (AR) and virtual reality (VR), have transformed remote medical consultations and patient care. Surgeons can now perform complex procedures using haptic feedback robotic systems controlled remotely, while patients can receive comprehensive medical consultations through hyper-realistic virtual environments. This is important when dealing with patients in rural areas and underserved regions

• Internet of medical things and continuous health monitoring: This has matured into a robust, interconnected ecosystem of smart medical devices that provide continuous, non-invasive health monitoring. Wearable and implantable devices now offer real-time, comprehensive health insights that go far beyond simple fitness tracking. It is important for monitoring, detecting, and transmitting data to healthcare providers

• Sustainable and regenerative biotechnologies: some of these technologies include: biodegradable medical implants that naturally integrate with human tissue; regenerative therapies that can repair or replace damaged organs; sustainable production of medical treatments with minimal environmental impact; and bioengineered solutions for addressing climate-related health challenges