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Paper short abstract

AI's high clinical potential and low adoption created a technology adoption chasm. Quantum Technology's potential to remedy many of AI's problems, yet suffers from similar potential/reality paradigm. This papers investigates this problem by interviewing quantum scientists and medical practitioners.

Paper long abstract

The use of Artificial Intelligence (AI) in clinical settings holds immense promise, from exponential increases in diagnostics accuracy to unburdening of clinicians’ administrative tasks. AI revolutionizing every facet of medicine is a common talking point that fears of a “great doctor replacement theory” is taking hold. However, the replication crisis affecting scientific research is miring the translational prospects of medical AI. Very few applications are approved for clinical settings, and coupled with inherent issues plaguing AI models – bias, explainability, curve-fitting, lack of a universal set of ethical principles guiding their development, and lack of agreement on the distribution of responsibility – this poses serious challenges for clinical adoption. Finally, the immense compute requirements, reaching a trillion plus tokens to train certain models, pose serious roadblocks in AI development. All are possible problems Quantum Computing (QC) has the potential to solve, due to its ability to exponentially increase computing power, leveraging quantum mechanics and entanglement as a novel means of computing, with simultaneous, rather than consecutive operations, which cannot be achieved with classical computing. Yet the QC promise faces several challenges, from the statistical nature of its results, the noise-prone processes which have yet to be fully error-corrected, the inaccessibility of its hardware due to high costs, complexity, and expertise required to conduct Quantum Machine Learning research, creating monumental barriers towards conducting interdisciplinary research necessary to harness its potential in medical research, ultimately resulting in similar conversations about QC potential, and its ability to leap over the AI adoption chasm.