Offering Healthcare Advice Using AI created by using Rigid Algorithms Safe?

In 1984, I wrote algorithms and created a programme called “PAT”, the Paediatric Assessment Tool. This was to help junior doctors (SHOs) distinguish between minor and severe illnesses that require hospitalisation. I identified the loopholes, stopped validating this tool, and began searching for an alternative. This was long before Jerome Groopman mentioned Algorithms in his book “How Doctors Think”

Dr Jerome Groopman (2007) emphasises that medicine is not a one-size-fits-all discipline; each patient presents unique symptoms, histories, and contexts that may not fit neatly into predefined categories. Overreliance on algorithms can lead to misdiagnoses and inadequate care, as they may miss atypical presentations and rare conditions.

Several experts have addressed the limitations of relying solely on algorithms for sharing healthcare information and making medical decisions. Dr Jerome Groopman, in his book How Doctors Think, discusses how overreliance on algorithms can lead to misdiagnoses and inadequate care, emphasising the importance of clinical judgment and personalised patient care.

Additionally, Dr Ruha Benjamin has explored the ethical implications of medical algorithms, highlighting how they can perpetuate biases and inequalities in healthcare. She advocates for a more critical and equitable approach to integrating technology in medicine.

Furthermore, a study published in the Journal of Medical Ethics examines the ethical challenges of algorithmic decision-making in healthcare, emphasising the need for transparency, accountability, and the integration of human judgment to ensure patient-centred care.

These perspectives underscore the consensus among healthcare professionals and ethicists that while algorithms can aid in medical decision-making, they should not replace the nuanced understanding and empathy that human clinicians provide.

There are a few alternative methods for providing care that go beyond rigid algorithms, emphasising personalised care, a holistic understanding, and active patient involvement. Here are some key approaches:

1. Dr Maya - Symptom-Based Classification Systems

Dr Maya’s method, which categorises symptoms into colour codes (Red, Blue, Green, Yellow), helps identify the severity and urgency of illnesses. This approach empowers patients to make informed decisions while allowing healthcare providers to focus on those needing immediate attention.

2. Clinical Decision Support Tools with Flexibility

These tools use guidelines as a framework but allow physicians to incorporate their clinical judgment.

3. Shared Decision-Making Models

Actively involve patients in their care by explaining their conditions, treatment options, and potential outcomes.

4. Narrative-Based Medicine (NBM)

Focuses on understanding the patient’s story, not just their symptoms. This approach considers emotional, psychological, and social factors influencing health.

5. Technology-Assisted Self-Diagnosis

Programs like Dr Maya integrate technology to help patients self-diagnose using symptom combinations, reducing unnecessary doctor visits while ensuring severe conditions receive prompt attention.

6. Community-Centred Healthcare

• Engage local resources, like community health workers, educators, and patient advocates, to provide culturally sensitive, localised care.

• Benefit: Reduces disparities in healthcare access and outcomes.

7. Integration of Artificial Intelligence with Human Oversight

AI can assist in pattern recognition and data analysis, but must be used with clinical judgment.

8. Focus on Preventive Care and Education

Equip patients with knowledge about their health through workshops, apps, and accessible materials.

The Goal: Personalised and Ethical Care